WWW.JOURNALMODERNPM.COM QUAD-MONTHLY | JANUARY–APRIL ISSUE | 2015 VOLUME 02 NUMBER 03 | US$ 21 CANADA CHILE USA BRAZIL BELGIUM RUSSIA SPAIN ISRAEL AUSTRALIA management of public programs and projects, public and municipal services administration, e-government TEONA, K. portfolio management of projects, project management offices, organisational capability TYWONIAK, S. TOOTOONCHY, M. BREDILLET, C. schedule risk analysis, Monte-Carlo simulation, change impact analysis VANHOUCKE, M. alliance collaboration, alliancing, managing risk, uncertainty, ambiguity complex project WALKER, D. 1 1 5 9 managing project, project duration, global sensitivity analysis, schedule GÁLVEZ, E. D. ORDIERES, J. B. CAPUZ-RIZO, S. F. 2 2 2 7 end of life aircraft recycling projects, green image of manufacturers, sustainable development objectives KEIVANPOUR, S. KADI, D. MASCLE, C. 3 3 white-collar project, earned value, earned- value metrics, cost performance index, CPI, remaining work index, RWI, staffing to schedule Index, StSI NEVISON, J. 4 4 international development projects, governance, flexibility BOAKYE, L. LIU, L. 5 innovation, competitiveness, design, benchmarking, indicator BERNARDES, M. OLIVEIRA, G. VAN DER LINDEN, J. 6 6 project management education, project value, strategic project management, tactical project management COHEN, I. 7 8 9 10 10 8 9 772317 396015 6 0
JANUARY – APRIL 2015 | THE JOURNAL OF MODERN PROJECT MANAGEMENT A 3
RISK MANAGING COMPLEX PROJECTS THROUGH ALLIANCING Walker, D.
ANALYSIS OF PROJECT DURATION: UNCERTAINTY USING GLOBAL SENSITIVITY ANALYSIS Gálvez, E. D. Ordieres, J. B. Capuz-Rizo, S. F.
THE CRITICAL SUCCESS FACTORS FOR END OF LIFE AIRCRAFT TREATMENT PROJECTS Keivanpour, S. Kadi, D. Mascle, C.
WORKING THE “EDUCATED” PLAN: HOW EFFECTIVE IS CORRECTIVE STAFFING IN A TYPICAL WHITE-COLLAR PROJECT Nevison, J.
GOVERNANCE OF TOMORROW’S INTERNATIONAL DEVELOPMENT PROJECTS (IDPS): FLEXIBLE OR RIGID? Boakye, L. Liu, L.
ICD PROJECT: IN PURSUIT OF GUIDELINES TO INCREASE COMPETITIVENESS IN THE BRAZILIAN INDUSTRY THROUGH INNOVATIVE PRODUCT DESIGN MANAGEMENT Bernardes, M. Oliveira, G. van der Linder, J.
INTEGRATING TRADITIONAL AND INNOVATIVE VALUE-FOCUSED MODELS IN PROJECT MANAGEMENT TEACHING Cohen, I.
PROJECT MANAGEMENT IN RUSSIAN PUBLIC ADMINISTRATION: THE CURRENT STATE Teona, K.
GRASPING THE DYNAMICS OF CO-EVOLUTION BETWEEN PMO AND PFM: A BOX-CHANGING MULTILEVEL EXPLORATORY RESEARCH GROUNDED IN A ROUTINE PERSPECTIVE Tywoniak, S. Tootoonchy, M. Bredillet, C.
ON THE USE OF SCHEDULE RISK ANALYSIS FOR PROJECT MANAGEMENT Vanhoucke, M.
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CONTENTS ARTICLES Order of appearance
YEAR 02 VOLUME 02 NUMBER 03
4 B THE JOURNAL OF MODERN PROJECT MANAGEMENT | JANUARY – APRIL 2015
JOURNAL BOARD /// YEAR 02 VOLUME 02 NUMBER 03 FROM THE EDITOR
In this issueIn modern management projects results rely heavily on estab-lishing a consistent flow of activities in the project and through-out its relationship chain, this is an alternative answer to the competing demands and constraints that challenge it, particu-larly: business constraints, implementing agility and the com-plexity of dealing with the flow of information from stakeholders and technical source.
The pace of implementation is critical for successful modern project management – hence creating a sequence of processes to enable a lean value stream. Without such features there will be no real agility in dealing with the dynamics and contexts surrounding the project. It’s not surprising that the underlying theory to project management is in crisis (see reference notes: Koskela, Howell and Ballard), stimulating new ways to combat this inefficiency in the traditional practices of organizations, which resulted in the Agile Project Management alternative. The fact is that setting up a flow unified with the appropriate pace considering the strategic values, which is context oriented and responds to the competing restrictions, is indispensable for pro-jects to currently deal with the complexity of a situation which is increasingly present in organizations.
However, many gaps remain to be understood, studied and resolved, perhaps an alternative is the inclusion or integration of new models, practices, techniques and diagrams, but the fact is that new project management support mechanisms need to be established or integrated in order to meet the competing demands of the real world in organizations, as well as provide an easier practical application because otherwise they will fall into disuse or be applied only by a few and solely in the restricted environment of some companies. There is no doubt that for the comprehensive practical implementation of a theory it must be made simple to use.
It goes without saying that it is not the purpose of this editorial to propose a solution to this problem, but rather stimulate de-liberations that contribute to shorter paths in the race for better project management in the modern context. In this sense the question is how to establish adequate flows and pace in project management processes and activities that are sensibly dealing with the many restrictions present in the project (see reference notes: Tyson R. Browning), however not disregarding the context, business value and multiple constraints.
It turns out that several important methods are currently avail-able to support this concern, but there is still a lack of order in how they can be integrated to provide better results in projects. Therefore we must be well familiarized with this so that the management of modern projects can deal with: processes flow; pace in activities in order to eliminate execution bottlenecks; as well as align restrictions and competing demands to the context. Project management agility requires essential premises, at which point we have an insight into the use of methods like DSM - Design Structure Matrix, DMM - Domains Mapping
Matrix or MDM - Multidomain Matrix as an integration link between the subsystems product/organization/process and the situations of practical influence in the projects run as strategy/context/pace, to be applied throughout the management cycle and the relationship chain of the project/program. The goal is to bring into operation the influence of the external environment and adapt the response time to characterize the needed agility to meet a common scenario in projects with constant changes and complexities of information. On the one hand mapping the dependencies between several competing constraints in terms of competing demands and on the other hand the mapping of variables from context, business and pace of implementa-tion. Transferring to the execution flow an alignment with the external environment and also the organizations’ perception of practical issues in the project processes. This set of mappings aims to provide the organization with an adequate implementa-tion pace that considers the perception of context, business and strategic intention at every moment of the project lifecycle, in other words agility. Let us theoretically consider that when im-plementing matrices as an input condition of an organizational process it would make the execution flow “context-sensitive” and especially at a suitable pace regarding the external environment. Bringing the context of the situation and appropriate pace rate into the project execution could establish a balance that would cancel bottlenecks, and also prioritize the results of the activities according to the project’s several competing demands. Conse-quently this project visibility could support the decision making approach which would consider two different point of views - practical and theoretical, with agility.
Finally, the possibility of mapping dependencies that carry, for example, the strategic intent, the project context and the influ-ence of stakeholders to the value stream of the company will make a difference in the project results. This considers the Five Domains Project - Goal System, Organizational System, Process System, Tool System and Product System, possibly through MDM/DMM applied in project management interfaces, as well as mappings on the variations of the multiple constraints and competing demands via DSM applied as an input condition of the processes and activities. This is justified by providing a run-pace in a flow without bottlenecks, and practical visibility into the project which would provide agility to decisions and actions in dynamic environments, currently quite common in projects.
The purpose of this discussion is basically to encourage research possibilities in this direction. Theoretically, the integration of methods and processes as suggested could act on real problems of modern project management, which are considered by many a fundamentally theoretical problem, however studies need to val-idate these situations and assess the feasibility and benefits these considerations could in fact bring into the project management area.
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ACADEMIC EDITOR Steven D. EppingerMIT Sloan School of Management
EDITORIAL ADVISORY BOARDChristophe N. BredilletQueensland University of Technology
Darren DalcherHertfordshire Business School (UK)
Edward J. HoffmanAsk Journal (US)
Joana GeraldiUniversity College London
Kalle KähkönenTampere University of Technology (Finland)
Marly Monteiro de CarvalhoSão Paulo University
P. John Clarkson FREngUniversity of Cambridge
Pierre BonnalCERN Switzerland
Sam SavageStanford University
Young Hoon KwakThe George Washington University
MANAGING EDITORS BOARDAbdelaziz BourasQatar University
Bernard YannouEcole Centrale Paris
Darli Rodrigues VieiraUQTR Canada
EDITORIAL REVIEW BOARDEugenio PellicerPolytechnic University of Valencia
Reference Notes: Ballard, Glenn (1994). “The Last Planner.” Northern California Construction Institute Spring Conference, Monterey, CA, April 1994.Browning, T. R; Yassine A. A. (2015). Managing the Portfolio of Product Development Projects under Resource Constraints; Decision Sciences Journal, 2015 (forthcoming).Browning, T. R (2014). A Quantitative Framework for Managing Project Value, Risk, and Opportunity; IEEE Transactions on Engineering Management Journal; Vol 61 (4):. Pgs. 583-598; 2014.Browning, R T. (2002). Process Integration Using the Design Structure Matrix; Systems Engineering Journal, Vol. 5 (3), pp. 180-193.Koskela, L. J., and Howell, G.A .. (2002). “The underlying theory of project management is obsolete.” In Proceedings of the PMI Research Conference, pp. 293-302.PMI 2002.
Zózimo – Editor in Chief
C A L L F O R P A P E R S
INDUSTRIAL APPLICATIONS (SYSTEMS ENGINEERING AND PROJECT MANAGEMENT)
• Aerospace
• Architectural engineering and construction
• Automotive
• Big data and analytics
• Energy
• Healthcare
• Information technology / software
• Infrastructure
• Manufactured and consumer goods
• Plant Engineering
• Others
RESEARCH TRENDS
• DSM approaches and methodologies
• Domain Mapping Matrix (DMM) approaches and methodologies
• Multidomain Matrix (MDM) approaches and methodologies
• Systems Engineering, System Dynamics, General System Theory, and others
• Developments and innovations in building, visualizing, analyzing, and understanding DSMs, DMMs, and MDMs
• Product architectures
• Organization architectures
• Process architectures
• Project management
DSM SOFTWARE TOOLS
• Commercially available tools
• Prototypes and research projects
THE 17TH INTERNATIONAL DSM CONFERENCENovember 4-6, 2015Fort Worth, Texas, USAhttp://www.dsm-conference.org/
Design Structure Matrix (DSM) techniques support the management
of complexity by focusing attention on the elements of a complex
system and how they relate to each other. DSM based techniques have
proven to be very valuable in understanding, designing, and optimizing
complex system architectures such as those of products, organizations,
and processes. The International DSM Conference provides a platform
for researchers, practitioners, and developers of DSM related tools to
exchange experiences, discuss trends, and showcase results and tools.
It also acts as a forum for developing new ideas regarding complexity
management in all kinds of industries and from many different
perspectives.
CALL FOR PAPERS:Short papers can be submitted for review until April 17, 2015, at: http://www.dsm-con-ference.org/submis-sions2015.html
Technische Universität München
The conference is run by the international DSM Community in cooperation with the Neeley School of Business (TCU) and the Institute of Product Development
(TUM - Technische Universität München) and is endorsed by the Design Society.Media partner
THE 2015 DSM CONFERENCE WILL INCLUDE THE FOLLOWING 3 MAIN TOPICAL AREAS:
KEYWORDS f alliance collaboration f alliancing f managing Risk f uncertainty f ambiguity complex project
LITERATURE REVIEW
r A B S T R A C T
Complex projects are characterised by not only known risks that present challenges in integrating technical and
human related interface issues but also by uncertainty about unknown unknowns and ambiguity about that
which is assumed to be known but is indeed a potential source of confusion. How best to establish a project
delivery approach for these types of projects? We know from government reports, audit office reports, aca-
demic research and often personal experience that typically such complex projects are delivered late and well
over budget. Surely, there must be a better way to deliver complex projects. One project delivery approach that
has generally offered a great deal of promise to managing risk, uncertainty and ambiguity is through the use
of alliancing. This paper draws upon evidence from government reports, academic studies and a wide body of
risk management and project delivery theory to illuminate this issue and to suggest a way forward. Several key
points are drawn in this paper: 1. Alliancing is not a panacea for managing any complex projects, there are some
important pre-conditions that need to be met and these are discussed further in the paper; 2. Where alliancing
has been used in both Australia and New Zealand it has been successful in delivering in terms of time/cost/
quality as well as in delivering many intangible benefits, these delivery benefits are also briefly discussed; 3.
Alliancing requires additional skill sets, knowledge, personal attributes and experience of participants and this
is perhaps the most important issue facing the future of alliancing and similar project delivery firms that are
evolving from alliancing; and 4. Australia and New Zealand lead the world in this form of project delivery. A short
section concludes the paper.
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INTRODUCTION
Project delivery success or failure is usually expressed in terms of the ‘iron triangle’ factors of being on time, to budget and fulfilling fitness for purpose criteria. This begs the following questions:1. Was the time schedule realistic, reasonable and balanced
between being challenging yet capable of being achieved?
2. Was the budget realistic, sustainable for all parties concerned, deliverable at that price and competitive?
3. Was the purpose adequately defined, communicated and thought through in terms of what was specified? As the Rolling Stones song title states ‘you can’t always get what you want but you can get what you need’ or another way of thinking about this is did you get what you asked for but not what you wanted or needed?
Such questioning is fundamental when deciding a project procurement approach that has a focus on value not cost and sustainability not short term advantage. Traditional project procurement approaches that define, design bid and tender to deliver assume that the client (the project owner’s representative) can effectively specify what it wants/needs. It assumes that the client’s designers can best shape the functional brief into an optimal design. It also assumes that the tender price plus whatever contingency that is set aside for modifications and variations during delivery are both adequate and realistic.
The alliancing form of project delivery has been in exist-ence for decades with early reports of its use in the devel-opment of oil and gas industry facilities and evolving from forms of partnering in the USA and UK to its adoption and extension in Australia (Lahdenperä, 2012).
The Department of Finance and Treasury Victoria describes project alliancing as, “… a method of procuring … (where) All parties are required to work together in good faith, acting with integrity and making best-for-project decisions. Working as an integrated, collaborative team, they make unanimous decisions on all key project delivery issues. Alliance agreements are premised on joint management of risk for project delivery. All parties jointly manage that risk within the terms of an ‘alliance agreement’, and share the outcomes of the project” (2010, p9).
Most project alliances have been centred in Australia and New Zealand since 2000 but the literature indicates that this form of procurement has been used for several alliances in Finland (Lahdenperä, 2012) and The Netherlands (Laan, Voordijk and Dewulf, 2011) with NetworkRail in the UK also using project alliances. The USA health services provid-er Sutter Health also uses a similar arrangement called Integrated Project Development (IDP) (Cohen, 2010) and discussions with those experienced with its use in the USA reveal that the inspiration for IPD came from the Australian Alliancing model. Procurement arrangements developed by British Airports Authority for Terminal Five, known as the T5 Agreement, also features many alliancing-like character-istics but with greater supply chain management integration (Brady, Davies, Gann and Rush, 2007; Doherty, 2008). Clear-ly forms of alliancing in the construction industry deliver an intense form of one-team collaboration to deliver highly complex and risky projects.
Not all clients rank cost and time delivery as being the key indicators of project delivery success. Public sector clients, particularly for infrastructure projects, have oth-
RISK MANAGING
COMPLEX PROJECTS
r Derek H.T. WalkerProfessor of Project Management RMIT University, Melbourne
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LITERATURE REVIEW /// RISK MANAGING COMPLEX PROJECTS THROUGH ALLIANCING
er key result areas that pertain to the public good. These projects have stakeholder engagement, physical environment and other social benefit value perfor-mance requirements. However, these social value outcomes can be specified and measures identified for their performance. This evidenced by results from a study of 58 alliances in Australia and two from New Zealand (Walker, Harley and Mills, 2013) in which the top three key results areas (KRAs) were report-ed to be environment, community and stakeholder relations across those 60 alliances. Alliancing also appears to be effectively used under conditions of uncertainty, ambiguity and high risk on complex pro-ject. The author has been involved in several studies on alliancing in Australia and has interviewed 60+ senior team members involved in alliancing at the alliance manager or alliance leadership team level and participated in research on over 100 alliances. Most research interviewees cite the main reason for adopting an alliance is that it allows clients to closely collaborate with the design and delivery teams on complex projects where flexibility and resilience is needed to respond to unknown or unknowable conditions. Inherent uncertainty and ambiguity de-manded that the project owner’s representative take an intense hands-on role with the design and delivery teams.
While assumptions may be reasonable and valid for projects where much is either known or knowable with readily available specialist advice it is not true for projects where much is unknown, unknowable, uncertain and ambiguous. These conditions are common for complex projects. Bent Flyvbjerg and his colleagues (2002; 2003) use the term ‘strategic lying’ about the way that large scale infrastructure business cases purposely underestimate costs and overstate revenue projections. They also assert that most infrastructure projects in their large data base are alarmingly over budget; around 28% on average. Ed Merrow (2012) takes a focus on time performance and concludes from his huge data base of oil, gas and resource industry projects that a major problem that is encountered is that insufficient effort is placed at the front end of projects and that leadership of the project delivery is often fragmented and ineffective to understand risks faced and how to best deal with them.
However, the story is not one of total woe and anguish. Success in project delivery by a public private partnership (PPPs) approach has been argued to be far more successful that adopting traditional approaches. Raisbeck and colleagues (2010) com-
pared 33 traditional projects with 21 PPPs and found that cost efficiency of PPPs ‘ranged from 30.8% when measured from project inception, to 11.4% when measured from contractual commitment to the final outcome’ and that ‘Between the signing of the final contract and project completion, PPPs were found to be completed 3.4% ahead of time on average, while traditional projects were completed 23.5% behind time’.
What is it about PPPs that seem to deliver better time and cost outcomes over traditionally procured projects? The literature points to two important factors, effective input into project definition and integration of the project design and delivery teams with the project owner (in terms of having to operate the facility over the long term), and more effective risk management. However, PPPs still need an effective brief and they still suffer from several disadvantages. PPPs engenders essentially a ‘hands-off’ relationship between the client and PPP special purpose vehicle (SPV) where the client hands over all risk to the SVP to manage. It is a service agreement for a project outcome such as patients treated, prisoners incarcer-ated away from the general public, road traffic users facilitated to travel from points A to B etc. But what if the client wants to retain ownership of the facility?
Part of the success of the PPP delivery model is attributable to the integration of the design, con-struction and operational management team with a single team goal to deliver a winning bid proposal and technically and commercially successful project outcome. This requires intense team interaction and collaboration and highly skilled risk, uncertainty and ambiguity management. If the SPV delivers what is technically asked for as stated in the client’s speci-fications that were tendered upon then the client is happy as long as the commercial and risk, uncertain-ty and ambiguity management of the SPV allows its sustainable continuity to deliver the service.
If the client wishes to retain the infrastructure asset (rather than receive a project outcome service) and the project is complex then the key aspects of the project delivery mechanism that needs to be retained is sound risk, uncertainty and ambiguity management, a competitive delivery proposition and excellent collaboration between design, delivery and operational management teams. This is essential-ly what an alliance delivers. Project alliances have been shown to deliver project outcome at or beyond expectation levels. A study by Wood and Duffield (2009, Appendix 1 page 1) reported that on a study of 71 alliances 85% of alliances had an actual outturn
cost (AOT) less that the target outturn cost (TOC) and that 94% of projects were completed ahead of schedule. In another Australian study involving 60 alliances Walker, Harley and Mills (2013) reported similar results with 51 out of 60 alliances being within budget and 46 out of 49 within budget time and a substantial number of projects being completed well within time and cost budgets. This suggests that something radically different and better is happening when compared to the data base sets of Bent Flyvbjerg and Ed Merrow which is based on data from more traditional project delivery methods. Walker and Lloyd-Walker present a tool that illustrates how collaboration can be better understood (2015, Appendix 2) and this is based on identification and measurement of characteristics of a 16 sub- element taxonomy that form three main ele-ments. These elements include provi-sion of platform integration facilities to facilitate collaboration, behaviour factors that drive normative practices and processes, routines and means that reinforce behaviours supported by the platform facilities.
Regardless of the form of project delivery to be adopted to procure the project, the client needs to have suffi-cient technical and business expertise, foresight, market and internal custom-er knowledge and general all round so-phistication to be able to provide clear and understandable briefing instruc-tions and knowledge of potential solu-tions to be able to know what to ask for, understand what is proposed and judge which of the proposed solution options should be chosen.
Thus far conclusions and the chain of reasoning can be summarised as follows:1. Clients need to be sophisticated
to demand, specify and judge what constitutes value from their perspective in order to ask the right questions that prompt the right solutions (technical, commercial and project delivery method);
FIGURE 1. Adaptation of the Cynefin Framework to explain effective collaboration
2. Clients need to understand that complex projects, particularly infrastructure projects engaged in a brownfield site context. They need to understand that they are central players in making the ultimate risk, uncertainty and ambiguity decision. Two basic bifurcations unfold:
ff Whether to take a hands-on or hands-off approach to the project delivery and
ff Whether the project outcome is a product (the infrastructure asset) or a service that is typically delivered in PPPs.
ff Assuming that we address the product outcome and not a service option then within the complex project context the evidence presented suggests that high levels of competency in risk, uncertainty and ambiguity management and high levels of collaboration to be able to identify and manage risk, uncertainty and ambiguity is required. Additionally there is a need for the client, design and project delivery teams to have the requisite levels of knowledge, skills, attributes and experience to handle the challenge of such complex projects.
3. The alliance form of project delivery provides significant improvements in project success likelihood and seems to manage this through high levels of collaboration, excellent levels of risk, uncertainty and ambiguity management. However, this requires high levels of sophistication in the client, design and delivery team to be able to collaborate as well as requiring a governance structure that links the provision of platform integration facilities to support collaboration, behaviour factors that drive normative practices and a set of processes, routines and means that reinforce behaviours that are supported by the platform facilities.
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LITERATURE REVIEW /// RISK MANAGING COMPLEX PROJECTS THROUGH ALLIANCING
Given point four above indicates a better way forward to successfully manage complex projects, how does this happen? What are the pre-requi-sites? Why do alliances seem to succeed where traditional approaches seem to generally fail?
1. Managing Risk,Uncertainty and Ambiguity through Collaboration
David Snowden’s Cynefin Framework (Kurtz and Snowden, 2003; Snowden and Boone, 2007) provides us with a clue about how to successfully manage risk, uncertainty and ambiguity.
The URL http://www.youtube.com/watch?v=-N7oz366X0-8 provides a brief but comprehensive summary of the framework. Figure 1 adapts that framework for the specific purpose of exploring
risk, uncertainty, ambiguity management in a collaborative context. Figure 1 illustrates a cloud at the centre of the diagram that indicates where most project team members find themselves when confronting a new situation that demands a decision and action. It’s a state of disorder where they don’t quite understand the situation and con-text that they are facing. They don’t know which way to jump. Quadrant 1 is a place of safety for some because they may see the situation as being standard and well known where standard solu-tions and ‘best practice’ apply. However there are acute dangers in assuming that standard solutions can be applied in a one-size-fits-all manner. Each project has its own context, history and set of unique systemic drivers that interact in a com-plex and unpredictable way (Duffield and Whitty, 2015). Quadrant 2 is a place that is complicated but it may be manageable because it is just com-plicated and once what is unknown is identified then experts who understand the context and interactions between the project’s systemic parts
Platform Foundational Facilities supply the basics for any form of collaboration. Sub-elements include:
ff Motivation and context to collaborate;
ff A joint governance structure;
ff Integrated risk mitigation strategy;
ff A joint communication strategy;
ff Substantial co-location.
Behavioural factors drive normative practice. Sub-elements for this element include:
ff The degree of an authentic leadership style;
ff A balance between trust and control;
ff A commitment to be innovative;
ff A common best for project mind set;
ff A no-blame culture.
Processes, routines and means reinforce behaviours and are supported by the platform facilities. Sub-elements include:
ff Consensus decision making between teams;
ff A focus on learning and continuous improvement;
ff Incentive arrangements;
ff Pragmatic learning in action;
ff Transparency and open-book processes; and
ff Mutual dependence and accountability.
FIGURE 2. The Relationship Based Procurement Taxonomy
can be commissioned to solve the problem. These contexts are basically quite ordered so tradition-al approaches (generally standard PM practices) suffice.
Quadrant 3 is interesting and is the world context for most complex projects. It is a some-what unordered context where ‘best practice’ does not exist but a set of better practices do. The key to this realm is unlocking people’s ability to rapidly collaborate and talk through issues, prob-lems consequences and potential solutions. This requires intense collaboration and an ability to take the perspective of others in the project team (Parker, Atkins and Axtell, 2008). The context is highly dynamic so a lot of experimental probing and ‘testing the waters’ is needed. Response is governed by perceived consequences and projec-tions so collaboration needs to be free and open and brave. Mistakes need to be expected and reacted to with fixes, quickly and without attribu-tion of blame. Quadrant 4 is even more unordered and perhaps patterns and cause and effect loops
are impossible to perceive so the response needed is to boldly act, rapidly sense the consequenc-es and respond. This needs special skills, deep perspective taking ability and an environment in which it is safe to offer advice and opinions, where power and communication asymmetries are flat-tened and a set of platform facilities that supports collaboration and complex decision making.
When projects start to unravel it is often be-cause the team in their disordered state in a com-plex project decides to position themselves in the ordered space of Quadrant 1 or Quadrant 2 when they should be taking actions shown for Quadrant 3 or Quadrant 4. The mindset for Quadrant 1 is that ‘best practice’ must be followed and this is disastrous in that situation. Space is limited in this paper but experienced practitioner readers will get the picture.
Risk is generally about what can be measured and managed. Uncertainty relates to that which is partially known or is fuzzy to comprehend. Ambi-guity is dangerous because it lulls us into think-
Aspect Specific means Comments
Identifying problems, issues or potential ambiguity.
Collaboration through common commu-nication tools and platforms.
Joint governance structures of the ALT and AMT and substantial co-location as well as shared and joint communication channels set all teams up to freely communicate.
Motivation to identify problems, issues or potential ambiguity.
No-blame culture, incentives are based on project not team performance, mutual dependence.
The consensus decision making requirement means that AMT and ALT decisions commit all parties in one direction. Dealing with early warning signals therefore makes sense.
Commitment to action on issues. Authentic leadership, no-blame culture focus on learning and continuous im-provement.
Teams expect to be Quadrant 3 of Figure 1 due to the project complexity levels. They inherently know that response to issues requires probing and action and that monitoring and review are natural parts of learning by doing. No-blame facilitates transparency.
Dealing with the ‘disorder’ cloud in Figure 1.
Authentic leadership, transparency and pragmatic learning-in-action.
Leadership is more authentic; people do what they say they will do. Open debate is encour-aged. No-blame encourages pragmatism and innovation to experiment and try new ap-proaches.
Rewarding teamwork Incentive arrangements, governance and learning.
The joint reward based on project outcomes is supported by confidence that teams support each other. People love to learn.
Quality of understanding Collaboration More realistic, reasonable and sustainable esti-mates are developed
TABLE 1. Exploring Aspects of Alliances that Enhance Dealing with Complexity Issues
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FIGURE 3. Illustration of the Estimate of Cost/Time based on Traditional and Alliancing Delivery
Table 1 summarises links between collaboration and risk, uncertainty and ambiguity management within the context of an alliance.
Table 1 provides a brief taste of how alliances work in practice. The better ones tend to have greater intensity of shared norms, objectives and ways of working together as a single ‘family’. As we find in all functioning democrat-ic families and societies, dissent and challenge are a part of the daily work of making sense of shifting events. Fundamental rules and norms govern the general path but the direction may weave and wander to find the best route through to the goal. Alliances tend to be a combination of apparent chaos and order where diversity of opinion and perspective is welcomed to
enable a greater pool of ideas that are available to solve any issue.
The main advantage of an alliance is that through processes briefly dis-cussed above, the ‘normal’ contingency allocation for risk and uncertainty is substantially reduced as alliance members share knowledge and jointly better understand the project’s tech-nical and other needs. When skilled client, design and delivery team entities collaborate as a single integrated team they provide a knowledge space where understanding the complex interplay of systems and events that impact and drive the project’s trajectory, then there is a greater understanding of the inter connectedness of the project’s con-stituent parts. This impact squeezes down the contingency budget because
it reveals what in a traditional approach would remain unknown and unknow-able and so the previously unknown becomes better known and accounted for and the remaining contingency re-quirement is reduced. The contingency is partially offset by additional costs for the team to gain a better understand-ing but in general the alliance substan-tially reduces the ‘normal’ contingency figure. Teams are then better prepared because of gaining this deep project knowledge to manage the project’s de-sign and delivery. Moreover, one of the senior alliance subject matter experts interviewed explained that the spread of estimate of cost and time is reduced forcing the profile of distribution from a flatter shaped distribution curve to a more peaked one.
TABLE 2. Limitatiions and Constraints to using Alliancing
Limitation and constraint Comment
Setting up an alliance This can be highly intensive in energy for the client (project owner representative) as well as for alliance consortium participants. The intensity of effort in cost competitive alliance tendering can be similar to that of a PPP or complex D&C project. Costs awarded to unsuccessful consortia in developing the TOC is far less than costs expended.
Opportunity cost The alliance tender stage requires a sustained and significal call upon high level participant or-ganisation resources and executive talent in developing the proposal and tendered TOC.
Complexity of project An alliance is best suited to projects in which there will be high levels of uncertainty and poten-tial ambiguity because of the intense whole-of-team involvement in understanding the project. For more straight forward projects the cost of the intense inter-team interaction is a distraction and does not deliver sufficient benefit to justify an alliance.
State of the market In overheated markets alliancing may be crowded out by other more lucrative opportinities for non-owner participants and this paradoxically makes an alliance more attractive to clients to help them retain key staff and competencies. During ‘bad’ market times clients that choose an alliance are subject to criticism that they may have been better off to go with a more traditional approach to take advantage of their market power.
Skills, knowledge attributes and experience of participants.
Both for the client and non-owner participants the demands for a special set of competencies are significant for alliances. The need for intensive collaboration places large leadership quality and ‘people’ skills demands upon all parties. These are in short suppply and also represent an oppor-tunity cost that needs close scrutiny.
Ownership of the project asset. Unlike a PPP an alliance results in the client owning the asset. This may be desirable and is a key rational for choosing an alliance over a PPP delivery. In terms of risk, the client needs to ensure that the project owner team has adequate representation of operational users and facility man-agers in their team so that operational matters are adequately considered.
ing we know when in fact we are communicating at cross purposes. Open collaboration exposes assumptions and perceptions to allow resolving ambiguity before it may cause a lot of strife and it helps to reduce fuzziness of uncertainty. It also allows risk to be better understood and thus managed. The key aspect of collaboration is that it allows knowledge transfer, perceptions to be better understood and complexity to be unpacked so that a lot of complexity can be reduced to mere complicatedness. This in turn improves under-standing of the situation so that budgets and time plan are more likely to be realistic and sustaina-ble.
Collaboration requires three main elements as noted above and 16 sub-elements in total as illustrated in Figure 2. The intensity of presence of each of the sub-elements can be assessed as being between low and high. The RBP Taxonomy provides guidance to measure these.
Figure 2 indicates that collaboration requires a solid platform of facilities to enable collabora-tion to be possible. This supports the necessary behavioural factors that are necessary for effective collaboration. However, while platform facilities and behavioural factors are necessary they are not sufficient for effective collaboration to take place. The identification of the processes, means and routines draws our attention to the ‘teeth’ required to reinforce collaborative behaviours. The alliance agreement has specific clauses and requirements such as a governance system with consensus between alliance operational members and alliance leadership team members together with a no-litigation clause that reinforces the logic that if the ‘team’ makes a consensual decision then individuals within the team can hardly complain later that they were railroaded into the decision. Consensus brings with it responsibili-ties. Similarly the alliance agreement has a pain
and gain sharing mechanism to reinforce per-formance because it is based on project and not individual team results. In alliancing the means and routines are designed to underpin and shape behaviour in a way not evident in other forms of project delivery, even for PPPs.
A significant emphasis on lowering asym-metries of power, information and formal status is evident in alliances that function well. The Walker et al. (2013) study clearly shows that well integrated platform facilities enhance opportuni-ties for open communication and that collabora-tive behaviour is closely linked to requirements in a project alliance agreement that specifies not only behaviours but includes governance means such as an alliance leadership team (ALT) struc-ture and an alliance management team (AMT) structure that formalises norms and practices. Incentive arrangements as well as transparency are configured into the alliance agreement with clear key results areas and key performance indi-cators. The literature shows that alliances can be contrasted with partnering arrangements in the level of formalisation and linkage of the three ele-ments illustrated in Figure 2. Partnering provides a charter and other related aspirational norms but these are neither reinforced by common platform facilities nor a formal contractual agreement and so partnering may be a ‘feel-good’ compact but it has no ‘teeth’. An alliance has teeth and specific standards and expectations. These encourage and demand collaboration so that perceptions are shared and greater levels of intensity of knowl-edge is focussed on not only problems to be solved but actions to be taken and monitored for dealing with risk, uncertainty and ambiguity. This results in estimates of time, cost effort and actions to be more reasonable, balanced, valid and sustainable.
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LITERATURE REVIEW /// RISK MANAGING COMPLEX PROJECTS THROUGH ALLIANCING
Brady, T., Davies, A., Gann, D. and Rush, H. (2007). “Learning to manage mega projects: the case of BAA and Heathrow Terminal 5.” Project Manage-ment Perspectives. XXIX: 33-39.
Cohen, J. (2010) Integrated Project Delivery: Case Studies, Sacramento, CA, American Institute of Architects (AIA) - AIA California Council.
Department of Finance and Treasury Victoria (2010). The Practitioners’ Guide to Alliance Con-tracting, Melbourne, Department of Treasury and Finance, Victoria: 161.
Doherty, S. (2008) Heathrow’s T5 History in the Mak-ing, Chichester, John Wiley & Sons Ltd.
Duffield, S. and Whitty, S. J. (2015). “Developing a systemic lessons learned knowledge model for organisational learning through projects.” Interna-tional Journal of Project Management. (0).
Flyvbjerg, B., Holm, M. S. and Buhl, S. (2002). “Underestimating Costs in Public Works Projects: Error or Lie?” Journal of the American Planning Association. 68 (3): 279.
Flyvbjerg, B., Rothengatter, W. and Bruzelius, N. (2003) Megaprojects and risk : an anatomy of ambi-tion, New York, Cambridge University Press.
Kurtz, C. F. and Snowden, D. J. (2003). “The new dynamics of strategy: sense-making in a complex and complicated world.” IBM Systems Journal. 42 (3): 462(22).
Laan, A., Voordijk, H. and Dewulf, G. (2011). “Reduc-ing opportunistic behaviour through a project alli-ance.” International Journal of Managing Projects in Business. 4 (4): 660 - 679.
Lahdenperä, P. (2012). “Making sense of the multi-par-ty contractual arrangements of project partnering,
project alliancing and integrated project delivery.” Construction Management and Economics. 30 (1): 57-79.
Merrow, E. W. (2012). “Oil and Gas Industry Megapro-jects: Our Recent Track Record.”
Parker, S. K., Atkins, P. and Axtell, C. (2008). Building better work places through individual perspective taking: A fresh look at a fundamental human process. International Review of Industrial and Organizational Psychology. Hodgkinson G. P. and J. K. Ford. Chichester, John Wiley & Sons Inc. . 23: 149-196.
Raisbeck, P., Duffield, C. and Xu, M. (2010). “Com-parative performance of PPPs and traditional pro-curement in Australia.” Construction Management and Economics. 28 (4): 345-359.
Snowden, D. J. and Boone, M. E. (2007). “A Leader’s Framework for Decision Making.” Harvard Busi-ness Review. 85 (11): 69-76.
Walker, D. H. T., Harley, J. and Mills, A. (2013). Longitudinal Study of Performance in Large Aus-tralasian Public Sector Infrastructure Alliances 2008-2013, Melbourne, RMIT University, Centre for Integrated Project Solutions: 48pp.
Walker, D. H. T. and Lloyd-Walker, B. M. (2014). “The ambience of a project alliance in Australia.” Engi-neering Project Organization Journal. 4 (1): 2-16.
Walker, D. H. T. and Lloyd-Walker, B. M. (2015) Collaborative Project Procurement Arrangements, Newtown Square, PA, Project Management Insti-tute.
Wood, P. and Duffield, C. (2009). In Pursuit of Addi-tional Value A benchmarking study into alliancing in the Australian Public Sector, Melbourne, De-partment of Treasury and Finance, Victoria: 191.o.
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author
r Derek H.T. Walker PhD 1994, (RMIT); Master of Science in Construction; Management and Economics (University of Aston in Birmingham, 1979); Graduate Diploma in Management Systems, (Swinburne Institute of Technology,1985); Higher National Diploma in Building, (University of Glamorgan Wales, 1970); Member of the Australian Institute of Project Managers (AIPM); Member of the PMI. Erasmus Mundi Scholarship in 2009 awarded by the European Community to work with the Masters in Strategic Project Management (European) MSPME Consortium to work in Umeå University, Umeå Sweden, 8,000 Euros. PhD (RMIT) 1995, ‘An investigation into factors that determine building construction time performance’ submitted in late 1994 and awarded in March 1995. 2008 Winner of the Emerald Literati Network Awards for
Excellence for the Outstanding Reviewer Award for work on Construction Innovation: Information Process Management 2007 volume. 2008 Highly Commended Award Winner at the Emerald Literati Network Awards for Excellence 2008 for the paper : Maqsood T., Walker D.H.T., and Finegan, A. D. (2007), ‘Facilitating Knowledge Pull to Deliver Innovation through Knowledge Manage-ment: A Case Study’, Engineering Construction and Architectural Management, UK,14, 94-109
To the left of Figure 3 we see the Normal sit-uation with the notional P50 or 50% chance that the cost or time would be ‘x’ and the 80% point on that curve. To the right we see the illustrated same project notionally estimated under intense and effective collaboration. Notice how the curve is far less spread between the 50% probability and 80% probability points. The additional clarity and use of broader perspectives allows much ‘tighter’ and more confident estimation of cost or time.
Evidence from the studies cited in this paper together with the illustration presented in Figure 3 indicates that close collaboration between the cli-ent, design team and project delivery team provides the real potential and actuality of more accurate estimates of time and cost as well as being able to ‘walk through’ the issues surrounding a project to enable the scope and requirements to be more effectively enunciated and understood by all parties.
2. Limitations and Constraints
The picture of alliancing as a project delivery form painted above may seem utopian. Certain-ly in presentations given in the USA and parts of Europe on alliancing this author has encountered a great deal of scepticism about the applicability of alliances. In one paper published to describe what it may feel like to be in an alliance (Walker and Lloyd-Walker, 2014) reviewers of the paper expressed concerns that an ambience was so per-sonal that it could not be accurately documented. This author has undertaken research on over 100 alliances through discussion with key alliance team members. It became apparent that the way that alliance projects are conducted, the way that risk, uncertainty and ambiguity is treated, is radically different in an alliance compared to other project delivery forms. Is this an advance?
Earlier in the paper it is stated that data from alliance studies suggest that something radically different is happening compared to the data used by for example Bent Flyvbjerg and Ed Merrow on more traditional project approaches. Results from the alliance studies suggest significant, perhaps overwhelming, improvements in project delivery performance. We can be confident that alliances work very well in certain circumstances. Howev-
er, alliances are expensive and time consuming to establish and as intimated earlier, specific skill sets are necessary and so the alliance is not a panacea. Limitations and constrains are summarised in Table 2.
3. To ConcludeThis paper outlines managing risk, uncertainty
and ambiguity and proposes that where a client wishes to retain the asset being developed for a complex project then the alliance approach should be seriously considered. The Cynefin framework provided a useful theoretical lens in which to con-sider not only risk but uncertainty and ambiguity. Many clients tend to underestimate the potential impact that ambiguity may have on emerging risks in projects and they also tend to also underestimate the need for uncertainty reduction through great cross-team understanding. The RBP Taxonomy was introduced and briefly explained to illustrate how alliancing may best address risk, uncertainty and ambiguity in complex projects. Some reference to studies undertaken of complex infrastructure projects presents interesting results that suggest that alliance projects can provide a solution to problems of poor complex project delivery.
To summarise the paper key points to be drawn are as follows:1. Alliancing is not a panacea for managing any complex
projects, there are some important pre-conditions that need to be met and these are discussed in Table 2;
2. Alliance collaboration intensity and depth allows parties to better understand each other’s perspective, assumptions and business processes. This leads to more realistic, reasonable, valid and sustainable estimates of time, resource and effort required of participants;
3. Where alliancing has been used in both Australia and New Zealand it has been successful in delivering in terms of time/cost/quality as well as in delivering many intangible benefit;
4. Alliancing requires additional skill sets, knowledge, personal attributes and experience of participants and this is perhaps the most important issue facing the future of alliancing and similar project delivery firms that are evolving from alliancing; and
5. Australia and New Zealand lead the world in this form of project delivery.
Readers may be interesting in consolidating greater knowledge about this interesting topic by accessing the following references.
KEYWORDS f managing project f project duration f global sensitivity analysis f schedule
SENSITIVITY ANALYSIS
r A B S T R A C T
Estimating the duration of a project is important in project management. The dependency structure matrix has been used
to estimate the duration of projects, and it has proven to be useful especially in complex projects, for example project with
activity overlapping. This estimate is based on the duration of the activities, their interrelationships and the permitted
level of overlap. However, these variables have uncertainty that generate uncertainty in the duration of the project. The
methods of global sensitivity analysis Morris and Sobol’ are used in this study to identify the key activities that affect the
uncertainty in the duration of the project. It is shown that adequate control of the uncertainty in these activities signifi-
cantly reduces the uncertainty in the duration of the project. Examples with and without overlapping are used to explain
the methodologies.
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GLOBAL SENSITIVITY ANALYSIS
identifying the relationship between them, as-sessing their impact on the project, and assigning resources to individual tasks (Browning, 2001).
Moreover, the scheduling of projects is based on finding resources and scheduling activities with the goal of optimizing the efficiency of the project (Tienda et al., 2011). Overlapping of se-quential activities occurs on most projects (Srour et al, 2013), which requires a two-way exchange of information among dependent design disciplines. That is, there are interdependent tasks and loops. As a result of the factors previously mentioned, recent efforts to reconcile project scheduling and DSM have sought to produce a tool that serves two purposes: analysis and project scheduling (Maheswari and Varghese, 2005; Srour et al., 2013). Researchers have demonstrated that DSM is a powerful tool in planning the sequence of tasks.
However, tasks in a project are subject to many unknown factors (Herroelen and Leus 2005; Perminova et al. 2007) that can lead to changes in scheduling. These uncertainty-causing factors include: tasks taking more or less time than was originally estimated, resources not being avail-able, required materials being ready before they are scheduled to arrive, tasks being introduced or withdrawn, and weather conditions. These chang-es or uncertainties can cause the schedule to be delayed, increase stock, or require major work, all of which lead to higher costs than those originally planned.
One of the limitations of the research con-ducted by Maheswari and Varghese is the dif-ficulty of obtaining a well-founded estimate of how long each task, the communication among tasks, and the overlap of tasks will take. Gálvez et al. (2012) studied the effect of uncertainty of task programming using DSM and grey theory or interval arithmetic. Shi and Blomquist (2012) ex-tended the DSM method proposed by Maheswari and Varghese (2005) using fuzzy numbers. Re-cently, Galvez et al. (2015) studied the uncertainty of project duration using Monte Carlo simulation and DSM. These studies are related to uncertain-ty analysis.
Uncertainty analysis refers to the determi-nation of the uncertainty in output results that derives from uncertainty in input factors (Helton et al., 2006). Therefore, the previous works are related to the characterization of uncertainty
(grey number in the work of Gálvez et al. (2015), fuzzy numbers in the work of Shi and Blomquist (2012), and distribution functions in Gálvez et al., (2015)) and presentation of uncertainty output results. However, no work has performed sensitiv-ity analysis.
Sensitivity analysis refers to the determina-tion of the contribution of individual uncertainty inputs to the uncertainty in output results (Helton et al. 2006). According to Saltelli et al. (2008), the GSA can be defined as “the study of how uncer-tainty in the output of a model (numerical or oth-erwise) can be apportioned to different sources of uncertainty in the model input”. These techniques have been widely used in different engineering areas and are of great importance to know the most significant variables in a model. The general objectives of GSA are: a) Identification of signifi-cant and insignificant factors. Possible reduction of the dimensions (number of design variables) of the optimization problem, b) Improvement in understanding the model behavior (highlight interactions among factors, find combinations of factors that result in high or low values for the model output). GSA corresponds to the evaluation of an output model when all model factors are simultaneously evaluated, being mainly resolved by numerical methods. This methodology has the advantage of simultaneously assessing all factors, while its disadvantage is that it requires a large number of data for which the model is evaluated and mathematical techniques are more complex. GSA methods can be classified into three groups (Confalonieri et al., 2010): 1) Regression methods, 2) Screening methods, and 3) Variance –based methods. Screening methods proceed from the area of experimental design and usually applied to problems that involve from a few input fac-tors to a few tens. Examples of these methods are fractional factorial design, Morris method and sequential bifurcation. In variance-based method, the variance of the model output can be decomposed into terms of increasing dimen-sion, called partial variances, which represent the contribution of the inputs (i.e., single inputs, pairs of inputs, etc.) to the overall uncertainty in the model output. Statistical estimators of partial variances are available to quantify the sensitivities of all the inputs and of groups of inputs through multi-dimensional integrals. To preclude a high computation cost, Homma and Saltelli (1996)
r Gálvez, E.D.Department of Mining and Metallurgy Engineering, Universidad Católica del Norte, Antofagasta- Chile
r Ordieres, J.B.Universidad de La Rioja, La Rioja, Spain
r Capuz-Rizo, S.F.Department of Engineering Projects, Polytechnic University of Valencia, Valencia, Spain
INTRODUCTION
The sequence of tasks is vital to the development of any project. Good sequencing reduces the amount of time necessary for completion. The order of tasks is influenced by the information flow among them. The dependency structure matrix (DSM) can be used to model information flow in complex projects, e.g. project with overlap. However, the information used by the DSM, including task duration, time required for communication, and task overlap, can have uncertain
values. However, there is no methodology for the iden-tification of significant and insignificant input factors on the project duration uncertainty. The aim of this paper is to show that global sensitivity analysis (GSA) can be used to identify significant and insignificant input factors on the project duration using the DSM.
The DSM is a widely used tool because it allows the different parts of the project or product to be broken down or to be put together. The complexity is simpli-fied by breaking down the project into smaller tasks,
*Paper approved for 2nd International Conference in Project Management at UQTR (May, 2015)
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introduced the concept of a total sensitivity index. The total sensitivity index indicates the overall effect of a given input, by considering all the pos-sible interactions of the respective input with all the other inputs. Some techniques in this group are: the Fourier amplitude sensitivity test (FAST), extended Fourier amplitude sensitivity test (E-FAST), Sobol’ method, and high dimensional model representation (HDMR).
In this paper the Morris and Sobol’ methods are applied to project planning using the DSM. Through an example it is demonstrated that GSA can identify input factors that most affect the duration of the project. Then, with proper man-agement of these input factors, the uncertainty in the project duration can be significantly reduced. An example, with and without overlapping is analyzed.
1. Strategy UsedIn this work an example is used to explain
how the GSA can be used to identify the activities uncertainty that most affect the uncertainty on the project duration. The GSA methods used are Sobol’ and Morris methods. Then, in this section an example is introduced and a brief description of Sobol’ and Morris methods are given and ap-plied to the example.
Example without overlap
The example consists of six activities from A to F, and the DSM representation of the example is given in figure 1. The DSM is a square matrix containing a list of activities in the rows and columns in the same order. The order of activities in the rows and columns in the matrix indicates the sequence of execution (for more information see Maheswari and Varghese, 2005). Values in the diagonal are the mean duration of the activities (days), for example the mean duration of activ-ity A is 2 days. A value in the off-diagonal cells indicated that these activities are information predecessors. This means that activity B needs information from activity A and activity D needs information from activities B and C. The values in the off-diagonal cells will be used later when overlapping is included in the example.
Based on the mean values of the activities the conventional project duration is estimated in 14 days (Figure 2). Note that activity C has not effect on the project duration and all other activities are in the sequence of execution without any time
leftover between activities. The conventional pro-ject duration is estimated with,
(EF)i = (ES)i + Aii 0 < i ≤ n (1)(ES)j = Max[(EF)i] 0 < i ≤ n, 0 < j ≤ n (2)Conventional project duration = Max[(EF)j] 0 < j ≤ n (3)
Where n is the number of activities; i all the immediate predecessors of j; j the current activity chosen in the order as identified by DSM; ES the early start; EF the early finish; and Aii the diagonal values of the DSM (duration of activity).
Let us assume that each duration activity has uncertainty of ±0.5 days with uniform distribu-tion. Then, for example activities A and D have a duration of ~Unif(1.5,2.5) and ~Unif(4.5,5.5) respectively. Two questions arise 1) what is the uncertainty in the project duration given the uncertainty in the activity durations, and 2) how important are the activity durations with respect to the uncertainty in the project duration. The goal of uncertainty analysis is to answer the first question, and the goal of sensitivity analysis is to answer the second question (Helton et al., 2006).
Global Sensitivity Analysis
GSA methods enable studying how the uncer-tainty in the output of a model can be assigned to different sources of uncertainty in the model input when all model inputs are simultaneously evaluated. In our case, GSA methods will be used to study how the uncertainty in the project dura-tion can be assigned to the activity duration and overlapping factor uncertainties. Two method are used: Morris and Sobol’ methods.
The Morris (1991) method is based on a dis-cretization of the inputs in levels allowing a fast exploration of the model behavior. The aim of this method is to identify the non-influential inputs with a small number of model calls. The Morris method allows classifying the inputs into inputs that have negligible effects, input having large lin-ear effects without interactions, and inputs having large non-linear and/or interaction effects. The method consists in random One-At a Time (OAT) design of experiments with random direction of the variation. The repetition of these steps allows estimating the elementary effects for each input and the consequent calculation of sensitivity indices.
The Morris sensitivity indices are the mean of the absolute value of the elementary effects (µ*
j) and the standard deviation of the elementary effects (σj). The µ*
j is a measure of influence of the j-th input on the output; if µ*
j is zero the effect
A B C D E F A B C D E F
A 2 A 2
B 0.87 4 B 0.13 4
C 0.95 3.5 C 0.05 3.5
D 0.95 0.95 5 D 0.05 0.05 5
E 0.95 5 E 0.05 5
F 0.95 0.95 3 F 0.05 0.05 3
Time factor of processor activities (Bij) Time factor of receiving information (C
ij)
FIGURE 1. DSM showing the mean values of duration of activities and time factor of transfer of information between activities.
FIGURE 2. Estimation and representation of conventional project duration.
FIGURE 3. Results of Morris method with 15 OAT experiments for example without overlap.
of the j-th input is negligible, and the larger the µ*
j value the more the j-th input contributes to the uncertainty of the output. The σj is a measure of the non-linear and/or interaction effects of the j-th input. If σj is zero then the elementary effects have no variations on the support of the input. Usually a graph of σj versus µ*
j is used because it allows to distinguish three group: low values of µ*
j (inputs that have negligible effect on the output), large values of µ*
j and low values of σj (inputs that have linear effects without interaction), and large values of both µ*
j and σj (inputs that have non-linear effects and/or interaction).
The Morris method was applied to the example (Eqs, 1 to 3) using 15 OAT experiments which require 105 model calls. The software R (R Core Team, 2013), package sensitivity (Pujol et al., 2014), which is a free software environment for statistical computing and graphics was used. Figure 3 plots the results. It is easy to visualize that A, B, D, E and F activities are influ-ential (large values of µ*
j), while C has no effects (values of µ*
j close to zero). In addition A and F have linear effects without interaction (values of σj equal to zero), and D and E have non-linear effects and/or interaction (large values of both µ*
j and σj).The Sobol’ method is based on the
partitioning of the total variance of model output V(Y), considering that the model has the form Y = f(x1, x2, ...
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xn), where Y is a scalar and xi is a model factor, using the following equation (Confalonieri et al., 2010):
(4)
Where Di represent the first order effect for each factor xi(Di=V[E(Y|xi)]) and Dij(Dij = V[E(Y|xi,xj)] – Di – Dj) to D1...n the interactions among n factors. The variance of the con-ditional expectation (V[E(Y|xi)]) is sometimes called main effect and used as an indicator of the significance of xi. The Sobol’ method allows calculating two indices, i.e., the first order effect sensitivity index corresponding to a single factor (xi):
(5)
and the total sensitivity index corresponding to a single fac-tor (index i) and the interaction of more factors that involve the index i and at least one index j ≠ i from 1 to n
(6)
The first order sensitivity index measures only the main effect contribution of each input factor on the output vari-ance. It does not take into account the interactions among factors. The first-order sensitivity index (Si) is important when the objective is to determine the most important input uncertainties. The total sensitivity index (STi) is important when the objective is to reduce the uncertainty in the output model (Adeyinka, 2007). If the first-order sensitivity index (Si) of the i input factor is very small, then the uncertainty in xi does not affect the uncertainty in the output model, . Therefore, xi is non-influential or unimportant. This does not say anything about input interactions or high-order sensitivity índices like Si,j or Si,j,k. If the total sensitivity index (STi) is also small, then apart from being unimportant, xi does not interact with other factors (high-order effects of xi are negligible). The implication of small Si and STi, is that the uncertainty in xi has no affect on the uncertainty in Y. Then, in a subsequent analysis, xi can be fixed to its nominal value (mean or median) and further research, measurement, analysis and data gathering can be directed to other fac-tors. Conversely, regardless of the magnitude of STi, a large value of the first-order sensitivity index, Si, implies that xi is influential. The arithmetic difference between STi and Si indicates the magnitude of the interactions between xi and other factors.
Sobol’ method was applied to the example (Eqs, 1 to 3) with six random inputs with Monte Carlo sampling, it has a cost of 400,000 model calls and we repeat the estimation process 100 times. The software R (R Core Team, 2013) was used with the Sobol-Jansen version in package sensitivity (Pujol et al., 2014). Figure 4 plots the results. It is easy to visualize that A, F, B, D, and E activities are influential in that order (large values of both first order and total Sobol´
indices), while C has no effects. In addition D and E have in-teraction (total and first order indices have different values). The interaction in other activities are small. These results are in agreement with the Morris method results.
Example with overlap
Let us consider overlap between activities. The overlap is represented in DSM in the form of ratios called time factors (Maheswari and Varghese, 2005). Two times factors are used, the time factor for receiving the information for the successor activity (represented by matrix Bij, given by the off-diagonal cell in Figure 1a), and the time factor for send-ing the information from predecessor activity (represented by matrix Cij, given by the off-diagonal cell in Figure 1b). For example, 0.95 in BCA implies that A can send the required information through C at the end of 0.95 times its duration, and 0.05 in CCA implies that it is essential that to continue, C receives information from A, but only at 0.05 of the time of its duration, instead of at the beginning of the task.
The natural overlap project duration is estimated with,
(7) (8)
(9)Where n is the number of activities; i all the immediate
predecessors of j; j the current activity chosen in the order as identified by DSM; ES the early start; and EF the early finish. Note that Bii and Cii are the diagonal values of the DSM (duration of activity).
Based on the mean values of the activity durations and mean values of the factor time the natural overlap project duration is estimated at 12.4 days (Figure 5). Now, let us consider that each time factor has uncertainty of ±0.05 with uniform distribution, then the off-diagonal values of Bij are ~Unif(0.9,1.0) and the off-diagonal values of Cij are ~Unif(0.0,0.1), but BBA ~Unif(0.74,1.0) and CBA ~Unif(0.0,0.26). Also uncertainty in the activity durations is included.
The Morris method was applied to the example with overlap (Eqs, 7 to 9) using 80 OAT experiments which re-quire 1,680 model calls. Figure 6 plots the results. It is easy to visualize that A, B, D, F activity durations and CBA time factor are very influential (large values of µ*j), while C, E activity durations and BBA, BFD time factors are influential. Also, there are interactions and/or non-linear effects in sev-eral input factors (large values of both µ*j and sj).
Sobol’ method was applied to the example with overlap (Eqs, 7 to 9) with 20 random inputs with Monte Carlo sam-pling, it has a cost of 1,100,000 model calls and we repeat the estimation process 100 times. Figure 7 plots the results. It is easy to visualize that A, B, D, F activity durations and CBA time factor are very influential (large values of Sobol’ in-dices), while C, E activity durations and BBA, BFD time factors are influential. Several time factors have no effects (values of Sobol’ indices close to zero). In addition B, C, D, E and CBA
FIGURE 4. Estimation of Sobol’ indices for the example without overlap
have interaction (Total and first order indices have different values).
2. DiscussionFor the example without overlap all
activities have the same level of uncer-tainty in duration, ±0.5 days, however the effect of these uncertainties on the uncertainty of the project duration is different. The uncertainty in the time duration of activities A and F are the most relevant to the uncertainty in project duration (largest values of µ*j in Morris method and largest values of Sobol’ indices). This is because these activities are sequential without inter-action and they will always influence project implementation. The uncer-tainty in the time duration of activities D and E also affect the uncertainty in
project duration. However, activity D will affect if the duration of activity D is greater than the duration of activity E, and vice versa. For that reason these activities have interaction (different values in first order and total Sobol’ indices). In Morris indices, both D and E activities have interaction and / or non-linear effects, however the model is linear (Eq. 1-3), then it must be inter-preted as interactions.
These results are independent of whether the Morris or Sobol’ method is used. Sobol’ method requires a signif-icantly greater number of model calls than the Morris method. However, as the mathematical model is simple using the Sobol’ method is not very costly from the computing point of view. Sobol’ method is more robust in the presence of non-linearity and inter-action among the activities because it explores the complete parameter space.
Moreover, Morris method is easier to implement.
These results indicate that efforts to reduce the uncertainty in the project duration should focus on reducing uncertainty in the duration of activities A, F and B. Reducing uncertainty in the duration of activities D and E have a lower impact on the uncertainty in the project duration. Reducing uncertainty in the duration of activity C will have minimal impact. If the resources are limited, the resources must be allo-cated to estimate the uncertainty of activities A, F and B.
Table 1 shows the results of Monte Carlo simulations for various scenar-ios with 1,000 calls to the model. The second column shows the results in the project duration when considering uncertainty in all activities. Columns two, three and four show the results when (in its average value) the dura-
FIGURE 5. Estimation and representation of natural overlap project duration
FIGURE 6. Results of the Morris method with 80 OAT experiments for example with overlap
FIGURE 7. Estimation of Sobol’ indices for example with overlap
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SENSITIVITY ANALYSIS /// ANALYSIS OF PROJECT DURATION UNCERTAINTY USING GLOBAL SENSITIVITY ANALYSIS
tion of activities A and F, D and E, and C is fixed, respectively.
Although the uncertainty in the duration of each activity has uniform distribution, the project duration is normally distributed. This was observed by Gálvez et al. (2015) and confirmed in the results observed in this example. Note that the average value of project duration is larger than the value calculated with the mean values (14 days), because the interaction was not considered. In fact if the activity duration with the largest interaction are fixed (D and E) the mean value is close to the 14 days.
If all activities are uncertain then the uncer-tainty in the project duration is 3.4 days, if the uncertainty in activity C is removed, the uncer-tainty in the project duration is not significantly reduced, 3.3 days. However, if the uncertainty in the activities A and F are eliminated the uncer-tainty in the project duration is reduced to 1.8 days, compared with 2.7 days if the uncertainty is removed in activities D and E. This confirm that GSA can be used to reduce the uncertainty in project duration.
The final decision on where to focus efforts in reducing the uncertainty depend on these results and on other aspects such as the associated cost, availability of resources and the feasibility of re-ducing the uncertainty in the activity duration.
In the example with overlap it is observed that in general the time factors have less effect on the uncertainty in the project duration, with the exception of the time factor CBA. This is not surprising because it is the time factor with most uncertainty. However, the effect of the BBA time factor is not as significant despite having high uncertainty. This is because the effect of CBA depends on the duration of activity B, whereas the effect of BBA depends on the duration of activity A (see equation 7), and because the duration of B is larger than the duration of activity A its effect increases.
If all activity durations and time factors have uncertainties, the uncertainty in the pro-ject duration is 4.1 days (based on Monte Carlo simulations), if the input factors that most affect the project duration uncertainty are fixed at their mean value (activities A, F, B, D, and time factor CBA) the project duration uncertainty is reduced to 2.0 days. This effect is significant. If the dura-tion of activities C and E is fixed then the project duration uncertainty is 4.0 days, i.e. its effect is marginal. On the other hand, if the duration of activities A and F is fixed the uncertainty is 3.0 days, i.e. there is a significant effect. These simulations confirm that using the methods of Morris and Sobol’ allow to identify input factors that affect the uncertainty in the duration of the project and the control of uncertainty of these input factors allow to diminish the uncertainty in project duration.
The Monte Carlo simulation when all activity durations and time factors have uncertainties gives a mean value for the project duration of 12.75 days, which is different from the value when the average value of the input factors are used (12.4 days). This is explained because when deter-ministic values are used the interaction between input factors are not considered.
3. ConclusionWe have proposed using the Morris and Sobol’
methods in order to identify the input factor un-certainty which is responsible for the uncertainty in project duration. The DSM-based scheduling proposed by Maheswari and Varghese (2005) was used to model de project duration based on the duration of the activities and the time factor asso-ciated to activity overlapping. It was demonstrat-ed that both methods can be used for this pur-pose, however the Sobol´ method has shown to
Project duration
No activity fixed A and F fixed D and E fixed C fixed
Minimum 12.41 13.15 12.74 12.53
1st Quartile 13.76 13.97 13.67 13.80
Median 14,16 14.19 14.03 14.20
Mean 14.16 14.19 14.02 14.19
3rd Quartile 14.52 14.47 14.39 14.59
Maximum 15.82 14.97 15.43 15.83
TABLE 1. Uncertainty analysis in project duration for various scenarios.
authors
r Edelmira Gálvez joined the Department of Mines and Metallurgical Engineering, the Universidad Católica del Norte in 1999 as assistant professor. Currently, she is enrolled in the doctoral program in project engineering at Universidad Politécnica de Valencia, Spain. Professor Gálvez graduated in Metallurgical Engineering from the Universidad Católica del Norte (1989, Chile), and Industrial Engineering from the University of Antofagasta, (1998, Chile). During the period 1992-1994, she studied at the University of Wisconsin-Madison (USA), where she obtained the PD degree. Professor Gálvez’s principal research interest is the use of a systems approach to solving problems in mineral process, design and analysis. In particular, his research covers the development of systematic methods and tools for solving problems in the mining industries, which
can be classified in terms of the following topics: modelling, design, analysis, and optimization. Professor Gálvez has published 19 peer reviewed journal articles, more than 30 conference papers, and more than 10 book chapters.
r Salvador F. Capuz-Rizo, PhD in Industrial Engineering and Professor of Project Engineering since 2003 at Universitat Politècnica de València (Spain). His research areas are Project Management, Environmental Concious Design and Eco-efficiency. Currently serves as Presi-dent of Spanish Project Management and Engineering Association (AEIPRO).
r Joaquín Ordieres-Meré has been a full professor in Project Management since 1997 and currently works at the UPM (ETSII). His re-search interests include project management, business intelligence and business analytics as well as decision support systems. He serves as reviewer for different peer-reviewed journals and is a member of the editorial board of IDMMM journal.
Adeyinka A.L., (2007). Applications of Sensitivity Anal-ysis in Petroleum Engineering, Thesis University of Texas at Austin.
Browning, T.R. (2001). Applying the design structure matrix to system decomposition and integration problems: A review and new directions. IEEE Trans-actions on Engineering management, 48 (3), 292-306.
Confalonieri R., Bellocchi G., Bregaglio S., Donatelli M., & Acutis M., (2010). Comparison of sensitivity analysis techniques: a case study with the rice model WARM, Ecological Modelling, 221, 1897 -1906.
Gálvez E.D., S.F. Capuz-Rizo, & J.B. Ordieres, (2012). Study of the uncertainty of task programming using the dependency structure matrix, Información Tec-nológica, 23(1), 19-34.
Gálvez, E.D., Ordieres, J.B., & Capuz-Rizo, S.F., (2015). Evaluation of Project Duration Uncertainty using the Dependency Structure Matrix and Monte Carlo Simulation, Revista de la Construcción, submitted.
Herroelen W., & R. Leus, (2005). Project scheduling under uncertainty: survey and research potentials, European J. of operational research, 165, 289-306.
Homma T., & Saltelli A., (1996). Importance measures in global sensitivity analysis of nonlinear models, Reliability Engineering & System Safety, 52, 1-17.
Helton, J.C., Johnson, J.D., Sallaberry, C.J., & Storlie, C.B., (2006). Survey of sampling-based methods for uncertainty and sensitivity analysis, Reliability Engi-neering & System Safety, 91, 1175-1209.
Maheswari J.U., & K. Varghese, (2005). Project scheduling using dependency structure matrix,
International Journal of Project Management, 23 (3), 223-230.
Perminova O., M. Gustafsson, & K. Wikström, (2007). Defining uncertainty in projects-a new perspective, Int. J. Proy. Management, 26, 73-79.
Pujol, G., Iooss, B., Janon A., (2014). Sensitivity: Sensitivity Analysis. R package version 1.10.1. http://CRAN.R-project.org/package=sensitivity
R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statis-tical Computing, Vienna, Austria. URL http://ww-w.R-project.org/
Shi Q., & T. Blomquist, (2012). A new approach for project scheduling using fuzzy dependency structure matrix, International Journal of Project Manage-ment, 30, 503-510.
Srour I.M., Abdul-Malak M.U., Yessine A.A., & Ramadan M., (2013). A methodology for scheduling overlapped design activities based on dependency information, Automation in Construction, 29, 1-11.
Saltelli A., Ratto M., Andres T., Campolongo F., Cariboni J., Gatelli D., Saisana M., & Tarantola S., (2008). Global sensitivity Analysis: The primer, John Wiley & Sons Ltd.
Tienda P., & M. Romano, (2011). A matrix algorithm RUPSP/GRUPSP “no splitting allowed” for produc-tion planning under Lean Construction method-ology based on production processes, Revista de la Construcción, 10 (2), 90-103.
refe
renc
es
be more adequate in the ranking of the input factors and the Morris method has shown to be more adequate for screen-ing of input factors. It was demonstrated that the control or reduction in the uncertainty of the key activity duration can reduce the uncertainty in the project duration.
It is clear that for complex projects the problem of pro-ject scheduling is far more extensive than just the duration of activities, it is also related to the issue of organizational
structure, resource allocation and behaviors of stakeholders. Then, the identification of the key activities from the point of view of project duration can help to reduce the number of variables and simplify the schedule problem.
If the resources are limited, approximate uncertainty can be assigned to the duration of activity and time factor. After the key input factors are identified the resources can be allo-cated to estimate the uncertainty of the key input factors.
KEYWORDS f end of life aircraft recycling projects f green image of manufacturers f sustainable development objectives
EMPIRICAL STUDY
r A B S T R A C T
EOL aircraft recycling projects have been receiving lots of attention in aviation indus-
try considering the green image of manufacturers and the sustainable development
objectives. Despite the large amount of investment in these projects, there is still a lack
of research on how it should be accomplished to be successful. Therefore, the purpose
of this paper is to derive critical success factors for the successful implementation of
EOL aircraft recycling project. We hypothesize that there are certain factors in different
phases of the life cycle of the projects that positively impact their success. A spiral model
is used to consider project lifecycle phases, the role of key stakeholders and the influen-
tial factors in different phases. This research seems to be the first practical study on the
management issues of EOL aircraft recycling projects.
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There are some major challenges in addressing EOL aircraft problem including the absence of relevant directives in the aviation industry, size of treated materials from EOL aircrafts, the com-plexity and challenges in fleet recycling process and the multilayered relationship among players. The other challenge is the sustainability of the whole value chain considering all involved stake-holders. Furthermore, considering the essential role of aircraft manufacturers and their inclusive attention to corporate social responsibility, the green strategies need to be incorporated into the design stage. EOL aircraft recycling management covers not only technical specifics, but it also requires an integrated strategic approach which cover sustainability and value creation concepts at the same time. This paper aims to address the success factors in EOL aircraft recycling projects. Typically, cost, time and quality are the criteri-on for measuring the project success. However, in the complex and dynamic context such as aerospace industry, a novel framework is needed to formulate the success in the pre-implementa-tion and implementation phases. Moreover, a few empirical studies have been conducted in diverse industrial environment that support the inclusion of stakeholders’ views in determining the project success. This paper aims to address the critical success factors of these projects through their life cycle. A spiral model and four propositions are proposed to address the link between the external/internal factors and project success. A framework for empirical study is also provided. The rest of this paper is organized as follows: in part 1, a review of EOL aircraft recycling projects is provided. In part 2, the literature review on critical success factors of the projects is explained. In addition, the main differences between EOL re-
cycling project and other types of the projects are demonstrated in order to shed light to the nature of these projects. Part 3 introduces a conceptual model and propositions. In part 7, a framework for the empirical survey and data analysis are pre-sented and finally, part 8 provides the discussions and the main conclusions of the study.
More than thousands of aircrafts will be retired in the next 2 decades. The recycling of these aircrafts provides several opportunities for aerospace business. Moreover, considering the increasing focus of aerospace community on environmental issues and landfill regulation, owners seek for efficient, economically and en-vironmentally-sound methods for EOL aircrafts (AFRA web site). From the aircraft manufac-turer’s perspective, the green image related to treatment of aircrafts at the end of life based on environmental concerns shifted gradually as a competitive advantage in the global market (Siles, 2011). EOL aircraft recycling can provide several business benefits. Regardless of the resale of the aircraft’s reusable parts, it is possible to make money from the recycling of materials. Further-more, the reduction of the environmental impact of a retired fleet through recycling process has an important role for all actors in operation process-es of recycling the EOL aircrafts. Moreover, such approach can ensure long term social benefits. Indeed, the development of this sector and having an infrastructure for recycling can lead to many
r Samira KeivanpourDépartement de génie mécanique, Université Laval, Québec
The number of aircrafts at the end of life (EOL) is continuously increas-ing. Dealing with retired aircrafts considering the environmental, social and economic impacts is becoming an emerging problem in the aviation industry in near future. Aircraft orig-inal manufacturers have an extensive background of looking for solutions to reuse or recycle aircrafts and their components. The two largest airframe manufacturers, Airbus and Boeing, are at the head of research and main projects in this field. In 2005, Airbus initiated a project in order to achieve new eco-efficient standards for the management of EOL aircrafts. Boeing has taken a leadership role in aircraft life cycle and end-of-service recycling strategies and established a consortium
to provide environmentally responsi-ble options for aging aircrafts in 2006. In 2012, Bombardier continued its partnership with the Consortium for Research and Innovation in Aerospace in Quebec, as well as the other research centers and universities to better understand end-of-life requirements and commercially practical recycling technologies for aircrafts. The goal of such efforts is to develop methods and test them to perform profitable recycling processes for EOL aircrafts, minimize the environmental impacts of the whole treatment process and maximize economics and social values for the involved stakeholders. Con-sidering, dynamics and multidimen-sionality of aircraft recycling projects, conventional management systems cannot be sufficient and responsive.
THE CRITICAL SUCCESS FACTORS
FOR TREATMENT PROJECTS
END OF LIFE AIRCRAFT
*Paper approved for 2nd International Conference in Project Management at UQTR (May, 2015)
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lasting jobs opportunities, which are the factors of social and local development (Sainte-Beuve, 2012). Developing new strategies for dismantling and decision support relating to design and manage-ment of EOL aircraft treatment in uncertain busi-ness environment needs to be taken into account. The efficiency of the treatment operation can be measured by its efficiency in creating value for all stakeholders involved in the problem.
Projects
According to Keivanpour et al., (2013, 2014- a, b), original manufacturers have a long history of looking for ways to reuse or recycle aircrafts and their components. In the past, at least 50 percent of the material used in aircraft construction was reused or recovered. The two largest airframe manufacturers, Airbus and Boeing, are at the head of research and main projects in this field. “. Airbus initiated a PAMELA project with recycling and recovery rate by 85 percent of the aircraft weight. Boeing has taken a leadership role in air-craft life cycle and end-of-service recycling strat-egies for more than 50 years. Aircraft Fleet Recy-cling Association, AFRA, is a global consortium of more than 40 companies that provide environ-
mentally responsible options for aging aircrafts (Watson, 2009). These options include maintain-ing and reselling reliable airplanes and returning them to service. Safe parts recovery scrapping and recycling services are available for airplanes that cannot be returned to service (Boeing 2010 Environment Report). Bombardier is also working on recycling challenges. In 2012, this company continued its partnership with the Consortium for Research and Innovation in Aerospace in Quebec (CRIAQ), as well as the other research centers and universities to better understand end-of-life requirements and commercially practical recycling technologies for aircraft (Bombardier Website). In this part, the characteristics of two projects (PAMELA and CRIAQ ENV-412) are presented with more details. The first main pro-ject was introduced by Airbus in 2005. PAMELA (Process for Advanced Management of End-of-Life Aircraft) was a two year project that focused on dismantling an A300B4. During this project, the effectiveness of different techniques was assessed (Aircraft Technology; PAMELA). The project was a partnership between Airbus and Suez-Sita, EADS CCR, EADS-Sogerma Services and the Hautes Pyrénées Prefecture. The estimated budget of the
project was 3.24M€ and the duration of the project was 2 years (PAMELA). The second project, Process for Advanced Management and Technologies of Aircraft End of Life” CRIAQ ENV-412, was initiated in 2012. The project involved the dismantling of CRJ100/200 aircraft at the Centre Tech-nologique en Aérospatiale (CTA) in Quebec, Canada. The duration of the project was 3 years and its budget estimated to be 1.4M$. Bombardier, BHTC, CRIAQ, Aluminerie, Alou-ette, Sotrem-Maltech, BFI, NanoQuebec, MITACS and four universities have contributed to the project (Bombardier report). Baccearini (1999) proposed the logical framework method (LFM) in order to provide a common understanding of overall project scope for all project participants. In this approach a hierarchy of project objectives shows the linkage between different levels of the project objectives. The author believes that this framework is valuable for addressing the concept of project management success (see Figure 2). Based on this approach, the different features of these two projects are illustrated in Table 1.
2. Critical success factors of the projectsLiterature review
The success of implementing any complex system re-quires identifying factors that promote the effective oper-ation via the life cycle of the system (Chou & Chang, 2008; Klein & Martz, 2003; Tsai et al., 2011). Moreover, the stake-holders play the critical role in order to implement any pro-ject successfully (Soh et al., 2000). The different facilitators have an influence on the projects’ deployment. Investigation of these factors is needed to better understand the involved parameters in the projects’ implementation process. The lit-erature on success factors of projects is quite vast. In recent years, this topic has received much attention from research-ers and practitioners. In this section, we have an overview of the studies that address success factors of projects. Table 2 shows some of these works.
What is different in the case of EOL aircraft recycling projects?
In this part, we review some specific features of EOL aircraft recycling project that differentiates it from other projects. The context of the project, level and the type of complexity of the project could influence the critical success factors. There are several studies that address the complexity of projects index. Raz et al. (2002) defined the complexity based on uncertainty, complexity of scope and criticality of time goals. Saynisch (2010) makes distinction between project complexity and environmental complexity. The size of the project, number of stakeholders, location and the type
of contract are the characteristics mentioned by Turner and Muller (2006) as project complexity. Muler et al. (2012) sum-marized the features of complexities across different refer-ences and mentioned three types of complexity: complexity of faith that relates to the novelty of the problem and its un-certainty; complexity of fact which addresses the structural complexity and the vast amount of interdependent informa-tion and complexity of interaction that presents the conflicts of the involved stakeholders and the interaction among them. Hussein et al. (2014) surveyed the complexities in new product development projects. They mentioned diversity, uncertainty, interdependency, task ambiguity and novelty as the sources of complexities in the projects. Botchkarev & Finnigan (2014) presented a systematic approach to com-plexity in project management and addressed the different attributes of the complexity in different projects such as IT or engineering project. Hence, the literature on complexity is extensively broad and covers different types of attributes. In this part, the different characteristics of EOL aircraft recycling project are considered to reflect the complexity of these projects. From a structural point of view, the different task functions in the project including disassembly, disman-tling, logistics; network design, material recycling and life cycle assessment as well as the multidisciplinary nature of the project with different disciplines such as mechanical, industrial, material and aerospace engineering and man-agement in addition to the different required databases infrastructure for different sub processes of EOL aircraft treatment could be mentioned. From technology novelty, the novelty of different technologies for material recovery and composite recycling, sorting and disassembling techniques could be considered as complexity attributes. The challenges of project management including the complexity of func-tional tasks, the uncertainty and ambiguity of tasks and lack of robustness in project elements are also other complexity features in these projects. The diversity of different stake-holders involved in the project, their expectations and the interaction among them are the other characteristics that makes the project complex.
3. The proposed spiral model We proposed a fresh spiral model for addressing the
critical success factors of the project through the life cycle (Figure 2). The circular dimension shows the increasing cost incurred in performing the different steps of the project in the performance measurement framework. The angular dimension represents the development of each cycle of the spiral that leads to project deliverables in the framework of the stakeholder’s commitment. The related tasks of the pro-ject in each cycle and the critical success factors are shown in Table 3.
Measuring the success of the projects is extensively known by practitioners and academics as a difficult concept.
FIGURE 1. Logical framework method for project objectives (adapted from Baccearini, 1999)
Project PAMELA ENV-412
Goal Eco efficiency in aircraft life cycle Corporate social responsibility and design for environ-ment
Purpose Setting new eco-efficient stand-ards for the management of the end-of-life aircraft
Developing general methods and test them on an experimental platform to dispose of and/or implement recycling processes and dedicated infrastructure for end-of-life aircraft and helicopters
Outputs First full-scale demonstration project and has identified a generic methodology for handling all end-of-life aircraft, along with a set of best practices
Optimizing recycling processProvide accurate information on the end-of-life of aircraft Implementing recycling processes lessons learned for future design
Inputs Resources and work Resources and work
TABLE 1. Two EOL recycling projects based on LFM approach
GOAL
PROJECT SUCCESS
PURPOSE OUTPUTS INPUTS
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to Ahern et al., (2014, p.1371), complex project management can be considered as a form of complex prob-lem solving due to the governance challenge of knowledge management under uncertainty. This study cited the characteristics of the complex problem from the earlier literature (Weinberg 2001; Snowden 2002; Cleden 2009 and Swinth 1971). These features could be revealed in EOL recycling projects too. The solutions for the EOL recycling problem must serve different stakeholders. Different functional teams involved in ENV-412 project including academics and industrial teams. Each of these partners had a
FIGURE 2. Spiral Model for representing critical success factors through project life cycle
NO Authors and year of publication
Sources Critical success factors Type of the projects under study
1 Belassi, & Tukel, (1996)
International journal of project management
Project factors; Management and teams factors; Organization factors; External environment factors
Construction ;Management Information systems; Defense ;Manufacturing Environ-mental; And others(including educational, HealthCare and pharmaceutical)
2 Ihuah et al.,(2014) Internation-al Journal of Sustainable Built Environment
The project managers’ performance; The charac-teristics of the team members; and the external project environment; Stakeholders supports
Sustainable social (public) housing estates’ delivery/provision
3 Gudienėa, et al.,(2013)
Procedia Engi-neering
External factors; Institutional factors; Project re-lated factors; Project management and members factors; Project managers factors;Client factors; Contractors factors
5 M¨uller et al.,2012 IEEE Transactions On Engineering Management
Leadership; emotional (EQ), intellectual (IQ), and managerial (MQ); leadership competences
Engineering, IT, combined Engineering and IT; Complex projects
6 Lindner & Wald 2011
International Journal of Project Management
Knowledge management including (Culture & Leadership; Organization & Processes; ICT-sys-tems)
Different projects ;IT/software ;Automo-tive ;Plant construction ; Manufacturing ; Consulting ; Public enterprises ;Transpor-tation/logistics ;Other services ; Construc-tion ;Pharmaceutical/chemical ;Financial services ;Telecommunication
7 Goo Hong et al.,(2012)
Internation-al Journal of Advancements in Computing Technology
User’s active participation; CEO’s active interest and support; Appropriate policy support of the Government; Continuous communication with related organizations; Application of feasible technology; Securing User’s ability to utilize the system
Radio Frequency Identification/Ubiquitous Sensor Network
8 Pisarski et al.,(2011)
25th Annual Aus-tralian and New ZealandAcademy of Management Conference
Project leader characteristics; Team leader charac-teristics
Complex projects
TABLE 2. Review in the literature
Phase AREA Task Critical success factors
Star
t- u
p
1 Defining the Goal and purpose of the project
based on the key stakeholder's Interest
Key stakeholders involvement ; Objectives defi-
nition
2 Preparing the scope of work (The draft of agreement) including estimated budget and
schedule
3 Defining the project outcomes and perspective
of the deliverables
4 Preparing the preliminary framework for evalu-
ation and project management process
Plan
ning
5 Forming the team of the project Key stakeholders involvement ; Objectives defi-
nition6 Finalizing the scope of the work, budget and
schedule and preparing final agreement
7 The approval of the project
8 Finalizing the performance measurement
framework and communication plan
Exec
utio
n
9 Production of the key deliverables Knowledge management ; key stakeholders
involvement; communications and coordination
mechanism10 Project management (Monitoring time, budget and quality)
11 Controlling the challenges and changes
12 Reporting and communication
Clo
se-
out
13 Contract close out and celebration Stakeholder’s expectations; Deliverables reporting
14 Team feed backs and reporting
15 Post implementation review and recommenda-
tion for future
TABLE 3. The related areas, tasks and success factors in each cycle of the spiral model
specific goal in the project considering their organizational mission. Moreover, there is a high degree of correlation between different sub-tasks. One academic team may be formed by the participation of the different universities and research centers due to task complexity. For example, new recycling technol-ogy requires the involvement of different experts and researchers to maximize the performance of the project’s outcomes. The novelty of the problem and challenges such as integrating the design for recy-
User satisfaction is a common indicator of system success. The questionnaires for stakeholders to express their view re-garding project could be a measure for project success (e.g., Guinan et al. 1998). Hence, we can classify the measures in three categories; project management success factor such as meeting the time, budget and project specification (Shen-har et al., 2001) performance improvement which includes the process improvement and technology development and finally the satisfaction (including partners satisfactions and all other stakeholders involved in the project’s life cycle).
4. Hypothesis development With this model, we find the relationships among the
identified success factors extracted from a literature review and their impacts on the different phases of the projects. Furthermore, the importance of the different project phases on project success indicators could be revealed. According
COSTS
PROJECTDELIVERABLES
PERFORMANCEMEASUREMENT
STAKEHOLDERSCOMMITMENTS
1 2
34
5 6
78
910
1112
13 14
15
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cling in the early stage of aircraft design process needs combining existing ideas and techniques in a fresh way. If we look at the logistics network of EOL aircraft recycling, the complexity of this network could be more than the complexity of supply chains and logistics networks. Choi & Krause, (2006) introduced ‘‘complexity’’ as a key area of managerial consideration in supply chain analysis. Based on the concept of complexity, the authors defined the complexity in supply chain analysis as a factor of the number of suppliers in supply base, the level of interaction among them and the degree of variation between these suppliers in terms of technology, size or organi-
zational culture. Hence, the number of suppliers, their variations and the level of interaction make the operational load for focal company in supply chain management. They analyzed this complex-ity by defining a supply base which includes the different suppliers and their interactions. In the analysis of EOL aircraft treatment value chain, we are faced with three bases: process base, per-formance base and stake-holder’s base (Figure 3). If we define the complexity in each base (level) as a factor of number of elements, their interac-
tion and the diversity among them, then the total complexity will be the function of these three levels of complexity. There are different sub-pro-cesses in the treatment of EOL aircraft. The relations among these processes and the diversity of these sub-processes in terms of the technology, required human resources, the challenges of im-plementations, and the geographical location and so on form the first level complexity. The second
level of complexity encompasses the sustainabil-ity, efficiency and effectiveness of this process. The number of aspects in this level (economic, so-cial and environmental level), the trade-off among these different aspects and the diversity of these criteria form the second level complexity. The third level includes the different players involved in this problem. The number of players, the inter-action among them and their diversity in terms of size, the influence, type of organization and so on form the third level complexity. Therefore, the total complexity in this problem is the interaction of three level complexities.
In the following sub sections, we discuss the essential aspects of the EOL recycling project in order to develop the hypothesis.
5. The objectives of the projects & key stakeholders involvement
As mentioned before, the general purpose of the project is developing general methods to implement recycling processes for end-of-life air-crafts. This general goal requires several tasks and considering the different tasks, the involvement of appropriate partners is essential. The first objec-tive regards conducting subassembly studies in order to perform the disassembling process. This step contains the decommissioning of the aircraft and passing several steps aligned with related regulations such as air worthiness certificate, etc. After the drainage, the next step is removal of the main equipment such as engines, landing gear, electronic, interior to deliver an empty airframe for the dismantling step. Dismantling process in-cludes finding the best methods and technologies to detach the main part of the air frame in dif-ferent sections (for example wings from fuselage). Then each part should be divided into smaller parts and sorted by material types to be prepared for recycling and recovery process. The logistics network of the recovery process and value chain
FIGURE 3. The complexity in EOL Aircraft treatment (Keivanpour, 2014-b
analysis should also be studied. Considering these three steps, several universities and research teams with expertise in aircraft assembling and disassembling, maintenance and logistics should be involved in the project.
One of the main challenges in retired aircraft recycling is alu-minum recovery considering the level of residual impurities found in the recycled metal. Hence, par-ticipation of the industrial and ac-ademic partners in materials and chemical engineering, logistic, economic and environment issues in re-using of materials is critical. All the knowledge achieved in these processes could be inte-grated in the design phase of new aircraft manufacturing in order to reduce difficulties to disassemble, to recycle and reduce the environ-mental footprint of this economic activity. The involvement of air-craft manufacturers such as the key stakeholder is also crucial to maximize the long term sustaina-ble outcomes of the project. Based on Ahern et al., (2014, p.1377), “Fostering a common will around a challenging mutual goal and pacing this common will towards achieving the mutual objective are two separate but crucial ingredi-ents for overall project success in complex organisational settings”. Moreover, typically the solution must serve a variety of objectives.
In the majority of cases in sub tasks, there is no clear vision about the objectives. The ambi-guity in the objectives in the plan-ning phase is more likely to have a set of flow objectives that could be transferred to the itemized parts during the life cycle of the project (Figure 4).
FIGURE 4. The nature of the objective in complex projects (transition from flow to itemized parts)
FIGURE 5. The variety of functional teams in EOL aircraft recycling project and emerging knowledge
6. Knowledge management & communication challenge
The formation of new knowledge and the effective coordination are the main aspects for governance in com-plex project management. According to (Cleden 2009, cited in Ahern et al., (2014); there is the ‘four quadrants’ approach to project uncertainty: ‘known knowns’ (knowledge), ‘known unknowns’ (risks), ‘unknown knowns’ (untapped knowledge), and ‘unknown unknowns’ (uncertainty). The uncertainty and emergent nature of knowledge in the EOL aircraft recycling project makes it as a complex problem solving environment. Figure 5 shows the different functional teams in the project and the uncertain nature of the knowl-edge from a variety of disciplines.
Now, the question is how can this complex setting and unclear objectives lead to the specific deliverables for the players?
Learning the project is the answer to the question, which includes the pro-ject functional teams, as a community of learners, developing the knowledge over the project life cycle with a problem
solving approach (Polanyi, 1967). Another challenge is the governance of these dif-ferent teams. Communication and coordi-nation between academics and practition-ers with different levels of expertise and tacit knowledge in a way to have a joint governance organization (Figure 6) is a complex task (Czarniawska-Joerges, 1989, 1992; Polanyi, 1969). Ahern et al., (2014) introduced a new term for the coordina-tion of emergent knowledge in complex project setup, “common will of mutual in-terest”. This term aids to highlight the role of stakeholder’s expectations and transla-tion of the outcomes and deliverables of the projects to common outset to facilitate the governance and sustain the success.
Based on the above discussions, we propose the following hypotheses:
ff H1: The key stakeholder’s involvement and clear objectives definition are the critical success factors of EOL project that positively affect the start-up and planning phases of the project.
ff H2: The knowledge management; key stakeholders involvement; communications and coordination mechanism are the critical success factors of EOL project that positively affect the execution phase of the project.
ff H3: The stakeholder’s expectations and deliverables reporting are the critical success factors of EOL project that positively affect close-out phase of the project.
EOL AIRCRAFT TREATMENT
PROCESS BASE
STAKEHOLDERS BASE
PERFORMANCEBASE
ACADEMIC TEAM 1ASSEMBLY/DISASSEMBLY
PROCESS PLANNINGRECYCLING AND
REMANUFACTURINGLIFE CYCLE ANALYSIS
INDUSTRIALPARTNER-TEAM 1
AIRCRAFT MANUFACTURING
INDUSTRIALPARTNER-TEAM 2
ALUMINIUM ALLOYS RECYCLING
INDUSTRIALPARTNER-TEAM 3
WASTE MANAGEMENT
INDUSTRIALPARTNER-TEAM 4
NANO TECHNOLOGYINNOVATIONS
ACADEMIC TEAM 3ALUMINIUM AND MAGNESIUM
RECYCLINGNEW RECYCLING TECHNOLOGIES
ACADEMIC TEAM 4LOGISTICS
RECOVERY NETWORKVALUE CHAIN ANALYSIS
Emerging and uncertain Knowledge
ACADEMIC TEAM 2GREEN PROCESSES
RECOVERY TECHNOLOGIESMANAGING ENVIRONMENTAL INFORMATION ASSOCIATED
WITH HUMAN ACTIVITIES
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EMPIRICAL STUDY /// THE CRITICAL SUCCESS FACTORS FOR END OF LIFE AIRCRAFT TREATMENT PROJECTS
7. Proposed framework for empirical study
From the literature review, a list of success factors is obtained. This review helps to find similarities and dissimilarities between the project under study and other project management contexts. The objective of the literature review is to develop a framework for the survey and to prepare the questionnaire. All success factors will be considered to be able to measure the related attributes. The preliminary draft of the questionnaire will be sent to the key members of the project in order to prepare the main research questionnaire. A prelimi-nary draft of the questionnaire is provided in Appendix A. There are different mechanisms for communication in the complex projects. The meetings, steering com-mittees and regular reporting are the common ways for communication and strategic decision making in the projects. For example, in ENV-412 there are two types of meetings; technical meetings and general meetings. The technical meetings are held with the participation of the researchers and graduate students in order to discuss different technical issues in the project. In the technical meetings, the agenda, reports and presentations for gen-eral meetings will be prepared. In the general meetings, the key stakeholders and industrial partners are par-ticipating. Hence, the strategic decisions regarding the next steps of the projects will be made. The members of technical meetings and general meetings could partic-ipate in the survey in order to evaluate the hypotheses. The respondents could be requested to rate the questions
FIGURE 6. Joint governance in emerging knowledge environment
the design phase of the manufacturing process in order to produce long term sustainable outcomes. It could bring insights for key stakeholders to identify the critical dimension of the projects, which leads to an effective and efficient cooperation.
Acknowledgments
We would like to acknowledge funding from Bombardier, BHTC, CRIAQ, Aluminerie Alouette, Sotrem-Maltech, BFI, NanoQuebec, MITACS and NSERC.
according to a five point Likert scale (1=very low and 5=very high), based on their actual hands-on experience on the project. The different statistical methods, such as multiple regression or factor analysis could be used to analyze the data and the questionnaire. Multiple Regression model is a mathe-matical model that can relate a number of independent variables to a depend-ent variable. Therefore, this technique could be selected to find the critical success factors through the life cycle of the project.
8. Conclusion & practical application
This paper discusses the internal/external factors that lead to the success of EOL aircraft recycling projects. It provided a comprehensive perspec-tive to the successful implementation of these projects. We surveyed the current literature on critical success factors of the projects. We proposed a conceptual framework and formu-lated propositions for strategic factors related to the performance of these projects. An evaluation of the critical success factors of EOL aircraft projects, based on bringing together the views of different stakeholders involved, leads to better outcomes and understand-ing about the problem. The improved understanding could create essential strategies to lessen the associated risks and unproductive management. It could result in considerable per-formance improvements in project management features and knowledge management. It could also help to de-sign an effective performance measure-ment framework. Identifying critical success factors in each cycle of the pro-ject could transform the project as the best practices for future experiences. As the EOL aircraft recycling is a novel and emerging problem, accomplishing a successful project can lead to certain practices for practitioners, particularly for industrial partners. The deliverables of such project could be integrated in
No Questions
Rate
Very Low
Low
Moderate
high
Very high
1 How do you rate the effect of key stakeholder’s involvement in start-up phase on project success?
2 How do you rate the effect of key stakeholder’s involvement in planning phase on project success?
3 How do you rate the effect of key stakeholder’s involvement in execution phase on project success?
4 How do you rate the effect of key stakeholder’s involvement in close-out phase on project success?
5 How do you rate the effect of objectives definition in start-up phase on project success?
6 How do you rate the effect of objectives definition in planning phase on project success?
7 How do you rate the effect of knowledge management in start-up phase on project success?
8 How do you rate the effect of communication and coordination in start-up phase on project success?
9 How do you rate the effect of knowledge management in planning phase on project success?
10 How do you rate the effect of communication and coordination in planning phase on project success?
11 How do you rate the effect of knowledge management in execution phase on project success?
12 How do you rate the effect of communication and coordination in execution phase on project success?
13 How do you rate the effect of knowledge management in close-out phase on project success?
14 How do you rate the effect of communication and coordination in close-out phase on project success?
15 How do you rate the effect of stakeholder’s expectation in planning phase on project success?
16 How do you rate the effect of stakeholder’s expectation in execution phase on project success?
17 How do you rate the effect of deliverables reporting in execution phase on project success?
18 How do you rate the effect of stakeholder’s expectation in close-out phase on project success?
19 How do you rate the effect of deliverable reporting in close-out phase on project success?
20 Please indicate any other factors that you think are critical in ENV 412 project success
21 If any question doesn’t have enough clarity, comprehensiveness and completeness, please specify here.
22 Other comments:
Appendix A: Questionnaire
ACADEMICTEAM 1
ACADEMICTEAM 2
ACADEMICTEAM 3
ACADEMICTEAM 4
INDUSTRIALTEAM 1
INDUSTRIALTEAM 2
INDUSTRIALTEAM 3
INDUSTRIALTEAM 4
JOINT GOVERNANCE
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EMPIRICAL STUDY /// THE CRITICAL SUCCESS FACTORS FOR END OF LIFE AIRCRAFT TREATMENT PROJECTS
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authors
r Samira Keivanpour is post-doctorate research-
er at department of mechanical engineering at Laval
University. She earned her Bachelors in Electrical
Engineering and MBA in Operational Management
from Iran. Her research activities are focused on
sustainable development, design for environment
and End of life vehicle problem.Development.
r Daoud Ait-Kadi is currently a full professor at
mechanical engineering at Laval University in Cana-
da. He received his Bachelor’s degree in mechanical
engineering in 1973, a Master of Science in industrial
engineering in 1980 and a Ph. D. in industrial engi-
neering, operations research and computer science
in 1985. His research interests include operations
the life cycle engineering and reverse logistics. He has authored
papers published in IEEE transactions on reliability, Naval research
logistics, IJPR, IJPE, RESS, EJPR, JQME. He coauthors a textbook on Stochastic processes (2004), a Handbook of maintenance manage-ment and engineering (2009) two other books (Reverse logistics and Minimal repair models) will appear in 2012. Aït-kadi is a resident member of Académie Hassan II des Sciences et techniques of Morocco Kingdom.
r Christian Mascle is a full professor at Department of Mechanical Engineering, École Polytechnique of Montréal. He received his PhD in Microtechnic Engineering from École polytechnique fédérale of Lausanne, his BSc degree in Mechanical Engineering from École Polytechnique of Montréal, and his engineering degree in Microtechnic from
Engineering School of Le Locle (Switzerland). His research interests include assembly and task planning, product life-cycle modeling, design methodology, intelligent CAD, tolerance modeling, and engineering applications of object oriented programming. He is a member of CIROD, CIRAIG, REGAL and OIQ.
refe
renc
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KEYWORDS f white-collar project f earned value f earned-value metrics f cost performance index f CPI f remaining work index f RWI f staffing to schedule Index f StSI
LITERATURE REVIEW
r A B S T R A C T
Past reports have claimed that earned-value metrics are inadequate to manage a dynamic
project. This report shows how a project can use earned-value metrics to respond to the
full range of tests that most projects encounter. The tests assume the project goal is to
finish on time with full scope and correct the staffing (the cost) to achieve success. The
results show how to work through realistic project delays in unplanned-for staffing, and
how the “desert of resources” expands Brooks’ Law that “adding staffing to a late project
only makes it later.”
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WORKING THE “EDUCATED” PLAN:HOW EFFECTIVE IS
CORRECTIVE STAFFING IN A TYPICAL WHITE-COLLAR PROJECT
plan, and then uses that plan to test the responses of a middle-sized, 12-month, white-collar project. The responses will demonstrate that a project can apply earned-value metrics to corrective staffing and deliver the original scope on the original schedule for the most reasonable cost. [5]
1. The basic educated project Planned staffing for the “educated” white-col-
lar project appears in Figure 1. The Written Staff Plan (with a scale from 1 to 24 people per month) indicates the rate at which the staff will work, and the Written Plan to Date (scaled from 0 to 40,000 staff-hours) adds up the staff work into the com-pleted project cost in 12 months. [6]
Figure 1 depicts a typical, 12-month, white-col-lar project with a design-build life cycle: The project staffs up during the early phases, works at a steady 14-person level through the middle of the project, and winds down through the testing and deployment. The project’s staffing will result in the total staff-hours of work that will reach the planned goal on the desired schedule. Each staff person works 160 staff-hours each month. Most
of the activity of the later phases includes the “undiscovered rework” that was highlighted in prior reports.
Prior reports also illustrated how new staff passes through a “rookie learning” period before becoming “full professional contributors.” One study chose a 12-month example of a white-collar project with baseline entry-learning values of a 6-week rookie up-to-speed interval, an average rookie productivity of 50%, and a 6 hour-a-week tutorial from the project professionals, to arrive at a “cost of entry learning” of roughly 8% of the project total cost. [7]
To create an “educated” plan that includes entry learning, we will run a trial project that includes all three entry-learning variables. All project hiring will be driven by project pres-sure working through the system delays in a cause-and-effect systems model. (See Figure 4 for details.)
The trial project begins with 0 rookies and 1 professional (to teach the rookies). As the project hires its staff, the project incorporates entry learning into the plan. When we are done, because the project will have combined earlier re-ports’ undiscovered rework and our entry-learn-
r John NevisonPresident of New Leaf Project Management
Project management literature is full of integrated system models. These mod-els have described useful insights into project behavior and management. [1] Some have detailed how undiscovered rework gradually bloats the scope of the project and overwhelms the initial plan. [2] Others have explored the process of entry learning on a project where the staff passes through the “arriving rookie” pool to the full “professional” pool of team members, able to contribute 100% to the project. [3] Unfortunately, some reports have disparaged earned-value analysis as a failed tool in managing the project. [4]
The present discussion builds on the lessons of these earlier reports, incorpo-rates the effects of undiscovered rework and entry learning into a basic “educated”
“We have met the enemy and he is us.”
Walt Kelly
FIGURE 1. The Written Staff Plan, Written Cost Plan to Date, life-cycle phases, and discovered rework
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LITERATURE REVIEW /// WORKING THE “EDUCATED” PLAN: HOW EFFECTIVE IS CORRECTIVE STAFFING ...
ing variables, we will call our result the “educat-ed” project plan.
The early entry learning activity in Figure 2 includes the effects of the getting up-to-speed interval, of the average rookie productivity, and of taking time to teach those getting up to speed.
Figure 2 shows how the educated project’s Actual Staff rate follows the Written Staff Plan. The entry learning activity occurs in the area between the Actual Staff rate and the Activity Against Plan. The entry learning activity occurs early in the project and lasts until month 7. It has the effect of delaying progress on the project until later than initially planned. After month 4, the project staff adds a few extra staff-hours to make up for the initial learning activities. When the project finishes on time, the staff has worked a total of 22,272 staff-hours (the area under the Actual Staff rate). This figure includes 2,162 staff-hours of entry learning and 20,110 staff-hours of planned project work (the area under the Activity Against Plan rate). The 9.7% cost of learning in this educated project is not too far from the 8% in the example cited in the earlier report.
Figure 3 shows how the project Activity Against Plan rate accumulates as the project’s Earned Value to Date to achieve the project’s final Project Goal (the scope goal), of 20,110 staff-hours, on schedule, at the end of 12 months. The Ac-tual Staff rate feeds the cumulative Actual Cost to Date to arrive at the total cost of 22,272 staff hours. The total cost exceeds the goal by the 2,162 staff hours, the cost of entry learning.
If you return to Figure 1 you will notice that the Written Plan to Date lines up with the Actual Cost to Date, not the Earned Value to Date. This is because Figure 1 shows an adjusted Written Staff Plan, with its details revised so that the Written Plan to Date covers the 2,162 staff-hours of entry learning. After the entry-learning work was cov-ered in the adjusted plan with extra staffing, the adjusted Earned Value to Date in Figure 3 reached the planned project goal of 20,110 staff-hours.
The basic educated project’s Earned Value to Date defines the full project scope goal at the end of 12 months. Figures 1, 2, and 3 show how the integrated educated project activities work when everything goes according to plan.
Cause-and-effect diagram of the basic educated project following the written plan
Figure 4 links the variables that are active in the educated project, based on the written staffing plan. (The system elements enclosed in boxes will be added later when the “educated” project is test-ed.) If you froze the action of this set of variables, you would be able to look at the current size of the Written Staff Plan; the Actual Staff and the Actual Cost to Date; the basic Activity Against Plan and the Earned Value to Date. [8]
Figure 4 maps the causal links between the system variables. Staff members devote time to learning activities and project activities. Activities are accumulated in the Actual Cost to Date and Earned Value to Date. These metrics combine with the Written Staff Plan to create Project Pres-sure on the project staff. [9]
In the educated project, entry Learning Activity increases the Ac-tual Cost to Date (but not the Earned Value to Date). The difference creates a mild positive Project Pressure to make up for the lagging earned value. (Details on the formulas will appear later in the discussion.)
Mild Project Pressure combines with the project’s Written Staff Plan to grow the planned staff along with a few extra part-timers. (See Figure 2.) The staff becomes fully productive over a 6-week, up-to-speed interval. During that interval the rookies incur an average training cost of 6 staff-hours per week from the profes-sionals already on board. To under-stand how this 6-week delay affects the educated project, see Figure 5.
The educated project begins with 0 rookies and 1 professional (to teach the rookies). Following the plan, the project staffs up and, after their up-to-speed interval, the rookies become full-time professionals. The rookie 6-week, up-to-speed delay causes the professionals’ growth to follow 1.5 months behind the rookies.
The Up-to-Speed learning curve
Figures 6 and 7 detail how an individual follows an up-to-speed learning curve.
Figure 6 shows the simplest form of a learning curve, where a new person on the morning of the first day on the project has a productivity of 0 hours/week and at the end of a 6-week, up-to-speed interval has a productivity of 40 hours/week. The individual’s average productivity is 50%. The learning curve is the diago-nal line that measures the increasing percentage of the working hours that are applied to the planned project work. The diagram also contains the equation for the working rate:
<working rate> = 40/6 x <the week number in the up-to-speed interval>
Figure 7 shows how the learning curve can be modified to include a different arrival pool productivity and the professional time-to-teach rate. First, with a more generous
FIGURE 2. The basic project with Written Staff Plan, Actual Staff, and Activity Against Plan
FIGURE 3. Earned Value to Date, Actual Cost to Date, and Project Goal of the basic educated plan
FIGURE 4. Cause-and-effect diagram of the educated project following the written staff plan
FIGURE 5. Educated project's Written Staff Plan, Rookies, Pros, and total Actual Staff on the project
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assumption that the arrival’s average productivity will be 60%, a planned arrival can immediately contribute 20% (8 hours) to the project. This assumption shifts the dotted-line learning curve rate up a bit.
Second, each person is drawing an average of 6 hours a week of time-to-teach from the professionals already on the project. The time-to-teach rate at the end of the up-to-speed interval will be 0, so to have an average of 6 hours in the middle, the time-to-teach rate at the beginning must be 12 hours per week. The solid diagonal line represents an adjust-ed learning curve that begins at -4 hours/week and ascends to 40 hours/week.
When a learning curve begins with a negative rate, it takes a new arrival a while to begin contributing to the project, and then a little while longer until the total contri-bution covers the initial costs. In Figure 7, the new hire’s net contribution turns positive when the rate exceeds 4 hours per week, when the positive triangle of contribution exceeds the negative triangle of cost. The length of time until a new hire “breaks even” and contributes to the project is labeled “Utility Horizon” and is exactly 1.09 weeks. (More on this calculation later.)
The utility horizon has a surprising side effect: it creates a “desert of resources” at the end of the project. Within a week of the end of the project, a new hire cannot learn enough to be helpful to the project. (Unless he or she comes with spe-cific, immediately useful, talents.) This utility horizon will become an important consideration when dealing with the delays in finding and teaching unplanned-for staff.
A variable range’s effect on the whole project
Earlier references, information from several surveys, and New Leaf’s clients’ experience over the past 20 years have led to the current values for a plausible range for each project
variable in the 12-month, white-collar project. [10] Given a plausible range of values for a variable, a natural question is “What effect does a variable’s range have on the whole project’s cost?” And, “How important is this concern to the overall problem of managing the project?”
For the basic, educated project, the total (100%) project baseline cost is 22,272 staff-hours to produce a 20,110 staff-hour scope goal in the required 12 months. The variables that define entry learning are displayed in Figure 8. [11]
The baseline values for these variables were selected to be on the conservative side of the median values of their ranges. [12] Together these three variables define the entry learn-ing for the project. The Up-to-Speed Interval is baselined at 6 weeks and can vary from 2 to 10 weeks, resulting in a project cost spread of 12%. The average Rookie Productivity is baselined at 60% and can vary from 50% to 90%, resulting in a project total cost spread of 7%. The Time-to-Teach Cost Rate is base-lined at 6 hours/week and can vary from 2 to 10 hours, resulting in a project cost spread of 4%.
The Up-to-Speed Interval has the largest cost spread because it has a compounding effect: if the interval is short-er, the individual becomes 100% productive faster and the individual uses less of the professional’s teaching time. So shortening the rookies’ Up-to-Speed Interval saves a lot of entry-learning staff-hours. Increasing the Rookie Productiv-ity and decreasing the Time-to-Teach Cost Rate also saves entry-learning staff-hours.
All three of these variables can lower the total entry learning and allow for earlier Activity Against Plan. The les-son is clear: Before starting a project, understand and reduce your average Up-to-Speed Interval. (For example, by having a clear and complete project plan to show the new arrivals.)
A 1% improvement in our $928,000 educated plan is worth about $9,300 [see note 8]. A simulation not shown
here estimates that improving all three of our entry-learning variables to their low-end values could save us 9.4% of the staff-hours, or about $87,300. (Note that the sum of the three variables’ low-end cost improvements is 13%. However, be-cause the variables all affect the same entry-learn-ing costs, the combined yield is only a 9.4% project improvement.)
The basic educated project: Summary
The educated project plan incorporates impor-tant lessons of past work on projects as systems: both entry learning and undiscovered re-work. The project integrates baseline variable values that are drawn from plausible ranges of values. The project calculates its corrective pressures from familiar earned-value variables. The pro-ject’s behavior makes a strong argument for hiring high-skilled staff and for pre-project training that can shorten up-to-speed times, increase initial rookie productivity, and lessen teaching demands on the working project professionals.
The basic educated project’s Planned Value to Date
The educated project’s basic Activity Against Plan (that adds up as the Earned Value to Date) is pushed later by the early entry learning activities and then compensates later by over-achieving the plan to allow the project to finish on time (see Figure 2).
Because the basic Activity Against Plan pattern shows how the project behaves when it includes entry learning and everything goes according to plan, this pattern accumulated in the Earned Value to Date will become the new reference pattern, the “educated” project’s Planned Value to Date, that we will use when we explore things not going according to plan. This “educated”, planned pattern will be the plan to which we will compare our project performance as we examine how earned-value metrics can
use corrective staffing to respond to sudden and systemic shocks.
(The original Written Staff Plan will continue to be used in our diagrams as a reference pattern, to make it easier to see how other variables are reacting.)
2. The shocked, educated project What happens when the educated project
must make an unexpected, significant adjust-ment? Can we continue to calculate project pres-sure derived from earned-value metrics? Can we get back on track? Can we still reach our project goal on schedule for the lowest possible cost? In order to answer these questions, we must take a detailed look at what happens when we seriously shock the project.
Our focus will be on the whole project react-ing to the worst-case scenario of a systemic shock. This systemic shock extends from the beginning to the end of the project. It could be called a chronic shock. The shock is that a persistent 25% of our effort goes into unplanned-for work, mean-ing that the staff is only 75% productive!
This systemic shock of 25% unplanned-for work could be caused by some all-too-familiar causes: poor requirements definition, bad ini-tial planning, inexperienced staff, challenging technical difficulties, interfering senior managers, hostile sister projects, additional undiscovered rework, or poor scope management.
A 25% systemic shock will require large adjustments to our initial plan and exercise old and new paths in our cause-and-effect model. The shocked project’s behavior may provide valuable lessons for future projects.
Cause-and-effect diagram of the shocked educated project following the plan
In Figure 4, the shocked project’s cause-and-effect diagram splits Project Activity into Ac-
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tivity Against Plan and Unplanned Activity. The Unplanned Activity is where the 25% shock hits the project.
Figure 4 adds Time-to-Find Cost and Time-to-Find Interval considerations for unplanned-for staff. Figure 4 also adds Overtime as a quick-act-ing response to the need for more staff-hours of work. Note that the Planned Value To Date is based on the basic Activity Against Plan, so that the educated project allows for the early delays that Learning Activity causes and will be reacting to the effects of only the Unplanned Activity.
Overall performance
First, we’ll examine how the educated project behaves when it experiences a systemic shock. Figures 9, 10, 11,and 12 show the educated project responding to a 25% systemic shock—that is, one-quarter of its efforts are spent on work that was not in the original plan but for which the project must pay.
Figure 9 begins with the regular rookies and many more pros. The figure also shows the imme-diate effects of increased project pressure to hire planned staff and build a pending pool of staff to feed the unplanned rookies into the project. (More detail on these two new pools will appear below.) Notice that the pool of unplanned rookies looks like it has peaked at 4 months. The number of project pros grows until the work has been caught up, then declines as the project slows its pace, finally dropping below than the original plan.
Figure 10 shows the split between the 75% ac-tivity worked against the plan and the unplanned 25%. By month 6 in the project, as the increased staff’s Activity Against Plan gets fast enough to match the original plan for 14 people, the project
pressure begins its steady decline and the project gradually reduces staff.
Figure 11 shows the workweek with overtime between 40 and 52 hours. The workweek, with only a 1-week delay, increases and decreases with the project pressure. Again, the overtime begins to decrease at the 4-month mark. Also graphed between 0 and 12 hours is the small amount (10%) of unpaid overtime.
Figure 12 shows the project’s Earned Value to Date achieving the project’s final Project Goal (the scope goal) of 20,110 staff-hours, on schedule, at a cost of 31,779 staff-hours (a 43% cost increase over the basic project’s 22,272 staff-hours).
Variable ranges and their effects
Figure 13 details the important set of the shocked model’s variables, their baseline values, their range of values, and the consequent spread of the project costs.
The shocked project begins with the basic educated project baseline values for all the project variables and changes only one value, the System-ic Shock, from 0 to 25%. The 25% Systemic Shock stresses the whole system. The increased cost spreads on the far right of the table show that, when the project is responding to stress, most of the variables have broader effects on cost.
The cost spreads of the first two variables have gone up from the basic educated project to the shocked project: Up-to-Speed Interval from 12% to 14%, and Rookie Productivity from 7% to 14%. This increase in the cost spreads comes from the big increase in required project activity driven by the 25% shock. When the project activity goes up, the difference in costs between the high and low setting of a variable often expands. The shocked
FIGURE 9. Shocked project staffing pools FIGURE 10. Shocked project's Activity Against Plan and Activity Unplanned
FIGURE 11. Shocked project overtime rate FIGURE 12. Shocked project's Earned Value to Date and Actual Cost to Date
FIGURE 13. Variable ranges and shocked project cost spreads
project makes remedial work necessary and can magnify the effect of each variable setting.
Overtime
A 25% shortfall in the project’s planned activ-ities usually results in an immediate increase in overtime for the current project staff. Depending on how fast a project manager can detect the problem and adjust the staff’s workload, the delay can be from 1-4 weeks. The OT Adjustment Inter-val has such a small difference in its range that its delays from 1 to 4 weeks make no significant dif-ference to the project cost spread! (See Figure 13.)
Some of the research on overtime indicates that by restricting the overtime hours to less than 12 hours a week, burnout can be avoided. [13] By adopting this 12-hour limit, the shocked project sought to avoid the stop-and-start dynamics of burnout. The shocked project assumes that a wise manager would detect individuals suffering from chronic overtime burnout and rotate them off the project until they could return refreshed.
As the shocked project’s Project Pressure varies between 1.00 (no change) and 1.99 (99% change), Overtime varies between 0 and 12 hours. The formula is:
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<Average workweek> = 40 + 12 x (Project Pressure – 1), when Project Pressure is between 1 and 2.
Otherwise,
<Average workweek> = 40, when Project Pressure ≤ 1, or 52, when Project Pressure ≥ 2.
Paid versus unpaid overtime
Unfortunately, in many white-collar profes-sions, overtime is considered “part of being a professional” and is not paid for.
In our shocked project, overtime is set to reflect a good project management practice with a paid overtime value of 90% (in an ideal world, it would be 100%). This good management practice will yield project costs that more nearly reflect the true labor costs of the project. The plausible range of values for paid overtime descends from a commendable 90% to a lamentable 10% and leads to a substantial 11% spread in the shocked project’s costs.
Ironically, the bad practice of not paying for overtime often hides another bad practice of allowing overtime burnout to occur. The shocked project baseline values sought to avoid both bad practices. Projects should pay for their overtime and try to avoid staff burnout.
Unplanned-for people
A 25% drop in our productivity creates a need to find and hire extra staff that were not included in the original, educated plan.
The Time-to-Find interval ranges from 2 to 14 weeks. For in-house staff, the range is on the shorter end; for outside hires, on the longer end. The baseline value for the shocked project’s Time-to-Find Interval splits the difference at 8 weeks. [14]
Surprisingly, the range of the Time-to-Find Interval has no significant effect on the shocked project’s cost spread. Because the project imme-diately senses the need for unplanned staff and begins a search to identify them, the Time-to-Find Interval is covered by the scheduled rookies signing on.
Figure 14 shows that unplanned-for staff adds two pools to our learning-working progression.
The first addition is the pending pool with an average length (delay) of 8 weeks. The second is a separate learning pool for unplanned staff. This separate pool allows us to assign different
rates for a planned-for staff person and an un-planned-for staff person.
People entering and leaving the project
Figure 14 also allows you to see how staff members might enter or leave the project. When people are needed, the first action is to sign-on the planned staff that is immediately available because they are part of the plan. (This is how the basic plan signed on the planned-for staff.) If unplanned staff members are also needed, the second step is to find them by identifying a pool of applicants for the pending pool. When it becomes possible after a time-to-find delay, the third step is to sign on unplanned staff from the pending pool.
White-collar projects, large and small, all seem able to remove a person from the project payroll in about 2 weeks. Sometimes, 2 weeks is the time required to fire someone; sometimes, it is the time to move a valuable staff talent to a different project. [15]
Unplanned Up-to-Speed Learning Curve
The shocked project distinguishes between planned and unplanned staff with the Unplanned-to-planned factor. When the factor is 1.0, both planned and unplanned staff members have the same learning curve; when it’s 1.10 or 1.20, the learning curves differ. (For more on the Un-planned-to-planned factor, see the Planned versus unplanned section below.) Figure 15 shows that, in the baseline case illustrated, the factor is 1.0 and the unplanned up-to-speed interval is 6 weeks, with the average productivity of 60%, the same as the planned values in Figure 7.
However, Figure 15 also shows that the average unplanned staff member has a much longer road to travel before joining the ranks of the project professionals. His or her Up-to-Speed Interval of 6 weeks is preceded by a Time-to-Find Interval of 8 weeks.
Figure 15 includes a new cost, the 22 hours of labor to find a new person, the Labor-to-Find. A plausible range for this variable (from client data) extends from 15 to 30 hours and leads to a project cost spread of 12% (see Figure 13).
Figure 15 also illustrates that when the 22 staff-hours of labor to find each additional person gets included, Net Hours Worked turns positive at 3.05 weeks and the Utility Horizon (also known as the desert of resources) grows to 8 + 3.05 = 11.05 weeks (or 2.76 months). [16] So a better, low-cost process to sign up pending, unplanned-for staff could significantly reduce total project costs.
FIGURE 14. Four pools with three delays
The desert of resources
The 2.76-month Utility Horizon stretches over 20% of the project’s total duration and creates a desert of resources where it no longer makes sense to hire staff because they will fail to contribute to the project! (See Figure 16.) The only exception to this rule would be a particular person with special talents and a much faster than average up-to-speed time.
It’s as if the project was a wagon train journey from St. Louis to Los Angeles and the final stage was across Death Valley. When crossing the desert, you don’t want to bring on any newcomers who don’t have their own water.
So projects should limit late-project hiring to those contributors with immediate, special talents. When senior management floods a late project with mediocre talent, the project professionals often ignore them. “If management assigns us people late in the project,” one project veteran asserted, “we put them off in a corner reading novels. If we tried to use them, they would only get in the way.” [17]
Fred Brooks, the author of The Mythical Man Month, proposed in Brooks’ Law that “Adding man-power to a late software project only makes it later.” The desert of resources means that this law can take effect well before the project becomes late! Brooks himself suggested that his rule of thumb was caused in part by the late arrivals’ entry learning. [18]
Staff hiring ahead
If, at the end of the project, a late arrival will only harm the project’s productivity, it might make sense to hire ahead, before the desert of resources, so that when you arrive at the edge of the desert,
FIGURE 15. Unplanned Up-to-Speed Learning Curve
FIGURE 16. The desert of resources at the end of the project
your staffing is already adjusted for the end of the project. When the project-staffing plan was set to look ahead the full 2.8-month distance, the resulting pro-ject costs were less because the project used overtime right away, staffed up more aggressively, and reached a smoothed, steady-state much earlier. This pattern occurred with hiring ahead for either a full desert or a one-half desert. In both cases, the total project cost was 94% of the 100% baseline, for a 6% cost spread. Again, aggressive early hiring (if the staff can be put to work) can help reduce costs.
Planned versus unplanned staff
It seems reasonable to assume that, while every rookie is qualified to work on the project, the produc-tivity and skill of the project’s planned staff might be a little better than the project’s unplanned staff. The
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shocked project provides three settings, one neutral and two adjustments, for unplanned staff. The adjustments assume that the unplanned-for staff may take little longer to get up to speed and have a lower average productivity while getting up to speed. Both are driven by an Unplanned-to-planned factor that can be 1.00 (no difference), 1.10 (10% difference), or 1.20 (20% difference):
<Up-to-speed of an unplanned rookie> =
<Up-to-speed of a planned rookie> x
<Unplanned-to-planned factor>
<Productivity of an unplanned rookie> =
<Productivity of planned rookie> / <Unplanned-to-planned factor>
The project baseline value is 1.00, with a variable range from 1.0 to 1.2 and a project cost shift of 7%. (See Figure 15.)
Earned-value metrics
The project pressures that drive corrective actions begin with the fundamental metric of earned-value analysis, the Cost Performance Index (CPI). The CPI is the ratio of the completed planned work, Earned Value, to the actual work expended, Actual Cost.
<Cost Performance Index> =
<Earned Value> / <Actual Cost>
Project pressures are the inverse of the indexes, for example:<Cost Pressure> = 1/CPI
The project pressures are:ff Exactly 1.0 when project activity should not change,
ff Above 1.0 when project activity should increase, and
ff Below 1.0 when project activity should decrease.
Two newer information indexes are included in the pressure calculations: the Remaining Work Index (RWI), and the Staffing-to-Schedule Index (StSI). These indexes are reasonable extensions of the familiar earned-value indexes and behave in a fashion similar to the CPI. Their defining formulas are:
Remaining Work Index (RWI)
RWI = <Remaining work planned> /
<Remaining work actual>
= (<Total Planned Value> – <Planned Value to date>) /
(<Total Planned Value> – <Earned Value to date>)
Staffing-to-Schedule Index (StSI)StSI = CPI x RWI
The formula for Project Pressure is: 1/StSI. [19].
Other earned-value variables
Other earned-value metrics that were explored but set aside include Cost Variance, Schedule Variance, the Sched-ule Performance Index (SPI), the Air Force Index, and the To Complete Performance Index (TCPI). The indexes that proved useful were the Cost Performance Index, the Re-maining Work Index, and the Staffing-to-Schedule Index. [20] The earned-value pressure allowed the project to re-spond successfully to a full range of project shocks.
Monitor Frequency
In most white-collar projects, information on the pro-ject’s progress is monitored in weekly or biweekly meetings. In the shocked project, the Monitor Frequency ranges from immediate, to biweekly, to once a month. The baseline value for Monitor Frequency is immediate. The re-plan range leads to a project cost spread of only 2%. (See Figure 13.) Other combinations of systems variables (not shown here) had a Monitor Frequency cost spread of 0%, so the project team can manage the project confidently with weekly, biweekly, or monthly frequency.
Project phases and up-to-speed time
Many white-collar projects get more complicated as they get into their later phases. Team interactions are complex and the half-built product can be challenging. Testing and rework may add additional uncertainty to the project. The complexity of later phases leads many project managers to increase their estimates of the up-to-speed interval for the later phase arrivals. That increase in the overall up-to-speed interval also expands the desert of resources at the project’s end.
The baseline, up-to-speed interval was a No Phase, flat 6 weeks (1.5 months). In Figure 17 the other two phase-effect charts are a Mild Phase-effect with an average the same as the No phase, and a Higher Phase-effect chart that begins with the flat rate and goes up.
The range of values for the phase-effect up-to-speeds lead to a huge project cost spread of 18%. The Mild Phase has the lowest project cost percentage of 94% because it has the lowest values for the early phases of the project, when most of the up-to-speed entry learning is occurring. The Higher Phase is at a project cost percentage of 112%, reflecting the early higher up-to-speed times (see Figure 15).
The project’s total cost spread was 18%. Also, the utility interval (and the corresponding desert of resources) crept up from 2.76 to 3.18 months, an increase of 1.7 weeks.
We see that, within a phase-driven Up-to-Speed Interval, the most important time is early in the project where the majority of the learning occurs. We also remember that the
desert of resources may prevent adding staff in the later project phases.
3. Other project shocksFigure 18 shows three possible shocks to the
project: Scope Creep, Sudden Shocks, and Sys-temic Shock.
A 40% systemic shock
Our 25% Systemic Shock resulted in a project cost of 143% of our educated baseline. If our sys-temic shock is 40%, the project cost is 193%. This almost doubling in size is consistent with other projects’ reported experience. [21] The project’s dynamic pattern of behavior was similar in all
respects to the 25% systemic shock. The project smoothly concluded on goal and on time. So the earned-value metrics work on a project up to the point where a complete re-plan would be neces-sary.
Persistent scope creep
A very common project concern is the well-known scope creep. If the scope change is an odd one-time event, then its effect is covered in the analysis (below) of a 25% sudden shock to a part of the project. However, sometimes scope creep manifests itself as a persistent pressure where every project stakeholder seems to want some-thing extra. How much they get depends on the scope control process of the particular project. Persistent scope creep’s effect on the project is
FIGURE 17. Up-to-speed time by project phase, 3 cases
FIGURE 18. Shocks to the basic educated project
FIGURE 19. Earned value growing to hit goal affected with 16.5% annual scope creep
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relatively simple: it increases the planned work required to reach the final Project Goal.
A plausible industry report estimated that two-year white-collar projects contained, on average, about 33% scope creep. [22] The range for the annual shock of scope creep in the educated project is: none, 16.5%, and 33%. (The third value is double the industry estimate.)
Figure 19 clearly shows how the persistent scope creep tilts the project goal line upward at a steady rate throughout the project. The Scope Creep numbers, while large, are easy to under-stand as a persistent change in the project goal. Because the earned value and the actual cost are so closely related, it is no surprise that a 16.5% increase in the project scope results in a 17% increase in the project cost (see Figure 18).
Once again, the earned-value metrics smooth-ly adjust the project pressure to the systemic shock of the steadily changing target and arrive at the goal on schedule.
A four-month, 25% sudden shock
What about a shock that is not systemic, that is sudden? What happens when a 25% sudden shock occurs at different times during the project year? “In the first 4 months with start-up going on?” “In the middle 4 months when the project is fully staffed?” “In the last 4 months when the project plan is winding down and the desert of resources looms?” How will the earned-value metrics respond to a sudden shock?
First-third sudden shock
When the 25% sudden shock of unplanned work hits at the beginning, the project behaves for the first four months exactly like the fully shocked project. However, at the end of the fourth month, the staff abruptly recovers its ability to produce planned activity. Productivity goes from 75% to 100%. The needed staff drops precipitously, thanks to the short 2-week delay in de-staffing, and the project again ends a bit ahead of schedule. The 25%-unplanned work has resulted in a project cost of only 117% of the basic educated project because it only lasted 4 months.
Second-third sudden shock
When the 25% sudden shock of unplanned work hits the project in month 5, the amount of planned activity abruptly drops (see Figure 20). The drop in Activity Against Plan increases project pressure and leads to a steady increase in the actual staffing. In 2 months the rate of the project work has recovered. In 4 months the total amount of work has almost recovered. Then the sudden resumption of the planned activity boosts the rate so much that some staff can be promptly laid off (with a 2-week delay). Because of the late mid-project jump in the staff and the work, the project actually concludes a week ahead of sched-ule. The mid-project 25%-extra work resulted in a total project cost of 117% of the basic educated project (coincidentally, the same as the first-third 25% shock).
FIGURE 20. 25% Sudden Shock of unplanned activity in months 5-8 (inclusive)
Final-third sudden shock
When the 25% sudden shock of unplanned work hits the project just as it is beginning to wind down and conclude, the staff stops declining along the written staff line and stays high to fill in the necessary work (see Figure 21). The Total Activity goes up because of Overtime. The project ends on schedule. The 9-12 month, 25%-ex-tra work has resulted in a project cost of 110% of the basic educated plan. (Cost was only 110% because the workers were all professionals, working at 100% productivity).
The project recovered graceful-ly using earned-value metrics ver-sus all three 25%-sudden shocks. The projects ended on time, or a little early, with their fully com-pleted scope.
4. Conclusion: The challenge of theeducated project
The educated project has an-swered the question posed in the title of this discussion: Corrective staffing can be highly effective in a white-collar project. Yet in the real world, white-collar projects con-tinue to get into trouble much of the time. Why is corrective staff-ing so unsuccessful in practice?
The two big differences be-tween the white-collar real world and our educated project are: real projects cannot find the skilled staff required to do the work and real projects are not permitted to hire the full staff required to get out of trouble.
In one study of 300 project managers, the top 5 ranked diffi-culties were:1. Not enough resources are assigned
4. Senior management fails to establish a clear goal
5. We fail to adequately plan [23]
Number 5 is the only item over which a project manager has full control. Project managers have a responsibility to resist items 1 through 4 but they cannot overcome them on their own. Managers require the support of the larger organ-ization.
One project manager who specializes in bailing out troubled projects con-fessed his “project turn-around” secret, “When senior management asks me to rescue a project, I make it a condition of my help that they give me the number of people I say I need. The truth is, I usually find that number from a look at the original plan (which senior management had never adequately funded). Most of my projects have been successfully res-cued by employing the correctly planned staff.” [24]
When a project manager begins with the proper organizational support and constructs an adequate, “educated” plan, the project can expect to apply earned-value metrics to corrective staff-ing and deliver the original scope on the original schedule for the lowest possible cost.
Notes
1. Some earlier system models with real world examples are featured in Roberts (1964), Powell (1987), Abdel-Hamid (1989), Cooper (1993 and 1994), and Nevison (1994). Cooper’s work includes many other applications of a systems model to the “real world.”
2. For details on the undiscovered rework cycle see especially Cooper (1993 and 1994). Cooper observes that many of his clients’ projects have had late surprises caused by “undiscovered rework” in tasks thought completed. On the other hand, research by Christiansen and others find projects with relatively stable Cost Performance Indexes after the first 20% of the project had unfolded. See Christiansen (1993, 1999, and especially, 1992).
3. Two Nevison articles (Project Management Journal and PMNETwork, June 1994) examine entry-level learning along with the results of a white-collar professional survey on projects.
4. In Cooper (1993 and 1994), earned-value tools are discounted as “looking backwards” and ignoring the many feedback activities in real projects.
5. This paper assumes that a shock to the project can be fixed by adjusting staffing levels. Responding to a shock by adjusting the project’s scope is discussed in other sources (Nevison 2014).
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6. One study by Nevison (2000) scales common white-collar projects at 8 months, another study averaged 21 months (Nevison, 1992). Jones (1991) and Cooper (2004) refer to projects with durations of well over a year. The 12-month white-collar project size maintains continuity with the project in Nevison, (Project Management Journal, 1994), as well as staying in general agreement with other surveys. This paper’s charts were made with the modeling tool iThink from isee Systems.
7. Nevison (Project Management Journal, June 1994) cited this example of entry-learning cost. The example in this paper is scaled to match the one discussed previously, with an additional 2% added for industry-wide turnover.
8. This white-collar project can be modeled using different units. Assuming an $80,000 annual salary for the average project staff member, the Actual Cost of 22,272 staff-hours could be $928,000. Planned value and earned value can be either 20,110 staff-hours or deliverable units such as 25,000 lines of code. Because staff-hours is the resource every project manager must manage, this discussion uses staff-hours.
9. A staff member or full-time equivalent (FTE) works 160 hours per month (when there is no overtime). This work accumulates in the “to-date” figures of the Planned Value to Date, Actual Cost to Date, and Earned Value to Date.
10. Nevison (1992) gives detailed distributions for many of the variables cited in the present paper.
11. The project size can be expressed in several different units. The middle-sized project is: 12 months of duration, 22,272 staff-hours of work, 139.2 staff-months of work, 14 full-time people at its largest staffing, and, at $80,000 per person, an in-house budget of $928,000. When performed for another business, the same project might be priced between $2.32M and $2.78M to cover the organizational overhead.
12. Up-to-Speed Interval and Time-to-Teach Cost Rate have ranges that come from Nevison (1992). The average Rookie Productivity must be between 50% and 100%. None of the values are particularly surprising: they agree with 25 years of New Leaf client experiences. Any of the ranges could be adjusted to reflect the
realities of different business environments.
13. For additional details on how 16 managers rated burnout, see Nevison (1992 and 1997). Other details on overtime can be found at Business Roundtable (1980), Cooper (1994), Department of the Army (1979), Department of Labor, and Jensen, et. al. (1997)
14. One project reported a figure of 40 weeks to find an unplanned-for staff person (see Nevison, 1992).
15. One surprising constant in U.S. white-collar projects is the 2-week removal time. This figure comes from 16 respondents (Nevison, 1992). It has been substantiated in New Leaf client experiences over the past 25 years.
16. To arrive at the 3.05 utility horizon, solve for where the Net Hours Worked equals 0, where the individual begins to be a net contributor to the project. The Net Hours Worked is the integral of the Work Rate.
17. Private communication from a very senior project engineer, Joe Gwinn.
18. See Brooks (1995).
19. See Nevison (“RWI and StSI,” June 2003) and Nevison (2013) for a careful discussion of these indexes and their handy uses.
20. For details on the Air Force multiplier, see Fleming (2010); for the StSI see Nevison (2003).
21. See, for example, (Cooper, 2004).
22. See Jones (1991).
23. When 300 U.S. managers were asked what factors caused problems on their projects, the most common answer was “inadequate resources.” (See Taylor, 1998.) In a New Leaf Project Management study, 278 respondents doing project work in ten companies said that only “seldom” was it true that “In our organization we have an adequate number of people to work on our current projects.” (See Nevison, 2000).
24. Confidential communication from a New Leaf client.
authors
r John Nevison President of New Leaf Project Management, is the author of six books and numerous articles on computing and management. His first simulation model was published in 1974. During the course of his business career, Nevison has built and sold two businesses, managed projects, managed project managers, and served as both an internal and external consultant to Fortune 100 companies. He is past president of the Mass Bay Chapter of the Project Manage-ment Institute (PMI®) and a past president of the Greater Boston Chapter of the Association for Computing Machinery (ACM). He is the author of the on-line learning sequence entitled QPM (for Quantitative Project Management). For more information see the website at newleafpm.com, or write Nevison at [email protected] or contact him 978-369-9009.
Abdel-Hamid, Tarek K. (1984) The Dynamics of Software Development Project Management: An Integrative System Dynamics Perspective. Massa-chusetts Institute of Technology Doctoral Thesis.
Abdel-Hamid, Tarek K. (December 1989) “Lessons Learned from Modeling the Dynamics of Software Development.” Communications of the ACM, Vol. 32, No. 12: pp. 1426-1438.
Brooks, Frederick P. (1995) The Mythical Man-Month, Anniversary Edition. Reading, MA: Addison Wes-ley Publishing Company.
The Business Roundtable (1980) Scheduled overtime effort on construction projects — A construction industry cost effectiveness task force report. New York.
Christensen, David S., & Kirk Payne (April 1992) “Cost Performance Index Stability: Fact or Fiction?” Journal of Parametrics 10.
Christensen, David S. (1993) “Determining an Accu-rate Estimate at Completion.” National Contract Management Journal 25.
Christensen, David S. (Summer 1999) “Using the Earned Value Cost Management Report to Evaluate the Contractor’s Estimate at Completion.” Acquisi-tion Review Quarterly.
Cooper, Kenneth G. (March 1993) The Rework Cycle: Benchmarks for the Project Manager. Project Man-agement Journal, Volume XXIV, No. 1: pp. 17-21.
Cooper, Kenneth G. (March 1994) “The $2,000 Hour: How Managers Influence Project Performance Through the Rework Cycle.” Project Management Journal, Volume XXV, No. 1: pp. 11-24.
Cooper, Kenneth G., & K. S. Reichelt (3-10 October 2002) “Quantifying Disruption: Rigorous Analy-sis of a Challenging Problem.” San Antonio, TX: Project Management Institute Annual Conference. 12 pages.
Cooper, Kenneth G. (11-14, July 2004) “Toward a Uni-fying Theory for Compounding and Cumulative Impacts of Project Risks and Changes.” Proceed-ings of the PMI Research Conference. 10 pages.
Department of the Army, Office of the Chief of Engineers (1979) Modification impact evaluation guide. Washington, D.C.
Department of Labor, Bureau of Labor Statistics (1947) Bulletin #917. Washington, D.C.: Govern-ment Printing Office.
Fleming, Quentin W. and Joel M. Koppelman (2010). Earned Value Project Management, Fourth Edition. Newtown Square, PA: Project Management Insti-tute (PMI).
Jensen, Jr., Don, John D. Murphy, Jr., and James Craig (March 1997) “The Seven Legal Elements Necessary for Successful Claim for a Constructive
Acceleration.” Project Management Journal: pp. 32-44.
Jones, Capers (1991) Applied Software Measurement: Assuring Productivity and Quality. New York, NY: McGraw Hill.
Jones, Capers (1991) Applied Software Measurement: The Software Industry Starts to Mature. Burling-ton, MA: Software Productivity Research, Inc.
Nevison, John M. (March 1992) White Collar Project Management Questionnaire Report. Internal Working Paper. Concord, MA: New Leaf Project Management.
Nevison, John M. (June 1994) “Up To Speed: The Cost of Learning On a White-Collar Project.” Project Management Journal, Volume XXV, No. 2: pp. 11-15.
Nevison, John M. (June 1994) “What Can we Learn About Learning on Projects?” PMNETwork: pp. 6-8.
Nevison, John M. (December 1997) Overtime Hours: The Rule of Fifty. Internal Working Paper. Concord, MA: New Leaf Project Management.
Nevison, John M. (February 2000) Snapshot Assess-ment Data. Internal Analysis. Concord, MA: New Leaf Project Management.
Nevison, John M. (June 2003) The Remaining Work Index (RWI) and the Staffing to Schedule Index (StSI). Internal Working Paper. Concord, MA: New Leaf Project Management.
Nevison, John M. (June 2009) Earned-value Bench-marks for Re-baselining a Project. Internal Working Paper. Concord, MA: New Leaf Project Management.
Nevison, John M. (June 2013) StSI and RbSI Com-pared. Internal Working Paper. Concord, MA: New Leaf Project Management.
Nevison, John M. (December 2014) The Reduced Scope Index (RScI): How to estimate your adjusted scope as you finish your project on time and on budget. Internal Working Paper. Concord, MA: New Leaf Project Management.
Private communications from current practitioner Joe Gwinn.
Powell, F.D. (1987) Study of a Software Development Process Dynamic Model. Bedford, MA: Mitre Cor-poration. MTR 10314.
------------- (2013) The Guide to the Project Manage-ment Body of Knowledge, 5th Edition. Newtown Square, PA: Project Management Institute (PMI).
Roberts, E. B. (1964) The Dynamics of Research and Development. New York, NY: Harper and Row.
Taylor, James (1998) A Survival Guide for Project Managers. New York, NY: Amacon
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KEYWORDS f international development projects f governance f flexibility
QUALITATIVE AND EXTENSIVE LITERATURE REVIEW
r A B S T R A C T
Although International Development Projects (IDPs) remain important instruments for activating and achieving
sectoral and national development in the developing world, they often fall short of making their desired impact
because they are implemented under challenging conditions with rigid procedures. This paper illustrates that
flexibility is critical to the success of IDPs as it improves their effectiveness. It contributes to literature on IDPs
and flexibility and is thus beneficial to IDP professionals, development organizations and the International
Development Body of Knowledge.
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GOVERNANCE OF TOMORROW’S INTERNATIONAL DEVELOPMENT
PROJECTS (IDPS):
ment such development interventions, all in attempts to ensure that they achieve their set objectives.
That notwithstanding, the record of development projects in the developing world has not being good - most of them simply fall far short of delivering their intended outputs and/or benefits in spite of their planning and management as well as several years of both individual and collective experience in managing projects. The Abyei Development Project in Sudan, having fallen far short of its objectives – both original and amended, was recommended for termination, and terminated it was (Barclay et. al, 1981); the Kpong Irrigation Project (KIP) in Ghana was terminated in 2004 after a schedule overrun of more than 90 months; and more recently, the Inland Valleys Rice Development Project (IVRDP) in Ghana was terminated in 2011 with many uncompleted civil works. In fact, one only has to do a cursory search to come across numerous examples of such projects that have failed. For some that succeed, their benefits are usually temporary and narrowly distributed (Rondinelli, 1979).
Owing to the nature of International Develop-ment Projects (IDPs), the difficult and unpredictable environment within which they are implemented and their path of identification through to im-plementation, they are almost always challenged. Moreover, the basics of interaction between financ-ing institutions and the host government of IDPs make it difficult to apply good project management practices (Youker, 1999). These render the problems associated with managing IDPs such as unrealistic time-frames and budgets, scope changes, technically deficient designs, lack of appropriate and essential human and institutional capacities, to mention but a few, intractable.
Project planning, no matter how detailed it is, is done based on limited available information which
increases as the project progresses. IDPs turn out to be more complex than conventional projects with close interconnecting activities where a decision to undertake successive activities largely depends on the outcome of preceding ones. Projects are unique undertakings and as Andersen (1996) indicates, the natural implication of uniqueness is the impossibil-ity to know all the activities required for a project to succeed at the initial planning stage. This very uniqueness is the characteristic that underpins the application of good project management principles in IDPs. Although there are instances where projects turn out to be complete failures due to their inabil-ity to produce actual benefits to the customer after being executed as planned, on time and on budget and achieve planned performance goals (Dvir et. al, 2003) the original plan, in too many IDPs, remains unchanged. This has become a common pitfall of IDPs.
This paper concludes that a flexible approach which allows for creative responses to opportunities, rather than rigid procedures, is critical to the gov-ernance of IDPs. By governance, the writer is refer-ring to their method of management. Thus, govern-ance and management may be used interchangeably in the paper. This paper is beneficial to IDP profes-sionals, development organizations and the Interna-tional Development Project Body of Knowledge.
1. Analysis of international development projects (IDPs)
By International Development Projects (IDPs), this paper is talking about government projects financed by institutions such as the World Bank; the
r Lawrence G. BoakyeProject Management Program, School of Civil Engineering, The University of Sydney, Australia
The financing and implementation of development activities through physical, economic and social investment projects has been an integral part of public planning and management in the develop-ing world for a long time, and thus national ministries, international lending institutions and private corporations have used, and continue to use project management as a means of planning and exe-cuting billions of dollars of investments to stimulate economic growth in developing countries since World War II (Rondinelli, 1979). Procedures have evolved to ensure that such development projects are planned in detail; covenants, conditions precedent and procurement regulations continue to be insert-ed into legal contracts to compel acceptable behaviour (Strachan, 1978). The logical framework, which is hard to use within today’s project management framework and integrate with other project manage-ment tools as a result of a few pitfalls (Couillard et. al, 2009), continues to be used to plan and imple-
*Paper approved for 2nd International Conference in Project Management at UQTR (May, 2015)
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Inter-American, African, Asian and Caribbean Development Banks; the Islamic Development Bank; and European Development Banks (Youker, 1999). They are public sector development pro-jects or programs which are specifically designed for the economic and social needs of developing countries and are usually financed by a donor (Ahsan & Gunawan, 2010). Such projects are ei-ther implemented by recipient governments under a bilateral agreement with the donor country, or through an “implementing partner” of the donor which is frequently a Non-Governmental Organ-ization (NGO) or professional contractor (Craw-ford & Bryce, 2003). IDPs are important instru-ments for initiating and attaining both national and sectoral development. Billions of dollars are available each year via donor countries, devel-opment banks and international institutions for developmental purposes in the developing world. Their importance to the developing world cannot be over-emphasized. For instance, in the mid-2000s, activities of the Ghana Pover-ty Reduction Project, an African Development Bank (AfDB)-funded project led to an increase in
the household income of pineapple growers on the project from GHs17.60/month to GHs95.00/month (AfDB, 2006a). And more recently, activi-ties of the Livestock Development Project (LDP), implemented in Ghana between 2003 and 2011, led to an increase in the average annual gross revenue per smallholder farmer from a baseline of GHs25,939.94 to GHs49,700.00 and from GHs1,144.41 to GHs3,420.00 for cattle and sheep farmers respectively (AfDBa).
IDPs differ from conventional projects as a result of their unique characteristics. Due to the cross-functional nature of project activities, projects often typically comprise a degree of com-plexity that is not found within other functional departments (Kharbanda & Pinto, 1996). Howev-er, IDPs are found to be more complex than other type of projects because they are implemented in highly difficult and unpredictable environments where, as Youker (2003) indicates, there is often a lack of basic infrastructure and all resources are in short supply. Then again, they are mostly not-for-profit with an involvement of several multiple stakeholders. Language barriers, cross-cultural
gaps and geographical distances among the stake-holders may hamper their smooth implementa-tion (Freedman & Katz, 2007). Their process of identification and development is often solely carried out by the donor or financing institution, resulting in local stakeholders feeling left out (Youker, 1999).
They are somewhat experimental and thus even seemingly routine replications are likely to meet unanticipated difficulties when transferred from one cultural setting to another (Rondinelli, 1979). Although there are some hard elements within IDPs, they are frequently concerned with soft issues like social or human development (Crawford & Bryce, 2003). More and more IDPs have turned out to be soft type projects involving social services dealing with people, versus con-struction in sectors such as education and even revising government pension programmes (Youk-er, 1999). The soft objectives of these projects are usually less visible and measurable compared to industrial or commercial projects (Ahsan & Gunawan, 2010). IDPs have thus turned out to be difficult projects to manage (Youker, 1999). They have also been found to be difficult to plan which is evident in technically deficient designs, scope changes as well as cost and time overruns, often reported as some of the major pitfalls of IDPs. This difficulty in managing them is aggravated by the fact that:
ff There is a lack of appropriate and essential human and institutional capacities in developing countries for their management.
ff It is impossible to anticipate all activities required for an IDP to succeed during planning.
ff During their governance, a decision to undertake an activity largely depends on the outcome of a preceding activity or activities.
The life cycle, stages linking the start to the end, of IDPs consists of a number of progressive phases that lead, from the identification of needs and objectives through the planning and imple-mentation of activities in order to address these needs and objectives, to the assessment of the outcomes (Biggs & Smith, 2003). Baum (1978) in-troduced a specific six progressive-phase life cycle of IDPs (Figure 1). The majority of development agencies such as European Commission (EC), Canadian International Development Agency (CIDA) and Australian Agency for International Development (AusAID) have a project cycle of five or six phases, very similar to Baum’s but with differences in content and in the names of the phases (Golini & Landoni, 2013).
The Logical Framework Approach (LFA) is typically used to manage IDPs. It is a tool for planning programmes and projects in the broad-er context of development goals which consists of a four-by-four matrix summarising the most important aspects of a project/programme under consideration (Baur, 2001). Its four columns are usually Narrative Summary, Objectively Verifiable Indicators, Means of Verification and Assump-tions and the four rows/lines consist of Goal, Pur-pose, Outputs and Inputs (Couillard et. al, 2009).
The LFA is now considered inflexible, complex and difficult to integrate with other project man-agement tools due to the lack of a clear process leading to its development, its confusing nature is evident in the difference between goal and purpose and a lack of stakeholders’ involvement which often compromise its validity [(Couillard et. al, 2009); (Coleman, 1987); (Solem, 1987)]. As a result, updated tools such as the Logical Framework Approach - Millennium [see (Couillard et. al, 2009)] have been proposed. Development agencies such as United States Agency for International Devel-opment (USAID) and CIDA also no longer use it (Golini & Landoni, 2013).
As illustrated above and as Youker (2003) indicates, IDPs are different from other types of projects for many reasons and thus the approach to their implementation must also be different. There is therefore the need, not for rigid imple-mentation procedures for their governance, but rather a flexible approach which will allow for creative responses to opportunities that might not have been anticipated during the identification and development process.
2. Research methodThe methodology similar to that of Olsson
(2004) was employed for this research. This paper is primarily based on secondary data. Findings and conclusion are based on an extensive review of Project Completion Reports (PCRs) and Project Evaluation Reports (PERs) of AfDB-funded pro-jects across various sectors in Ghana, archived and available on the Bank’s website for public ac-cess. Archived project reports are credible sources for research as the data sourced from them are more objective than primary survey data because they are free from contamination by respondent perceptions and/or memories of the phenomenon of interest (Calantone & Vickery, 2009).
The findings and conclusion are also based on extant literature on flexibility and influenced by
FIGURE 1. Project Cycle of International Development Projects (Baum, 1978)
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one of the authors’ personal experience working on IDPs as well as observing how they are imple-mented in Ghana. The paper is mainly qualitative in nature.
3. An overview of flexibilityFlexibility can be said to be the ability to
adapt investment decisions, including timing and scale, to existing market conditions as opposed to pre-set assumptions and goals (http://www.businessdictionary.com) or the capacity to adapt in simpler terms (Golden & Powell, 2000). It may also be described as a way of making irreversible decisions more reversible or postponing irrevers-ible decisions until more information is available (N. O. Olsson, 2004).
Flexibility approach could be of two forms in a project – process flexibility and product flexi-bility. Process flexibility, which is associated with adaptability in decision making in projects, is a means of responding to uncertainty. An exam-ple is the “last responsive moment” approach as illustrated by Ballard & Howell (2003) where de-cisions are not taken until the very last responsive moment. Product flexibility, on the other hand, is associated with adaptability in the use of pro-ject deliverables. According to Golden & Powell (2000), the literature proposes numerous stand-points from which to measure flexibility with four metrics viz. efficiency, responsiveness, versatility and robustness emerging.
4. Findings and discussionAll IDPs are somewhat experimental and even
such seemingly routine replications often meet unanticipated difficulties when transferred from one cultural setting to another (Rondinelli, 1979). As such, as indicated by Youker (1999), although good project management if started early in the project development process could solve most of the problems associated with IDPs, it is difficult to do so owing to the basics of the interaction between the financing institution and the host government – the process of identification and development is often solely done by the financing institution. These have led and continue to lead to one common recurring IDP pitfall which is long lead time to get the project rolled out. For example, it took the KIP, the Small Scale Irriga-tion Development Project (SSIDP) and the IVRDP,
all of which were implemented in Ghana, 55, 40 and 37 months respectively to get started after approval [see (AfDB, 2005); (AfDBc); (AfDBb)]. This long lead time leads IDPs with no option other than an update of the project concept and design before implementation which is almost never done. Another effect of the basics of interaction is the implementation problems synonymous to IDP governance which arise because different people, other than those who design and plan the projects, end up implementing the projects. These, as well as other problems are compounded by the dynamic and unpredictable nature of the environment as well as the lack of appropriate and essential human & institutional capacities in project management in the developing world.
Project planning provides structure, reduc-es uncertainty and increases the likelihood of success (Dvir et. al, 2003) but the chances of realizing a plan without amendments decrease with increasing time horizon (Olsson, 2006). It is also virtually impossible to anticipate all activities required for an IDP to succeed during planning. Moreover, IDPs tend to have several closely linked phases (e.g. construction works are linked to train-ing, formation of users associations and provision of inputs and credit) with a decision to undertake a successive activity often dependent on the out-come of the preceding one. These have rendered most IDPs less effective. At least, AfDB’s projects have been found to be less effective as they are good at delivering outputs but weak in translating the outputs into outcomes and impact (AfDB, 2011), which explains the call for modifying the existing Project Management Body of Knowl-edge (PMBoK) in the management of IDPs [see, for example, (Do Ba & Tun Lin, 2008)]. Flexibility is primarily an approach to improve the effective-ness of projects and is thus the factor that could fit well in the effectiveness of IDPs (Shahu et. al, 2012). Shahu et al. (2012) conducted an empirical study on flexibility as a critical success factor for projects and found that the cost of its application is much lower than the cost of managing unex-pected changes in the course of project delivery. That same study revealed that projects which had a scope of flexibility in process, decision making, design, etc. showed higher levels of success rates as compared to those with rigid systems. They therefore concluded that its application could be seen as a value addition to projects through an improvement of the overall project effectiveness and beneficiary satisfaction. This explains the de-sire of project owners and users to have “room for manoeuvring” so as to be able to adjust projects as
they gain knowledge about their needs and changes in the project context (Midler, 1995).
A review of reports of AfDB’s IDPs in Ghana offers some clear insights on the need for a flexible approach to managing IDPs. The review identifies the lack of project flexibility as a major cause of failure for the Bank’s projects. One report indicates inflexible and cumbersome procedures as major sources of implementation delays (AfDB, 2011) with an informal note (AfDB, 2006b) indicating projects should ensure more inbuilt flexibility during imple-mentation for satisfactory outcome. The LDP by ex-ercising flexible decision making approach during project implementation, minimised losses through a change from a conventional cash credit scheme to a credit-in-kind scheme using small ruminants when it discovered that the recovery rate for the disbursed loans under the cash credit scheme was low (AfDBa). Similarly, the Second Line of Credit to Agricultural Development Bank (AgDB), disbursed in the form of a project to boost overall agricultural production in Ghana, succeeded in attaining its objectives with a flexible approach. The PCR (AfDB, 1997) states that flexibility which “allowed the Afri-can Development Fund (ADF) to enable the AgDB to revise the list of goods and services in line with the actual demand for credit was an important factor for the achievement of the objectives of the project.” (p. 17).
A classical case of the need for a flexible ap-proach in IDPs can be seen from the KIP which was considered a failed operation and terminated by the AfDB after several years of implementation only to get its fortunes turned around by a private compa-ny (AfDB, 2005) through product flexibility. Thus says the report:
“In Ghana, the transformation of a
failed operation (KIP) into a success
story through the use of infrastructure
for a high-value crop by a private
company illustrates the need for
the Bank to have a more open and
flexible approach on the finality
of the infrastructure.” (Page 18)
A flexible managerial approach is not a new concept as Olsson (2004) reports. Several examples of flexibility as a readiness approach to the effects of uncertainty in planning have being identified by researchers such as Sager (1990). In spite of the use-fulness of flexibility in improving project effective-
ness, it seems to be a paradox that mainstream pro-ject management focuses on stability for the project whilst major parts of other management disciplines strongly emphasise flexibility (Olsson, 2004). It is traditionally described as undesirable in project management context (Shahu et. al, 2012). The case against flexibility stems from project efficiency. The argument is that once a project has been decided upon and the planning and execution has begun, changes will not only generate disagreements between the different project actors but it will often reduce the project’s efficiency (Olsson, 2004). This case clearly neglects the projects’ effectiveness aspect. However, the traditional focus on stability in project management becomes challenged under uncertainty (Kreiner, 1995) which calls for the need of flexibility. There is therefore a dilemma in its application as a result of these arguments. But of what use is an efficiently delivered project which is rendered effective because it cannot make the desired impact or produce the desired revenue?
5. ConclusionProjects will remain the dominant means of
organizing investment in the foreseeable future because they offer important advantages (Rondinel-li, 1979). IDPs will therefore continue to be a major way of activating and attaining development in the developing world irrespective of the numerous challenges associated with their governance and their continuously reported failures. That notwith-standing, owing to their path of identification and development as well as the usage of the deficient logical framework for their planning and manage-ment, IDPs will continue to be difficult and chal-lenge endeavours undertaken in a developing world characterized by a lack of adequate and diminish-ing resources. A likely effect of this is a continuous failure of these projects in the foreseeable future. Failure in itself is good in that we learn by failing. However, the cost of learning from the failure of development projects is painfully high. And thus, one inexpensive way of learning how best to man-age IDPs is through studies of this nature.
IDPs are again complex activities with higher levels of uncertainty and are thus beset with several problems during their management. It is virtually impossible to anticipate all the required activities necessary to enable them to succeed. There is also no guarantee that all planned activities will be ex-ecuted to the latter during implementation. And as indicated by Siffin (1979), a development project is not like a train trip to a ticketed destination; rather
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Strachan, H. W. (1978). Side-effects of planning in the aid con-trol system. World Development, 6(4), 467-478. doi: http://dx.doi.org/10.1016/0305-750X(78)90096-7
Youker, R. (1999). Executive Point of View: Managing Inter-national Development Projects-Lessons Learned. Project Management Journal, 30(2), 6.
Youker, R. (2003). The nature of international development projects. Paper presented at the PMI Conference, Balti-more, MD.
refe
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authors
r Li Liu PhD, is Senior Lecturer in Project Management at the University of Sydney (USYD). He has his PhD degree from the Australian Graduate School of Management (AGSM). He also has a Master’s degree in Taxation from USYD, a MBA, and a BE. Dr. Liu has one decade of working experience in sys-tems engineering, project management, business research, management consulting, and managing an e-commerce company. He has been published in many international journals including: IEEE Trans-actions on Engineering Management, Project Management Journal, Journal of Information Technol-ogy (JIT), International Journal of Project Management, and Construction Management & Economics
(CME). Dr. Liu has presented at INSEAD and a number of international conferences, including Project Management Institute (PMI®) Research Conferences and the International Conference for Information Systems. He has served as a reviewer for a number of journals and conferences, including MIS Quarterly, JIT, Construction Management & Economics, International Conference for Information Systems, European Conference for Information Systems (ECIS), and the PMI Research Conference. Dr. Liu has been serving as a senior editor for Journal of Information Technology. His research focuses on the performance of infrastructure projects, organizational project management, organi-zational learning in project organizations, IT/IS project management, organizational control theory, and project/program governance. Development.
r Lawrence Boakye holds a BSc (Hons) Degree in Agriculture from the Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana. Having worked with the Inland Valleys Rice De-velopment Project (a defunct African Development Bank-funded project) and having had brief stints, during his undergraduate studies, with the Brong Ahafo Region Development Programme of Action-Aid Ghana as well as the Kintampo-South District and the Fanteakwa District Area Development Programmes of World Vision Ghana, he has quite an appreciable level of experience in development work. He is currently in his final year of his MPhil candidature with the Project Management Program at The University of Sydney, Australia on the Australia Awards Scholarship programme and seeks a
scholarship to pursue doctorate studies. His research interests are in International Development Projects (IDPs) and why they fail. His goal is to contribute to the interchange of ideas between academia and the development project world so that academic research can have practical outcomes, thereby helping the developing world adopt good managerial approaches in the delivery of development projects.
it is more like sailing on a ship, hopefully beyond the point where the internal rate of return becomes favourable, in the direction of a better and more generously endowed climate. There is thus the need for modifications to be made to suit prevailing conditions as they progress and more information becomes available. This paper has illustrated that the one factor suitable for such a modification is flexibility. It is thus illuminating and provides a basis to generate further research. The paper is beneficial to IDP pro-fessionals, development organizations and the International Development Project Body of Knowledge.
AfDBa. Ghana: Livestock Development Project-Pro-ject Completion Report.
AfDBb. Inland Valleys Rice Development Project-Pro-ject Completion Report.
AfDBc. Small Scale Irrigation Development Pro-ject-Project Completion Report.
AfDB. (1997). Ghana: Second Line of Credit to the Ag-ricultural Development Bank-Project Completion Report
AfDB. (2006b). Informal Note on PPER Summary of Four Agriculture Sector Projects (O. E. D. (OPEV), Trans.).
AfDB. (2011). Agricultural Water Management: An Eval-uation of the African Development Bank’s Assistance in Ghana and Mali, 1990-2010.
Ahsan, K., & Gunawan, I. (2010). Analysis of cost and schedule performance of international development projects. International Journal of Project Manage-ment, 28(1), 68-78. doi: http://dx.doi.org/10.1016/j.ijproman.2009.03.005
Andersen, E. S. (1996). Warning: activity planning is haz-ardous to your project’s health! International Journal of Project Management, 14(2), 89-94. doi: http://dx.doi.org/10.1016/0263-7863(95)00056-9
Ballard, G., & Howell, G. (2003). Lean project manage-ment. Building Research & Information, 31(2), 119-133. doi: 10.1080/09613210301997
KEYWORDS f innovation f competitiveness f design f benchmarking f indicator
ACTION RESEARCH
r A B S T R A C T
This article brings a strategy designed to set guidelines to increase competitiveness in the Brazilian industry. In
order to achieve the proposed goal, this work has conducted the diagnosis of the design process in five compa-
nies that develop consumer goods. The diagnosis listed critical success factors for effective innovation manage-
ment, which were essential for implementing mechanisms oriented to the development of innovative solutions
for products and services. In addition, an indicator system was developed to assess the competitiveness of each
company. To date, it was found that the required efforts to increase competitiveness through innovative product
design management must have a well-defined strategy of integration between processes and intervening agents
before the start of the project.
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INTRODUCTION
In an increasingly competitive and turbu-lent context, design has assumed a position of importance, since through it a company can strategically position or reposition a product or service within a determined market (Steinbock, 2005). Design can be considered as inducing activity of incremental and radical innovation processes in different organizations (Stamm, 2008). However, to be used in a strategic way, design should be integrated into the compa-ny’s managerial processes in such a way as to effectively participate in the strategic decisions of an organization. Integration demands that a company suitably manages the design (Borja de Mozota, 2003).
Due to the importance of design within the economy, diverse national and international agencies have sought to introduce it into their investment priorities. Some countries have sought to carry out diverse actions promot-ing design as a means for the innovation and differentiation of their services and industrial products, obtaining positive results most of the time. In this context, in research carried out through case studies of small UK prod-uct development companies (Margaret Bruce, Rachel Cooper, & Delia Vazquez, 1999), it was found that there is a consensus on the benefits of design for increasing the companies’ com-petitiveness. According to Rosane Fonseca de Freitas Martins (2004, p. 3), “the UK Design Council has also carried out research studies that utilized relatively rigid comparative stand-ards (by range of performance measures) among businesses with and without concentration on design. Those that presented results significant-ly better are the businesses with a concentra-tion on design.”
In Brazil, in recent years, incentives have been carried out through the decrease of taxes paid by the consumers of determined prod-ucts, as a way to increase the consumption and productivity of some sectors of the Brazilian economy. But these measures, in addition to being seasonal, have been incipient in the status quo maintenance of an efficient design and innovation process. This can be explained due
to the fact that these measures incentivized consumption and not the development of goods that radically innovate the market. These most recent, besides enabling companies to become more competitive, open space for generating value in terms of knowledge, research, and science oriented towards technological develop-ment (Mascitelli, 1999).
With the intention of reverting this scenar-io, since the second semester of 2012, a project is being developed entitled ICD (Innovation, Competitiveness, and Design), with the objec-tive of seeking out guidelines for increasing the competitiveness of Brazilian product devel-opment companies through product design management and innovative services. With this project the intention is to capacitate, struc-ture and provide method standards in which product development companies innovate the market. In addition to this, the project seeks to develop a benchmarking system which gives visibility to Brazilian companies and encourag-es them in the search for good practices of de-sign and innovation. In this sense, the objective of this article is to present the strategy that was conceived to define guidelines that intend to in-crease the industrial competitiveness in Brazil.
1. Design, Success, and Competitiveness
Design creates value because it improves product image, or in other words, external appearance, and with this, the perceived quality of the product (Borja de Mozota, 2003). With this view, the design is seen as a “plus”, a “some-thing more” of a product or service perceived by a specific user. In addition to this, the design increases the quality of the product in terms of performance, efficiency, functionality, and originality (Brown, 2009). In other words, it provokes differentiation among products.
However, the competitive advantage doesn’t come only from the differentiation of a product. The advantage is also the result of coordination
r Maurício BernardesFederal University of Rio Grande do Sul, Brazil
INCREASE COMPETITIVENESS IN THE BRAZILIAN INDUSTRY THROUGH
INNOVATIVE PRODUCT DESIGN MANAGEMENT
*Paper approved for 2nd International Conference in Project Management at UQTR (May, 2015)
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improvement among the various functions of the company (Porter, 1998). This is observed in a study developed by Borja de Mozota (2003), carried out with small and medium European companies. It confirmed the existence of an innovation policy within these companies as essential to their survival. This innovation policy should have clear operational goals that facilitate their integration with the other existing functions in the company.
Tatiana Schoneweg Mello (2003), pg. 75, states that “the Design Man-agement shows to have as a main function, the articulation of infor-mation via methods that facilitate the integration and the interaction of different areas, minimizing errors, risks, and uncertainties, and in this way, helping the viability and the concretization of an initial idea”. Meanwhile, so that designers can be inserted into the strategic man-agement of a company, first they must contribute to the design of the organization itself, helping its manag-ers to identify existing gaps between
company strategy and organizational design (Ron Sanchez, 2006). One way to facilitate the insertion is to uniformize concepts practiced by both professionals: the strategic management of the company and the design management. This demonstrates the importance of the proposition of structured methods, procedures, and stand-ards, that clarify the roles of the diverse entities involved in the product development process.
There are various research studies that corroborate with the presented context. Ahn, Zwikael, & Bednarek (2010) developed a multi-disciplinary model to differentiate, prioritize, and select investments in technological pro-jects within the portfolio of an organization. The results of applying the model suggested that it is possible to increase the competitive-ness of technology based companies, although the study was not carried out through action research, opening up the possibility of ques-tioning its validity. Artto, Kulvik, Poskela, & Turkulainen (2011) discuss the role and the im-portance of project management offices in the managerial integration of projects concerning
innovation. This way, according to the authors, establishing the clear functions of a project office is essential to leading a well-structured innovation process. This data is also corrob-orated by Mir & Pinnington (2014). These au-thors identified that the performance efficiency of the project management process is related to the success of the project itself. Gallego, Rubal-caba, & Hipp (2013) discuss how innovation in services supports organizational innovation, through a conceptual framework that propos-es to increase the competitive advantage of companies. Wong & Chin (2007) propose that a company can be more competitive through a better managerial process of organizational innovation. They prove this through the pres-entation of a framework concerning innovation management. However, the inexistence of a structuration of design methods in the present-ed cases is perceived in the proposed research studies. Robin Roy & Johan Riedel (1997) pro-pose that design has an important role in the innovation process of tangible products, and
FIGURE 1. ICD Research Design
FIGURE 2. Quad helix
FIGURE 3. Project development plan: collection, analysis, and implementation.
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this relates positively with the competitiveness of the organizations.
2. Research MethodsThe project was conceived in such a way as
to make possible the development of scientific works in the areas of design and innovation, in companies that develop consumer goods in Brazil. This way, it was sought to structure it with the support of four fundamental pillars (figure 1): partnerships with other Brazilian Universities; partnerships with product devel-opment companies; free lectures and colloqui-ums of visiting professors from Foreign Univer-sities, and publications of the project results. These four pillars were fundamental to gaining visibility for the project. This was possible with the implementation of free events with well-known professors from foreign universities, as well as through the publications based on the results of the developed research. The partner-
ship with Brazilian Universities and product development companies improved the connec-tions with professionals from diverse areas and academia, increasing significantly the coverage of the project.
To facilitate the communication process of the project, as well as connections among professionals and academics, it was decided to use the quadruple helix scheme, integrating the University’s initiative with companies, govern-ment, and non-governmental organizations (figure 2). This way, through monthly strategic meetings, the main results and work develop-ments were discussed with representatives of each of these entities.
Action research was used as the main re-search strategy. To propose the guidelines for increasing Brazil’s industrial competitiveness, the understanding of the context of the product development companies, defining the project to a non-probabilistic sample of consumer goods manufacturing companies, was initially decid-ed. In this way, five companies from different performance areas were sought to work with,
FIGURE 4. Events developed during the action research
FIGURE 5. Distribution of development team and project management
FIGURE 6. Research paper integration
Team Mem-ber
Research Description Status* Need**
PhD1 Managerial framework Managerial framework oriented to the development of innovative product and services
ID BP
PhD2 Benchmark systemComputation system for the benchmarking process of product developers
ID BP
PhD3 Business gamesDevelopment of business games to ease the implementation pro-cess of design mechanisms
ID BP
MSc1 Design diagnosis Design process diagnosis of studied businesses C BP
MSc2 Indicator system Innovation, competition and design indicator system C BP
MSc3 Design mechanismsDevelopment of converging factors between theory and practice through the employment of design mechanisms
C BP
MSc4Creativity in shape and function
Correlation between creativity in shape and function of successful consumption goods in the market
C DP
MSc5 Environment to innovate Analysis of proper environment for innovation TD DP
MSc6Indicator system implemen-tation
Innovation, competition and design indicator system implementa-tion in product developers
ID BP
MSc7Criteria do select design strategic projects
Establishing criteria for the selection of design strategic projects ID DP
MSc8Project management in design offices
Analysis of project management process in design offices TD DP
* C – Completed; ID – In development; TD – To be developed. ** BP – Before the start of the ICD project; DP – During the ICD project.
TABLE 1. Description of research developed and under development
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having entered into the study for convenience and avail-ability, since these companies had already participated in previous partnerships with the University of the authors of this article.
The studied companies operate in the following segments: footwear, cleaning tools, hand tools, plastic containers, and toys. All are large sized companies and export their products to different countries of the world, and are notably recognized in the Brazilian market for being competitive and innovative. To understand the con-text of these companies, case studies about the role and function of design in the product development process (PDP) of the studied companies were carried out. Figure 3 shows the planning of the developed activities. In this way, the design processes of the partner companies were graphically modeled through the creation of focal groups with their teams of product development. These teams had, at minimum, a representative of the design, mar-keting and engineering sectors. The modeling occurred through the creation of four focal groups in the studied companies, which lasted around three hours each. Post-its and bond paper were used to carry out the modeling and indicate the form in which the PDP was developed. The modeling stage was developed between September of 2012 and April of 2013. The results of the diagnosis of the design process were presented to the partner companies in May of 2013.
FIGURE 7. Keywords related to design, according to the studied companies (Bruna Ruschel Moreira, 2014)
FIGURE 8. Scheme proposed by Kumar (2012) and used to guide intervention in the design process
FIGURE 9. Guide to innovation and its application
FIGURE 10. Categories that make up the system of innovation, competitiveness and design indicators
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Next, it was sought to understand how the top managers of the studied companies understood the relation between design and innovation. This second part of the diagnosis was carried out between June and November of 2013, through the realization of semi-structured interviews with the president, commercial director, and industrial director of each partner company, totaling 15 interviews, with approximately 30 hours of recordings. Through content analysis, the critical factors of success were identified, for the incorporation of design mecha-nisms for the companies’ PDP, as well as elements that restrict-ed the innovative potential of the participating companies. The result of this final part of the diagnosis was presented indi-vidually by each participating company between December of 2013 and January of 2014, in three-hour meetings, for the top managers of the companies.
The diagnosis listed critical success factors for the manage-ment of efficient innovation within the companies that covered the areas of strategy, communication, projects, processes, and human resources. The critical factors identified were essential to implement mechanisms concerning the development of innovative product solutions and services in the studied com-panies. As for mechanisms, it is an understood set of methods, techniques, and tools of design, and was defined as structured methods of design. The diagnosis enabled the development and presentation of an action plan to work with the design mecha-nisms to make this last process more efficient and effective.
To implement the mechanisms of the studied companies, project multifunctional operational teams were designated, formed by at least one product designer, one administrator with marketing emphasis, and one production engineer. The leader of each team was the staff member who had already co-ordinated the product development department of the partici-pating companies.
The design mechanism implementation proposal was presented to the top managers (CEO) of the studied compa-nies, and implemented through 09 modular workshops and 05 monitored workshops carried out at the participating com-panies (figure 4). The five companies participated together in the workshops and their objective was the capacity and imple-mentation of the abovementioned mechanisms in the com-panies. The monitored workshops had the help of professors from the area of design at the Universities. Harvard, Berkeley, Lisbon, Saragossa, and Delft. The workshops were concluded in October of 2014, and each participating company presented a solution that encompassed both a product, as well as a service connected to it. The realization of these events enabled collect-
ing data for the construction of a managerial framework orientated towards product develop-ment and innovative services. This framework was developed between January and September of 2015.
In parallel, a system of indicators was devel-oped, which enabled investigating the compet-itive levels of each company in the following categories: response to the consumer, innova-tion, efficiency, finance, and result. For the de-velopment of the indicator system, an extensive literature review was carried out, in search of possible metrics that could be used in the pro-posed system. An experiment was then carried out, with the staff from the marketing, design, and engineering sectors of the five participating companies of the project, with the intention of establishing consensual indicator parameters chosen that allowed evaluating the competitive levels of these companies. The chosen indica-tors were validated in three focal groups with directors of the strategic, financial, and com-mercial areas of the studied companies. The system of indicators will be implemented in a computational system of benchmarking that will be validated with other companies in 2015. The system should provide the feeding and ret-ro-feeding of data directly by product develop-ment companies as a form of assisting them to identify, in real-time, their competitive position in the market. In addition to this, it will be possible to register the best practices that are being employed by the participating companies, in order to facilitate the learning processes of these companies.
3. Development team and project management
Figure 5 shows the distribution of the management team and the development of the project, which is managed by a professor with PhD in Civil Engineering Project Management and Post-doctorate in Design, along with two professors with PhD in Production Engineering and a professor with PhD in Architecture.
The project development team also includes three PhD students, one with Master’s degree in Civil Engineering, another with Master’s degree in Design specializing in Business Games and the third with Master’s degree in Strategic De-sign. The team also includes eight Master’s stu-dents (one business administrator, a production engineer, an electrical engineer, four product designers and a fashion designer). A commercial director of one of the companies involved, and a specialist in the area of industrial development of the Federal Government and the technical director of a non-governmental organization with activities focused on the dissemination of design in Brazil.
Figure 6 shows the integration strategy for studies that make up the larger project. The body of research works, which were and are being developed, was assumed to enable the construction of guidelines necessary to increase Brazil’s industrial competitiveness. It starts with the view that work experience can serve as reference to companies that did not participate in the project. These companies will have free access to publications resulting from the com-pletion of each research work. They will allow other companies in the Brazilian industry to replicate the work. In addition, the pursuit for work integration and outline gives a synergis-tic effect on the operational discussions of the working team.
Table 1 shows the description of each individ-ual research project. The research works of the larger project were identified through discus-sions among the members of the project team. Part of these works was developed because one or another member of the work team pointed out some evidence and analysis.
4. Results and DiscussionsThe method called graph of initial opportu-
nities (Kumar, 2012) was chosen to facilitate the analysis of the large amount of data obtained during the diagnosis of the design process. The application of this method generated two key dimensions, considered to be relevant for the
EQUATION 1. Composite Indicator ICD
TABLE 2. Indicators by category
CATEGORY 1: CONSUMER RESPONSE
ff Sales of new products in relation to existing ones
ff Estimated market share
ff Percentage of products that received complaints
ff Variation on the number of the website views
ff Repurchase indicator
ff Indicator of new clients per year compared to the total number of clients
ff Percentage of net profit obtained with the sale of new products
CATEGORY 2: EFFICIENCY
ff Percentage of projects implemented at the estimated time
ff Percentage of products delivered at the estimated time
ff Materials waste
ff Percentage of projects at the estimated budget
ff Percentage of closed projects
ff Occupancy rate of production
CATEGORY 3: INNOVATION
ff Percentage of radical innovation projects
ff Percentage of new product patents
ff Investment in r & d over the net profit
ff Net profit obtained with new products over the total net profit
CATEGORY 4: QUALITY
ff Rework hours over hours worked
ff Index returns
ff Variation of the rejection index
ff Percentage of the checklist attended
ff Frequency rate of accidents
ff Hours of training in production
CATEGORY 5: RESULT
ff Variation of net profit
ff Return on investment
ff Profit margin
ff Net profit per employee
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analysis. The graph was formed by two oppos-ing axes (Figure 7). The horizontal axis depicted the strategic and operational dimensions for interpreting the data. The vertical axis repre-sented the dimensions of tangible and intan-gible elements. Thus, this diagram allowed the creation of four scenarios (strategic tangible, strategic intangible, operational tangible and strategic tangible).
When analyzing Figure 7, each quadrant is observed to include concepts associated by the companies participating in the project with the term Design. The (tangible and intangible) operating axis was most commonly mentioned by the companies. This can be explained by the fact that the studied companies have related the concept of design more directly to the charac-teristics of products than to the elements that strategically support the efficient and effective performance of the product development pro-cess. Moreover, the least mentioned of all was the strategic axis. A fact that agrees with the idea that, for the participating companies, the concept of design is more related to operating activities than to strategic activities.
The data collected in the diagnosing phases allowed to determine critical success factors (CSF) for the repositioning of design activities in a strategic context (Moreira & Bernardes, 2014). These factors were divided into six pillars that are characterized as deficient foci: Com-munication, Knowledge, Processes, Projects, Human Resources and Strategy. Every pillar relates to their respective evidences and conse-quences for the companies, as described below (Moreira & Bernardes, 2014):
ff Communication: associated with the need to clarify which roles are assigned to each employee, as well as the systematization, formalization and management of flows involving the product development of businesses. Deficiency evidences refer to difficulties in meeting deadlines; lack of awareness on inputs and outputs of the design process; slow troubleshooting and solution; and significant interference of senior managers in design operating activities.
ff Knowledge: refers to aspects related to the theoretical deflection of employees from the department of product development with
regard to their concepts of innovation, causing divergence and conflicts, as well as indifference and insecurity among the forefront staff of the design process to propose new ideas.
ff Processes: related to the evidence that the design activities are operationally positioned in the studied companies. In this case, the design process is not explored at a strategic level in the businesses, but instead, at an operational level.
ff Projects: since the design process is more oriented towards operational activities, projects end up missing key elements of successful solutions that are generated and released to the market. This was particularly perceived through the significant investment in incremental innovations and in new projects with no prior and clear identification of the new experiences consumers might have.
ff Human resources: linked to the lack of career plan formalization and, above all, correlated with the lack of incentives for the development of radical innovation proposals. There were also no clear investment programs in knowledge management in the participating companies.
ff Strategy: there was the development of products that mostly exploit the potential of equipment and machinery of the industrial park without applying structured methods to enable a clear identification of customer latent wishes.
After the diagnosis result, the planning of the implementation of design mechanisms was started. These mechanisms were implemented to enable the development of the design process in a structured manner. The pursuit for struc-turing it was particularly relevant, because it allowed the standardization of the method of application in all participating companies.
As described in the research method of this article, the model proposed by Kumar (2012) was chosen to be applied. It presents the design process in seven modes (Figure 8): sense intent, know context, know people, frame insights, explore concepts, frame solutions, carry out offerings.
Considering that there is a great number of methods and methodologies in design (Design Council, 2007; Burdek, 2005; Bonsiepe, 1975; IDEO, 2003; Patnaik & Becker, 2010; Baxter, 1995; Ulrich & Eppinger, 2011; Otto & Wood,
2000; Cross, 2008; Pugh, 1991), the proposal of Kumar (2012) seemed reasonable, because it is not linear, thus corresponding to the intrinsic thought of design (Brown, 2009; Lockwood, 2010; Farrell & Hooker, 2013), besides present-ing a set of structured tools to be used, with practical suggestions for their application.
Kumar (2012) was translated from English into Portuguese by the research group that made up the project management and devel-opment team. This was important in order to standardize communication, and also to reduce staff complaints about wrong translations in the companies. Thus, a box named Innovation Guide was created, and inside it the proposed mechanisms were included in the form of cards. Therefore, each mode of Kumar (2012) was presented in a specific workshop, in which each participating company chose a set of tools that, according to their opinion, would be more easily applicable to their reality.
After choosing the methods, each partici-pating company had three weeks to present the results of their application (Figure 9).
The innovation guide application results will be assessed through the application of a system of innovation, competitiveness and design indi-cators (Figure 10), proposed by Natália Debeluck Plentz, Maurício Moreira and Silva Bernardes, & Paula Görgen Radici Fraga (2015). The indica-tor system followed the categorization of Hill & Jones (2012). However, indicators for each cat-egory were chosen by the product development teams of the companies studied. The selection used the list of indicators presented in Paulo Roberto Nicoletti Dziobczenski (2012).
The system consists of 27 indicators, some referred to and studied in the literature review and others that were proposed in this project. There are 12 mandatory indicators and 15 optional indicators, and of these fifteen, eight should be chosen by the company that will ap-ply the system to reach a total of 20 indicators needed for application. The system includes, thus, 20 indicators, since it has been assumed that a greater amount could hinder their imple-mentation in the companies.
Because there were five categories, there was a need to have a composite indicator to allow the overall identification of the level of innova-tion, competitiveness and design of each com-
pany. Therefore, a composite indicator called ICD (equation 1) was proposed.
To form the composite indicator, a scoring system was assigned, which varies according to the indicator itself. The researchers preferred to have the same scoring system for most of the indicators to facilitate their use. It should be noted that, in most of them, the higher the value the better the performance. But there are cases where low scores indicate a good result on the indicator. Each indicator receives a score from zero to five according to their value. The categories consist of four indicators, and can collect up to 20 points (four indicators that may total a maximum of five points each). The five categories add up to a total score of 100, which is the highest score that the company can receive in composite indicator of innovation, competitiveness and design. Table 2 shows the indicators identified in each category.
The indicator system is adjusted through the use of focus groups conducted with three senior managers of the companies studied, along with three researchers from the same University as the authors of this article. By December 2014, five focus groups were conducted, aimed at cre-ating small changes in classifications, method of calculation and allocation of scores for each indicator. Other two focus groups are sched-uled to occur from January to February 2015, and are intended to assess issues related to the planning and implementation of the system, to make it easier.
Data needed to calculate the indicators will be collected between March and August 2015, in order to indicate the status of innovation, competitiveness and design of the studied companies; to determine whether adjustments to the indicators and their implementation are possible; and allow the analysis of the impact of the implementation on design mechanisms in the companies studied.
The ICD indicator system is the basis for programming a computer benchmarking sys-tem, whose development began in September 2014, with a view to enable the recording, anal-ysis and view of the evolution of ICD indicators in companies that develop products. The aim is to complete programming and validation in companies from similar industries of those companies studied, by August 2015. Thus,
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authors
r Maurício Moreira is associate professor at Federal University of Rio Grande do Sul (UFRGS), Brazil. He holds a bachelor’s degree in Civil Engineer-ing with M.Sc. and Dr. in
Construction Management and Postdoc in Design by the IIT Institute of Design/Chicago. His research areas include project and design management, design methods and product development process. He is the vice director of Product Development Center of the School of Engineering at UFRGS. He is also the project coordinator of the ICD Project that aims at enhancing the competitiveness of the Brazilian indus-try through the conception of innovative solutions and design management.
r Geísa Oliveira is currently doing a doctorate in Design at Federal Uni-versity of Rio Grande do Sul (UFRGS), Brazil, where she is assistant professor. She hold’s a bachelor’s degree
in Civil Engineering with M.Sc. in Civil Con-struction. Her research interests aims at developing a benchmarking computation-al system in order to evaluate the levels of innovation, competitiveness and design of the Brazilian industry.
r Júlio van der Linden is adjunct professor at Feder-al University of Rio Grande do Sul (Porto Alegre, Brazil) working with undergrad-uate and graduate design programs in the fields of
Design Methodology, Design Theory and Human Factors. He has bachelor’s degree is in Product Design and, after working in different sectors, he got Master’s and Dr degree in Production Engineering. Nowadays, his research interests are related with two questions: “how product designers work in professional practice?” and “how can we improve micro and small enterprises’ competitiveness by means of innovation through design?”.
through the government agency that has been a partner to this project (Figure 2), we intend to disseminate it in the Brazilian market. The system will be distributed with no charge to companies that develop consumer goods, and it is believed that in 2016, Brazil will have a reliable database regarding the competitive lev-el, in terms of innovation and design, of these companies.
5. Final RemarksThis paper presented actions currently being
developed in a large research project in Brazil, with the aim of increasing the competitiveness of the Brazilian industry. This is pursued through design methods structuring initiatives, oriented to innovation in companies that develop products, together with standard forms of performance measurement.
Up to the present, it was found that the required efforts to increase competitiveness through innovative product design man-agement must have an integration strategy between processes and well defined actors involved before the project starts. The commu-nication process has shown to be essential, so that everyone involved can have a clear idea of intentions, deadlines and goals to be achieved. Process integration and communication were thus essential to facilitate the coordination of
design resources geared towards the devel-opment of innovative solutions in products and services. It showed that, even in complex projects with companies from different sec-tors pursuing similar objectives, increased competitiveness and collaborative work has its advantages. The different views and exchange of experiences for the development of products with the help of the methods provided greater involvement and commitment of members, for the implementation of the proposed mecha-nisms.
Acknowledgments
The authors of this article would like to thank Coordination for the Improvement of Higher Education Personnel (CAPES), CNPq (National Council for Research and Develop-ment), the SCIT (Secretariat for Science, Innova-tion and Technological Development of the State Government of Rio Grande do Sul), FAPERGS (Foundation for Research Support of Rio Grande do Sul) and ABDI (Brazilian Agency for Indus-trial Development) for providing support for this project. We would also like to thank the participating companies, which allowed explor-ing the topic in the industrial business environ-ment of product development.
KEYWORDS f project management education f project value f strategic project management f tactical project management
LITERATURE REVIEW
r A B S T R A C T
Modern project management approaches focus on maximizing the value realized from
projects; yet, many project management textbooks and courses do not incorporate this
aspect into their curriculum. This paper reports on our experience in developing and
teaching project management courses that integrate the element of maximizing project
value in their syllabi. The suggested teaching approach uses traditional and innovative
models to teach students how strategic and tactical project decisions should be made.
Students are introduced to models that support strategic decisions such as matching a
project objective to a business case, deciding on a product configuration, and developing
a project plan. They also learn about tactical decisions such as ones that are made during
a project’s execution and control, which are also important for realizing all the project
benefits. Thereafter, students use an innovative model that links strategic and tactical
decisions, implemented in a simulation tool, to experience the various tradeoffs that
affect project value. Based on teachers’ and students’ evaluations, we recommend using
the suggested approach in project management education.
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VALUE-FOCUSED MODELS
classes: strategic and tactical. Strategic factors, such as defining the project objective and goals, getting manage-ment support and preparing the project plan (all important components of value management, Zwikael and Smyrk, 2011 pp. 181), should be pursued in the project’s early stages. The remaining seven factors are tactical and pertain to the ability to carry out the project according to its plan; they are mostly relevant during the project’s execution. The authors found that strategic factors are more important than tactical ones, especially during early project stages,
after which the importance of tactical factors increases.
Samset (2009) strengthened the conclusions of Slevin and Pinto (1987) about the increased importance of strategic decisions compared to tactical decisions by analyzing an on-shore torpedo battery building project that was completed as planned (i.e., project execution was successful) but closed soon after since it was obvious that the concept of stationary torpedo batteries is obsolete, and no enemy would ex-pose its forces to these batteries—thus the project will never generate value.
Clearly, the torpedo battery project closed because its owners thought that there is no linkage between what they perceive as value and the actual output. The decision making process that selected the stationary battery config-uration, if performed, was deficient. Nelson (2005) denoted such a situation a “failed success”.
The present article uses recent work that suggests improvements to project management education (e.g., Van-houcke, 2014) as a starting point. The focus of our teaching approach is on maximizing a project’s value, thereby
r Izack CohenTechnion-Israel Institute of Technology Industrial Engineering and Management Lecturer
Modern project management approaches such as the increasingly popular Lean Project Management (hereafter, Lean; Womack and Jones, 2010; Ballard and Howell, 2003), and Benefit Management (Zwikael and Smyrk, 2011) aspire to maximize a project’s value to its stakeholders (hereafter the term value is also used as synonymous to benefit). Still, project management education mostly relies on traditional models (e.g., the critical path method and resource constrained project scheduling), which only con-sider time and cost aspects, without
taking performance and value into consideration.
Value management focuses on strategic decisions such as defining a project objective, selecting a product configuration, and preparing a project plan. Serrador and Turner (2014) found that while meeting time and cost goals is important for project success, there are other important factors contrib-uting to project success, presumably, project value.
The literature about value manage-ment is traced back to Slevin and Pinto (1987). Based on 418 projects, they composed a list of 10 success factors that were classified into one of two
IN PROJECT MANAGEMENT TEACHINGbringing it in line with modern project management approaches.
A desired teaching approach is one that will lead to good strategic and tactical decisions in future projects. Yet, what constitutes a good decision? Furthermore, how can we teach our students to make such decisions (see the discussion in Cohen, 2008, pp. 101-102)? One answer is to examine a decision’s outcomes, but such an an-swer is too simplistic in the context of teaching since it provides answers only in hindsight. Moreover, a decision that
generates good outcomes after a year can lead to poor outcomes after five years, and a decision could lead to good outcomes only under specific realiza-tion of future events or to poor out-comes, through no fault of the decision maker (Clemen, 1996, pp. 3-10).
Consequently, we follow Cohen (2008) who argued that the quality of the decision making process is a primary parameter in determining if a decision is good. In the context of teaching for successful project management, this means a focus on
teaching high quality, formal strategic and tactical decision making proce-dures. This focus is in line with the link, established in value management literature, between formal processes and project success (Serra and Kunc, 2014; Chih and Zwikael, 2014, Doherty et al., 2012). Specifically, we combine in-depth theoretical teaching of models with Simulation-Based Training (SBT) through which students gain experi-ence using these models.
The suggested teaching approach has two distinct characteristics that make it attractive when teaching how
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to integrate project management and value management: 1. It focuses on models that link early,
strategic decisions such as defining project objectives, identifying value, and defining target outcomes to project execution and control; the emphasis on this linkage is expected to increase the chance of realizing the project’s value.
2. It combines theoretical models with SBT, which is an effective combination according to a recent study by Zwikael et al. (2013). They concluded that having adequate theoretical knowledge is a pre-condition for effective SBT of project management.
Differently from modern project management methodologies that use qualitative techniques for value maxi-mization (e.g., Lean; see, Womack and Jones, 2010), we use a quantitative mod-el, introduced by Balouka et al. (2014) and Cohen and Iluz (2014), which integrates decisions about product technical performance with time, cost and resource management decisions.
The suggested approach closes a lacuna in project management educa-tion resulting from the lack of focus on value management. The authors of the present paper who teaches in an institute of higher education has seen this common flaw in the courses taught to undergraduate and graduate engi-
neering students. Only recently did we realize that it is important to empha-size value management issues in our courses and now have begun to do so.
3. Project ManagementCourses
This section provides a high level view of value management integration within project management courses, such as the ones taught in my depart-ment. Such a course typically includes 13 three-hour classes, each followed by a one-hour tutorial and home assign-ments.
Project Management Courses and Value Management
Through an Internet search, using terms such as “project management course”, and “syllabus”, I was able to explore project management courses around the globe in order to get an impression how value management is integrated in them. Many courses follow the PMBOK, which has yet to include value management, while other courses use various project manage-ment textbooks.
The courses are diverse in context (e.g., software oriented vs. construction oriented) but the terms ‘value’ and ‘benefit’ are rarely mentioned. More-over, while some courses mention the project initiation phase—where value management plays a major role—many courses focus on scope, schedule, and cost without linking them to the project objectives (see a typical syllabus in Table 1). Even in courses that do refer to an initiation phase, we did not see evidence of value management oriented processes for identifying stakeholders and their needs, and transforming the needs into a specification and value.
Teaching in most courses is based on lectures and class discussions. Few courses use project management tools such as Microsoft Project to demon-strate technical issues (e.g., how to construct a Gantt chart), and SBT is rarely used.
4. Suggested CourseStructure
Table 2 presents a course structure that reflects the suggested teaching ap-proach. Value management is stressed the most during the first third of the
course, when we teach front-end pro-cesses. For example, we teach students about project life cycle models such as the U.S. DOD 5000 and the six-phase life cycle model (suggested by Archiba-ld et al., 2012), which emphasize that project outputs should be utilized in order to realize overall project values. We also introduce models (e.g., Quality Function Deployment—QFD, Analytic Hierarchy Process—AHP and Multi-At-tribute Utility Theory—MAUT), which increase the chances that project out-puts will be utilized by stakeholders. The students’ apply the taught models to strengthen their theoretical models (see an example in the appendix) and use basic simulations. Next, we teach project management basics such as network models, scheduling etc. These foundations are important to realize project goals.
In the last third of the course, the students have sufficient knowledge to integrate value management into pro-ject management. At this point of the course, we teach an integrative math-ematical programming model that links project strategic decisions such
as choosing a product configuration with tactical scheduling and resource allocation decisions (see Section 3), fol-lowed by an extensive, in-class simula-tion exercise such as the one described in Section 4.
5. The Taught ModelsTo generate value, project outputs
must be used by stakeholders, which is why we teach models that emphasize stakeholders’ involvement, especially in the first stages of the projects. This sec-tion describes the main models taught in the course and their linkage to value management.
The first model that we present to students is the Quality Function Deployment (QFD), and especially its main design tool—the House of Qual-ity (Akao, 1997). The essence of QFD is to map stakeholders’ needs through the so-called voice of the customer (e.g., a need may be an economical car), to set attributes through which one can measure to what extent the need is ful-
filled by a project or an alternative (e.g., fuel consumption per kilometer) and to set target outcomes which constitute the project value, if met (e.g., 20% de-crease in fuel cost per mile). Then, QFD defines requirements for these attrib-utes, which form a product (or service) specification for outputs (e.g., an engine volume of 1000 cubic centimeters). The important issue, which makes QFD adjusted with our value management focus, is that the specification (i.e., pro-ject output) maintains a close linkage to the project value (Love et al., 1998). The House of Quality, which inherent-ly includes a benchmark of products/services, enables the setting of realis-tic yet challenging target outcomes, which are required in order to improve performance. The link between setting challenging targets and improved performance has been demonstrated in general decision making settings (e.g., Locke and Latham, 1990) as well as in the context of project manage-ment (Cohen and Iluz, 2014). We teach various approaches to combining QFD with other approaches such as Analyti-
ff 1. Introduction: What is a project?ff 2. An overview of traditional project managementff 3. Project scopeff 4. Defining project activitiesff 5. Time, resources and cost estimation approachesff 6. Network diagrams ff 7. Planning the schedule and costff 8. Project execution: Managing the project teamff 9. Project controlff 10. The critical chain project managementff 11. Project closure
TABLE 1. A typical syllabus for a project management course
Class Topic Learned Value management related HW/Recitation/Simulation
Specific relations to benefit management
1 Introduction Life cycle models, benefit creation purpose of projects
2-4 Project initiation and selection + Stakeholder identification, QFD (The House of Quality, The Voice of the Customer), identification of needs, cost–benefit and cost–effectiveness analyses, development and selection of alternatives, AHP, MAUT
5-6 Project scoping, work break down, and network models
+ The relation of scope to project target outcomes
7-8 Project scheduling
8-9 Resource constrained project manage-ment
+ The effects of scarce resources on realizing project outputs
9 Project budget, project and product control
Control as a means to achieve outcomes
10 An integrative mathematical program-ming model
+ The relation between project outcomes, project cost, sched-ule and resources
11 A simulation exercise In-class, 3-hour exercise in which students apply their theo-retical knowledge in a simulator
12 Risk management + In-class simulation to demonstrate the impact of uncertain-ty and its effect on projects
13 Additional project management ap-proaches, debriefing and summary
+ Lessons learned on how the improved project benefit are realized, other methodologies that emphasize value (e.g., Lean Project Management)
TABLE 2. The focus on value management within a project management course
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cal Hierarchy Process (AHP; Kwong and Bai, 2002; De Felice and Petrillo, 2011).
AHP was introduced by Saaty (for more details, see Saaty, 1988). It is a decision making approach for prioritiz-ing and selecting attributes and alternatives in complex, multi-attribute settings with various stakeholders. In our context, AHP gets as inputs the outcome attributes (e.g., through QFD) and calculates their relative importance (weights), which allows them to be prioritized (e.g., when there are tradeoffs between competing outcomes; see Zwikael and Smyrk, 2012 pp. S14). When defining alternative project concepts (e.g., investing in a stationary torpedo battery or in a fleet of torpedo equipped ships) and configurations (e.g., reengineering a torpedo or developing a new one), the attrib-utes are scored on how well they are satisfied by each alter-native (e.g., to what extent the selection of a configuration satisfies the target outcomes). This allows one to calculate a value for each alternative, based on the weighted summa-tion of the scores. These values enable us to discriminate between the outcomes of possible alternatives, thus aiding in selecting the best one.
As an alternative to AHP, we teach Multi-Attribute Utility Theory (MAUT, see Sarin, 2013), which is specifically designed to maximize the utility (i.e., value) in complex de-cision making settings while taking into account personality characteristics or organizational culture (e.g., risk averse-ness) and the specific situation (e.g., the stakes at risk and the level of uncertainty). As in AHP, the first step is to decide on attributes and outcomes. For example, valid attributes for a project that aims to develop a non-petroleum car with a primary objective to reduce the yearly family expenditure are the operating cost per kilometer and the car’s reliability; possible target outcomes are a 20% reduction in the cost per kilometer, and a 25% increase in the intervals between main-tenance checks. MAUT assumes that a real valued function can represent the decision maker’s preference ordering, so the next step is to construct the decision maker’s utility function with regard to the attributes and to evaluate how they are met by different project concepts (e.g., an electric car vs. a solar car) or by different configurations (e.g., using current battery technology in an electric car or designing improved batteries based on new technologies). Then, the multi-attribute utility function value is calculated, through a structured process—which we teach in detail—enabling outcome maximization decisions regarding concepts and configurations.
Finally, we introduce to the students a new mathemati-cal programming model that maximizes the project value, subject to constraints such as meeting a due date and a given budget, maintaining a positive cash flow throughout the project, satisfying resource availabilities, etc. What is inno-vative about this quantitative model is that it accommodates the impact of both strategic and tactical decisions—both of which are important for a project’s success (Serrador and Turner, 2014; Serra and Kunc, 2014). The model is flexible in the sense that tactical aspects such as meeting cost and
schedule goals can be included in the objective function to reflect the relative importance of these goals compared to achieving target outcomes. Thus, the model, introduced by Balouka et al. (2014) and Cohen and Iluz (2014), finds (near) optimal project configurations and plans with respect to a given objective function and the associated project schedule and resource management policy (that is, how many resourc-es to reserve for hiring/firing throughout the project). This NP-hard model must be solved via heuristic approaches. A simplified version of the model with a solution algorithm was implemented in a simulation tool, so that our students could use it. The model can also be used for project con-trol; if the project deviates from its plan, the model can be resolved with updated data, to yield recommendations for plan adaptations.
Finally, we teach students the concept of an efficient frontier. In real projects there are numerous possible project plans and product configurations, but only a fraction of them—those that are both feasible and expected to deliver the target outcomes—should be considered. To this end, we teach students to construct and analyze an efficient frontier, which only contains these viable plans, delivering the high-est outcome for a given cost. In contrast, any project plan with an equivalent or lower outcome that costs more is not efficient and should not be considered. Students learn that they should choose a plan on the efficient frontier—some-times higher outcomes and higher cost plans are preferred and sometimes a small increase of the outcome does not justify a large additional capital expenditure.
To recapitulate, we teach several value focused models, for use in the project front-end and a mathematical pro-gramming model that feeds from these models and is used in the design, planning and execution phases of a project. Students apply the models in a simulated project environ-ment, as described in the next section.
6. Simulation-Based TrainingA project goal, its objective function, a product concept
and a project plan, as well as execution related decisions, interact to determine a project’s outputs, cost, schedule, etc. Insights regarding these interactions are hard to obtain, especially since the involved mathematical models are complex. In such circumstances, simulation-based training (SBT) is an appropriate tool for experiencing the interac-tions.
SBT has been recognized as an effective teaching ap-proach (e.g., Ruohomaki et al., 1995). We mention in passing the extensive practice of SBT in various disciplines such as software engineering (Pfahl et al., 2001), time-critical de-cision making (Cohen, 2008), and flight training (Rolfe and Staples, 1988). Likewise, there are many project manage-ment simulators (e.g., Celemi, Double Masters, Forio, Polstar, Race to Results, SimProject, SMG, and Synergest). We use a
simulator that was specifically developed to model the links among performance, cost and time aspects, as detailed next.
The Simulation Tool
The simulation tool—the PTB (Shtub, 2012)—facilitates a stochastic, dynamic project management training environ-ment. It was developed to close a gap, found in previous sim-ulators (e.g., Davidovitch et al., 2008), that did not integrate PMBOK knowledge areas. PTB guides students through project management processes, such as choosing a project concept, planning, execution and control, and through knowledge areas, such as scope management, cost manage-ment, time management, quality management, risk manage-ment, etc. It does not require prior simulation knowledge, and students can learn to operate it within an hour, which made it ideally suited for our course.
PTB can simulate project scenarios with budget, sched-ule and performance constraints. Students can design project concepts, choose product configurations from those available, and analyze possible tradeoffs. For example, a stu-dent can choose an innovative high performance configura-tion (e.g., a new technology based design) that entails budget and schedule risks, or a conservative configuration (e.g., a modification of an existing design from a previous project) that may lead to lower performance and enjoys lower risk and smaller budget expenditures. Which choice will lead to the desired project outcomes? This is exactly the type of dilemmas we want students to grapple with, by applying the models they learn, using PTB.
PTB supports several project management best practices, which are learned in the course, for choosing among alterna-tives, for scheduling, budgeting, resource management and control.
The Simulation Concept
We detail here the characteristics of simulated project scenarios and the simulation concept. A good PTB scenario enables students to experience dilemmas that reflect what they will face in reality. Ideas for creating scenarios are detailed in Section 5.
We model a project as an Activity on Node (AON) net-work, where the nodes represent activities (or work packages) and the arcs are precedence constraints. Each activity may be performed in one or more modes. A mode can be viewed as a chosen alternative for an activity (e.g., for the activity “design database configuration”, two possible modes are: Design a centralized database or a decentralized database). Each mode is characterized by output, cost, duration and resource requirements. The amount of resources, renewable and non-renewable, is limited and the project must maintain a positive cash position throughout its duration. The project may face different conditions such as an interest rate, a due date with bonus/penalty for earliness/tardiness, respectively, etc. The project management environment is uncertain, in the sense that actual project execution may deviate from the plan (e.g., activity durations are random variables drawn from Beta distributions). The project objective function is determined through procedures, discussed earlier, such as QFD, AHP or MAUT. An important feature of the model is that it links the activity outputs to the project’s expect-ed value. This is done by setting weights to the different outcome attributes and by the linkage that QFD (and similar approaches) establishes between output values (i.e., those re-quired by the specification) and the corresponding outcomes.
Students have to consider alternative plans and config-urations and choose an efficient one. The efficient frontier (that includes the efficient alternatives) is automatically generated by solving mathematical models via heuristic approaches (e.g., a genetic algorithm). After deciding on a
FIGURE 1. AON for a radar development project.
FIGURE 2. A PTB screen shot that presents the formulas and parameTers for calculation of the radar’s quality, range and reliability.
FIGURE 3. A PTB screen shot that presents relative importance of the radar’s range, quality and reliability.
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project plan, its execution commences. Until the project is completed (or abandoned), uncer-tainty plays a role in the scenario and students have to control and react to changes in the plan. When faced with time and cost overruns, students have to deal with the dilemma of what should be done to maximize the outcomes—continue without changes, change modes (scope), and/or reschedule and change resource management policies. Upon completion, the project outcome, if utilized, is automatically calculated, and students’ performances can be evaluated. In a debriefing session, which completes the project, the students assess their performance and gain insights into future projects.
7. An Illustrative ExampleA stylized example, such as the one de-
scribed below, is typically given to students in the last part of the course.
Consider a radar improvement project, composed of five activities (work packages), as presented in Figure 1.
The students use QFD to identify the re-quired outcomes, which are defined as targets.
The radar project aims to improve identifi-cation of aerial threats, which was mapped to be a function of three attributes: The radar’s range, its quality and reliability. The current baseline of the existing radars was evaluated, resulting in the following target outcomes. The radar’s range should be increased by 20% (i.e., 12 miles compared to the current 10-mile range). The target outcome for the quality was: Increased identification quality of objects 15% smaller than those identifiable by the current radars. The target outcome for reliability was: 10% increase in time between failures by 10%. We note that the realized value of each of these three attributes is affected by mode selections for project activities, as demonstrated in
this point in the simulation students are requested to analyze the efficient solutions (i.e., the ones that constitute an efficient frontier), such as presented in Figure 5 for the radar project. For this project, only two of the possible 25 combinations are determined to be feasible and efficient so students can focus their attention on them. By saying efficient, we mean that there is no other combination of modes for which the outcome is higher and the cost is lower.
Students have the option of overriding the effi-cient frontier recommendations and exploring other alternatives.
Finally, students choose a project plan and start the execution phase. When there are conflicts, such as in the case of a resource shortage, students have to deal with them through rescheduling, mode changes or resource management decisions. They are asked not to rely on intuition and to use the decision mak-ing models they have been taught.
Students may use saved simulation history to review the project, either in class, or individually. The PMBOK (2013, pp. 99) acknowledges the importance of such review for improving future projects.
8. Developing Project ScenariosProject scenarios for the exercises should be
prepared and validated carefully to increase SBT effectiveness (Zwikael et al., 2013). Exploiting the full benefits of SBT is very dependent upon the developed scenarios, so a lot of attention should be invested in developing high-quality SBT scenarios. In this section, we briefly present two ideas, typically used in our scenarios, to emulate value-related dilemmas and tradeoffs.
The first idea is to design scenarios in which there are tradeoffs between a project’s performance and its cost. The tradeoffs can take different forms. A classic tradeoff is one in which configurations with higher performance (enabling higher outcomes) lead to higher life cycle costs. Students need to decide if it is worthwhile, from a project outcome perspective, to choose the higher performance modes or to opt for lower performance to prevent budget overruns, pro-ject delays, etc. A popular strategy that captures such tradeoffs is benefit–cost that maximizes a project’s benefit-to-cost ratio. Our experience suggests that students intuitively choose high performance modes (that is, over-scoping) and underestimate the effect of the resulting increased cost. SBT together with the use of the studied models are necessary to achieve high benefit-to-cost ratios in such scenarios (Cohen and Iluz, 2014) and teach students not to rely on their intuition.
Obviously, a project that leads to bankruptcy does not provide any value, and can pull down the compa-ny (see Pan and Flynn, 2003). Thus, the second idea emphasizes the importance of scheduling, value and cash flows. To this end, we design projects with tight cash flows and multiple payment milestones. Upon the milestones’ successful completion, the company receives payments, which may be used to finance fu-ture activities. Since the project cash flow is designed to be tight in the project’s early stages, students who choose the more expensive modes can end up bank-rupt. To reap the potential values from such projects, students have to make the correct choices of modes and schedule payment milestones, near their early starts.
9. Students’ and Teachers’ Perspectives about the Teaching Approach
This section provides qualitative perspectives by both teachers and students about the teaching approach. The teachers have been teaching project management courses for more than 10 years (and have 20 years of experience in project management as workers, managers or consultants); the students are undergraduates and graduates who used the simula-tor.
The in-class feedback to the teachers led them to believe that SBT and value management integrat-ed with conventional project management models increased students’ understanding of the integrative nature of projects and the importance of jointly con-sidering strategic and tactical decisions (e.g., design decisions that directly affect the project outcomes and resource management decisions that affect project efficiency).
The teachers believe that the students gain in-sights into value-cost-time-performance interactions that if not for SBT, might not have been identified (this belief was confirmed by analyzing students’ feed-back, as detailed below).
Students’ assessment of SBT were collected, through questionnaires (Coffani, 2013) that were analyzed using statistical methods (i.e., non-par-ametric tests such as Wilcoxon signed-rank, and Wilcoxon matched pairs), and qualitatively (coding was the analysis method used). The feedback (from 38 students) indicated that PTB users think it is a good project management training tool and that it enhanc-es the understanding of project tradeoffs, which is
FIGURE 4. A PTB screen shot that presents the data for the activity “antenna design”.
FIGURE 5. A PTB screen shot that presents the efficient solutions for the radar project.
Figure 2. For example, outputs such as the transmitter power, receiver noise and antenna gain determine the radar range through the radar equation (Figure 2, see the formula for range). Note that similar radar range values can be attained by different mode selection combinations (each activity mode selection yields a different output). Nevertheless, these selections also affect the project risk, duration, and cost, and may yield tradeoffs with the other attributes—all these complex interactions have to be taken into account and may affect project outcomes.
To resolve possible tradeoffs, students carry out the procedures they have learned such as the House of Quality, AHP or MAUT, which provide the relative importance of the attributes, and their target values. Figure 3 presents the outcome of such a procedure. The formula for the expected outcome is: 7x[Range]+8x[Quality]+6x[Reliability], where target outcomes for each attribute have been set earlier. Having done all this, there is a basis to plan a project that will maximize the outcomes, subject to the project constraints.
In the simulation, each project activity is associated with modes from which one has to be chosen. For example, see the data for the activity “antenna design” in Figure 4. There are two possible modes: Reengineering the antenna currently used and designing a new antenna. For the former mode, the expected duration, costs and resource require-ments are lower than for the latter mode, and the performance is also expected to be lower. Students have to consider the tradeoff between the two modes. When planning is completed, PTB provides students with relevant alerts (for example, when resource requirements exceed their availability), and information about the project’s expected completion date, cost, etc.
Obviously, in real projects there are numerous mode combinations of which only a handful can be thoroughly analyzed (e.g., if there are n activities, with 2 modes for each, there are 2n possible combinations). At
JANUARY – APRIL 2015 | THE JOURNAL OF MODERN PROJECT MANAGEMENT A 8584 B THE JOURNAL OF MODERN PROJECT MANAGEMENT | JANUARY – APRIL 2015
LITERATURE REVIEW /// INTEGRATING TRADITIONAL AND INNOVATIVE VALUE-FOCUSED MODELS IN PROJECT MANAGEMENT TEACHING
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Coffani, F. (2013). Combination of tools and techniques for project teams to enhance performance. A work-ing paper, URL: https://www.politesi.polimi.it/handle/10589/80601?locale=en
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Nelson, R. R. (2005). Project retrospectives: Evaluating project success, failure, and everything in between. MIS Quarterly Executive, 4(3), 361-372.
Pan, G. S., & Flynn, D. (2003). Gaining knowledge from postmortem analyses to eliminate electronic commerce project abandonment. E-commerce and Cultural Values, 108-123.
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Roth, J. (2014). Applicability assessment of the project management simulation software Project Team Builder. Technical Report.
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Serra, C. E. M., & Kunc, M. (2014). Benefits realisation management and its influence on project success and on the execution of business strategies. International Journal of Project Management, 1-14.
Serrador, P., & Turner, R. J. (2014). The relationship between project success and project efficiency. Proce-dia-Social and Behavioral Sciences, 119, 75-84.
Shtub, A. (2012). Project management simulation with PTB project team builder. Springer.
Slevin, D. P., & Pinto, J. K. (1987). Balancing strategy and tactics in project implementation. Sloan Management Review, 29(1), 33-41.
Vanhoucke, M. (2014). Teaching integrated project man-agement and control: Enhancing student learning and engagement. The Journal of Modern Project Manage-ment, 2(1).
Womack, J. P., & Jones, D. T. (2010). Lean thinking: Banish waste and create wealth in your corporation. New York, NY: Simon and Schuster.
Zwikael, O., Shtub, A., & Chih, Y. Y. (2013). Simula-tion-based training for project management education: Mind the gap, as one size does not fit all. Journal of Management in Engineering, 26-38.
Zwikael, O., & Smyrk, J. (2011). Project management for the creation of organisational value. London: Springer.
Zwikael, O., & Smyrk, J. (2012). A general framework for gauging the performance of initiatives to enhance organizational value. British Journal of Management, 23(S1), S6-S22.
refe
renc
es
author
r Izack Cohen is a lecturer in the Industrial Engineering and Management
Faculty at the Technion, Israel. Izack received his B.Sc in Chemical Engineering,
his M.Sc in Materials Engineering and his Ph.D in Industrial Engineering and Man-
agement, all from the Technion. Before joining the academy, Dr. Cohen has held
senior logistics, engineering and management roles in the Aerospace and Defense
industry. Dr. Cohen has a long-time interest in project management research.
one of our main targets. Answers (on a five-level Likert scale: 1=strongly negative,…,5=strongly positive) to questions such as: How well do you understand the possible tradeoffs of your project (Mean=4.0); and how simple is it to schedule a project for the first time (Mean=4.1), supported good effec-tivity of PTB. In these aspects, PTB was found to be signif-icantly more effective compared to the popular Microsoft Project software package, thus underscoring our use of SBT.
Roth (2014) analyzed PTB for functionality and then conducted experiments with 16 undergraduate students in their 4th and 5th semesters. He stated in his report that “The usefulness of the PTB software in university courses and PM courses along with the improvement of practical PM skills is approved. Additionally, the qualitative written and oral feedbacks were in the majority with some exceptions, in favor of PTB. Most of the negative statements were due to the experiment design especially the scenario choice and some introductory weaknesses. Therefore, these cannot be attributed to the software.” His last two sentences are in line with the teachers’ assessment, and with the relevant litera-
ture (Zwikael et al., 2013) that scenario development is very important.
10. Concluding Remarks We suggest a teaching approach, which is consistent with
new project management trends that focus on projects’ val-ues. This teaching approach, which incorporates traditional project management models, an innovative mathematical model and SBT, enables students to experience the impact that decisions regarding different project knowledge areas may have on a project’s value.
Unlike conventional project management teaching approaches, the use of SBT provides real-time feedback re-garding the students’ performances, and offers opportunities to improve them. The feedback, from teachers and students, about the suggested approach are encouraging so we rec-ommend considering its application in project management education.
APPENDIX — AN EXAMPLE OF AN EXERCISE FOR CHOOSING THE HIGHEST BENEFIT ALTERNATIVEA car manufacturer wishes to improve the popularity of the next family car model compared to the current one. After conducting a survey among potential customers, the manufacturer identified the three main attributes that affect the current car’s popularity: Fuel consumption, esthetics, and safety level. The manufacturer-defined target outcomes compared to the current situation: 15% improvement in fuel consumption, which in the current model is 13 KPL (kilometers
per liter), 15% improvement in the assessment (by a group of designers) of the car’s esthetics (now ranks 6 on a scale between 1-10), and a safety rating increase of 1 star (present ranking is 4 stars by NHTSA rating). However, there are tradeoffs between the target outcomes and the organization has to select the maximum value alternative from the three relevant alternatives, presented in the table below. Use MAUT to choose the best alternative.
# AttributeAlternative Worse
value Current valueBest
possible value3 2 1
1 Fuel consumption 20 15 10 10 13 20
2 Esthetics 6 9 8 1 6 10
3 Safety ranking 5 4 5 1 4 5
Additional information about stakeholders’ preferences with regard to the attributes:
1. Indifferent about fuel consumption of 15 KPL with certainty to 20 KPL with 50% probability and 10 KPL with 50% probability.
2. Indifferent about esthetics ranking of 6 with certainty to 10 with 60% probability and 3 with 40% probability.
3. Indifference between safety ranking of 4 with certainty to 5 with 90% probability and 3 with 10% probability.
4. The attributes are not additive independent and 1 2 30.3 0.2 0.5k k k= = = .
KEYWORDS f management of public programs and projects f public and municipal services administration f e-government
COMPARATIVE ANALYSIS
r A B S T R A C T
The author carried out research the level of maturity of modern project management in the Russian govern-
ment. The experience of federal and regional authorities, as well as state-owned companies in the development
of project management principles are given factors impeding the displacement of the functional approach in
the management of public programs and projects.
JANUARY – APRIL 2015 | THE JOURNAL OF MODERN PROJECT MANAGEMENT A 87
ods of project management in their activities. Their experience requires studying and analyzing to expanding the scope of project management in the activities of the authorities.
Vice Minister of Economic Devel-opment, Leonid Osipov, is very active to adapt and implement in the Russian governmental structures project man-agement techniques that have proved effective in countries such as France, Canada, the UK and Singapore [2].
Now in the Ministry of Economic Development the important directions of its own activities are described in draft form with the formulation of goals, objectives, activities and the relationships between them that allows saving time and budget.
Implementation of project man-agement is a strategic process which requires a thorough monitoring of how this approach is used in government at the moment. Based on the results of such monitoring in autumn 2013 Leonid Osipov, presented a report on the experience of project management in the executive branch and companies with state participation. The report provides a comparative analysis of the main indicators of implementation of project management in the executive branch at the federal and regional lev-els, as well as in companies with state participation.
The main results of the analysis about the level of development of project management in the Russian state structures shows a comparative diagram shown in Figure #1 but in general, it can result in the following conclusions:
ff Project management terms are used in 69.2% of the federal executive authorities, 70% of the regional executive authorities and 100% state-owned companies;
ff Methodological tools of project management are used 15.4% of the federal executive authorities, 12.5% the regional executive authorities and
81.5% of state-owned companies;
ff «Project Committee» (a collective body for the coordination of project management in the executive branch), or at least its equivalent, is not established in 76.9% of the federal executive authorities, 81.3% of the regional executive authorities and 27.3% of state-owned companies;
ff «Project Office» or its equivalent, is not established in 61.5% of federal executive authorities, 68.8% of the regional executive authorities and 0% state-owned companies.
Most governments and state-owned companies use a variety of information technologies for project management, but 100% of the federal executive au-thorities, 87.5% of the regional execu-tive authorities resort to simple tools, such as MS Word and MS Excel. And only 7.7% and 18.8% of the regional ex-ecutive authorities use MS Project for calendar planning. As for specialized IT solutions of project management - only about 10-20% of the executive authorities are using them.
Thus, the majority of the 16 re-spondents regional executive authori-ties do not demonstrate a high level of using project principles: organizational structure of project management and appropriate training and motivation of employees spread slightly. Compa-nies with state participation have been most active in the developing of this approach, this fact demonstrates one of the benefits of public-private partner-ships.
As a result, governments can more efficiently manage the financial, material and human resources, rather than simply react to the arisen cir-cumstances, solving partial functional tasks without linking to the strategy of the whole project. According to V. Dementiev’s research, based on the analysis of the Russian experience, the transition to project management costs are reduced by 15-20%, and the
achievement of objectives is accelerated by 15-30% [3].
This also brings up the question if the federal and regional authorities are ready for the internal reforms and pub-lic servants to change representations about personal responsibility for the implementation of projects.
1. Experience of Russian Regions
There is not enough precedents of successful implementation of project management principles in Russian regional executive authorities and state corporations, as well as experience in organization of project offices. This provides the basis for the analysis and development of standard project man-agement approaches in public admin-istration.
Note that in the pilot regions there are all necessary conditions, including availability OF qualified personnel for the implementation of projects and im-plementation experience of individual elements of project management.
Thus, in the Belgorod region (Bel-gorodskaya oblast, the federal subject of Russia) a federal subject of Russia project offices established not only at the level of the subject, but also in the municipalities, elaborated regulatory framework, and the state program are treated as portfolios with clear perfor-mance indicators. As for the forma-tion of the organizational structure of projects, the control group it is usually created and the working group of the project based on the project contrac-tor. Implementation of the project management system is authorized by the Department of personnel policy of the Belgorod region, and the main contractor is the project management department of the organization (PMO). At the initiation stage of the project the
PROBLEMATIC OF RESEARCH
During the last decade modernization of public administration in Russia has been one of the priorities of development. It’s based on the transformation methods of public administra-tion that must evolve to meet the new standards of management with using project methods and information technologies. Practical realization in this direction was the implementation of two in-terrelated and interdependent processes - admin-istrative reform and e-government. The main in-strument for the implementation of these reforms became the project-based approach, which allows solving problems with the highest level of sophis-tication and complexity. In such circumstances the question arises about the introduction of a well-functioning and effective implementation of the standard toolkit of key state projects.
Project Management in Russia has been ac-tively used for the past 10 years in various areas including (in descending order): national and in-ternational projects - 18%; innovation and R & D - 18%; Information Technology - 16%; industry and transport - 13%; energy (oil, gas, electricity) - 11%; Construction - 8%; social sphere - 8%;, Telecom-munications - 5%; others (media, banks) - 3% [1].
The double situation has developed in the modern Russian public project management. On the one hand, today almost all directions of state and municipal government are approved by the various programs and projects. On the other hand, they are mainly managed by using the functional approach, firmly rooted in the Russian practice of government. In addition, project ap-proach is required for transfering implementation of state tasks to outsource. At this stage only a few regional and federal authorities introduce meth-
r Krivosheeva TeonaSenior lecturer, Head of the Department by Promotion Innovative Projects South Russian Institute of Management
PROJECT MANAGEMENT IN RUSSIAN PUBLIC ADMINISTRATION:THE CURRENT STATE
88 B THE JOURNAL OF MODERN PROJECT MANAGEMENT | JANUARY – APRIL 2015
COMPARATIVE ANALYSIS /// PROJECT MANAGEMENT IN RUSSIAN PUBLIC ADMINISTRATION: THE CURRENT STATE
[1] Dementiev V. (2012). Project management in the strategic planning. Journal “Budget”. 9. pp. 32-35. Retrieved from: http://bujet.ru/article/200687.php
[2] Ministry of Economic Development (2014). Minutes of the meeting of the Council for the implementa-tion of project management in the federal bodies of executive power and bodies of state power of subjects of the Russian Federation. Retrieved from: http://www.economy.gov.ru/wps/wcm/connect/economylib4/mer/activity/sections/councilintrpro-jmanag /2014062610
[3] Order of Ministry of Economic Development of Russia from (2014). “On Approval of Guidelines for the implementation of project management in executive authorities.” Background of the legal system”. Retrieved from: http://www.consultant.ru/document/cons_doc_LAW_162120/Consultant (accessed: 20.04.2014).
[4] Order of the Government of the Russian Feder-ation (2013). “On the Draft Federal Law” On the basis of public-private partnership in the Russian Federation”. Retrieved from: http://base.consult-ant.ru/cons/cgi/online.cgi?req=doc;base=EX-P;n=551468;dst=0;ts=68307C33AA62AD0893C3BE20A238D475;rnd=0.4180825185030699
[5] Sergachev V. (2012). “Project management in the Belgorod region”. Journal “Budget”, № 7, pp. 48-50.
[6] Sinyavsky D., Morgunova N., Lobster T. (2012). “Study of the practice of project management in government agencies and local government ”. Journal Economics and Management, № 8 (93), pp. 96-100.
[7] Website “Rossiyskaya Gazeta”. Retrieved from: http://www.rg.ru/2012/12/12/mer-site.html (ac-cessed: 20.08.2013). re
fere
nces
provide departments ready and flexible tools that help not only to implement projects more efficiently, but to interact in carrying out joint activities and to cooperate with the involved contrac-tors by unifying approaches. However, the testing and evaluation required the effectiveness of project management principles developed by the authorities at the regional and federal level.
Thus, in the Russian modern public administration the implementation of project management is determined by several factors:
ff beginning only outline goals and the need to adjust them as they reach the intermediate results of the quantitative and qualitative evaluation is difficult;
ff timing and duration of the project depends on the probability factors or only outlines and subsequently subject to change;
ff project expenses, as a rule, are not optimized and depend on budgetary allocations;
ff resources are allocated as required when possible;
ff there is an administrative formalism which introduces innovations, that have only a modern look without proper analysis of their effectiveness in specific contexts;
ff changes in legislation occus without a basic change;
ff the organizational culture does not provide for risk and innovation;
ff there is the inertia of public servants who accept only the top-down initiative.
These and other factors determine the list of problems whose solution will expand the use of project management to the Russian authorities and compa-nies with state participation:1. The lack of detailed implementation
of project management methodology and basis of legal regulation of project management in the executive branch;
2. Project management is not used as a tool to achieve the strategic objectives;
3. Rigid organizational structure at the level of executive authorities does not produce a team to implement projects in accordance with the objectives;
4. Shortage of project management specialists, low motivation and insufficient public and municipal employees trained in methods of project equations;
5. The need for a unified, flexible and affordable software to support project activities.
However, any managerial inno-vation will not bring their effect in
unprepared environments. Introduc-tion of innovative information and management technologies should be accompanied by changes in the organ-izational culture and motivation of the authorities.
The major task in the development of Russia at the present stage appears more effective realized government programs. In this paper the author presented an analysis of modern in-novation methodology of this kind of project management and considered the possibility of project management for the implementation of government programs. It consists of a description of the structure and functions of the project office for the implementation of government programs based on outsourcing. Strategic management of the modernization of public ad-ministration in Russia is based on the methodology of project management. It describes the benefits of project man-agement applied to the management of innovation in public administration. The project management provides a tool which allows to achieve the strategic objectives for the moderniza-tion of public administration the most effective way.
FIGURE 1. Development of project management in the Russian state structures
helps reduce the work course by 20-50%, and at the same time minimize budget overruns [4].
Project-based approach can be used as an optimal tool in the implementation of public-private partnership. In the Tomsk re-gion a large-scale project was implemented - “INOTomsk’2020” that requires effective coordination of all stakeholders - govern-ments, universities, large businesses, public corporations, on the basis of the design approach.
For the near future the possibility of replication project management methodology based on the analysis of the experi-ence of the pilot regions is planned to be considered.
2. State-owned companies —leaders in the implementation of project management methodology
State-owned companies are increasingly using modern technology, including the methodology and project manage-ment. The new trend in 2014 is the realization of infrastructure projects by state companies, together with private partners based on the creation of special purpose vehicles (SPV) [5]. The project company on the basis of an agreement with state-owned companies is implementing the project. Development of this area also contributes to the planned adoption of the law in a public-private partnership. This model allows, on the one hand, to circumvent restrictions on the duration of the project in the budget legislation, on the other - to carry out the current control and monitoring of the progress of the project through equity in the Public Procurement SPV.
We can assume that the experience of project management will be adapted to the needs of the Russian authorities in the chain from the foreign companies operating in Russia in accord-ance with international standards of project management to the Russian business and public companies, and finally, to the authorities.
3. ConclusionAt this stage it is necessary to institutionalize project man-
agement. In this regard, and in order to overcome the problems in the implementation of project management, the Ministry of Economic Development in 14 April 2014 published the long-awaited Methodical recommendations for the implementa-tion of project management in the executive branch. [6]
They were developed in 2013 by the Council on the imple-mentation of project management in the executive branch at the Ministry of Economic Development based on the analysis of the Russian and international experience in the implementation of project management in government [7]. Uniform for the entire country the Methodical recommendations are designed to
initiator application is registered in the department of personnel policy. After that, the industry expert committees collectively decide on the future of the project. If the decision is positive, the general concept of the project is accepted and the planning of the pro-ject takes place, which is reflected in the prescribed form - passport project and the project management plan.
It’s hard to ignore the fact that the modern project management is hard to imagine without the use of information tools projects. Therefore, all projects are registered in the database system in general elec-tronic document “Electronic Government of Belgo-rod Region” by which the check points are tracked throughout the whole course of each project. All executive authorities at both the regional and munic-ipal levels work in the database. It registered 1,233 projects, implemented 622 projects, 204 sold, and the rest are time-consuming stage of development. Most of the projects are social, there are more than 465 units, followed by economic - 304, then the technical - 238 and -187 organizational. With the experience of the Belgorod region, project-based approach allows to link the performance of public servants with their material and moral incentives. Also it provides optimal distribution of time, human and material re-sources, contributes to the quality level of interagency cooperation to achieve all common and understand-able results. Implementation of project management
author
r Teona Krivosheeva is head of the department by promotion innovative projects, graduate student. Research Dissertation topic: “The project approach in the development of e-government as a basis for the transition to public administration”. Experiences: the implementation of projects to create multifunctional centers providing public and mu-nicipal services (MFC) in the Rostov region and other regions of Russia; chief editor of scientific and practical magazine “innovation in public and municipal administration”; the organization of training courses and conferences for state and municipal officials on modern technologies of public administration.
KEYWORDS f portfolio management of projects f project management offices f organisational capability
EXPLORATORY STUDY
r A B S T R A C T
Organizations’ poor performance results in losing 10.9 percent of their investment in portfolios of projects
which shows their inadequate management of strategic initiatives. Thus the development of Portfolio Manage-
ment of projects (PfM) as an organisational capability that enables organisations to align their projects with
organizational strategy on the one hand, and concurrently, the raise of Project Management Offices (PMOs) to
support executives ensuring strategic alignment across the portfolios, organizations establish. PMO supports
PfM to fill the gap between strategy formulation and implementation. While PMO forms show a variety of
structures in different organizational settings and transformations over time within the same organizational
setting, there is still no clear understanding of how PMO evolves in its short life span in conjunction with PfM
practices. However, the understanding of co-evolution processes of PfM practices and PMOs as organizational
settings remains unclear, partly because the past and current studies rely on a being ontology in order to study
change and evolution and because they are mostly based on single level or flat perspective locating phenomena
in “a web of interconnections”. Taking a routine perspective and a process and becoming view we offer in this
exploratory study: 1. A multi-level conceptual framework unveiling the dymamics of co-evolution between PfM
practices and PMO as organizational / organizing entities. To this regards this research could be called a box
changing research as, if the primary point of reference is the PfM and PMO literature, we call for routine lens. 2.
An assumption challenging approach based on a process and becoming ontology, supporting a somehow inno-
vative research, involving a multilevel or tall view linking local praxis level, i.e. PfM routines, with higher level of
structure and system, i.e. PMO.
JANUARY – APRIL 2015 | THE JOURNAL OF MODERN PROJECT MANAGEMENT A 91
INTRODUCTION
Organizations’ poor performance results in losing 10.9 percent of their investment in port-folios of projects which shows their inadequate management of strategic initiatives (PMI, 2014).
Thus the development of Project Portfolio Management (PfM) as an organisational capabil-ity enables organisations to align their projects with organizational strategy (Killen & Hunt, 2012; Levine, 2005) on the one hand, while increasing Project Management Offices (PMOs) to support executives, ensuring strategic alignment across the portfolios, establishing organizations (PMI, 2010). PMO involvement shows a significant effect on schedule and budget performance, productivity and decreased number of failed projects (Solutions, 2010). With various struc-
tures, functions, models, roles and typologies (Desmond, 2014; Hobbs & Aubry, 2006, 2007, 2008), PMO supports PfM to fill the gap between strategy formulation and implementation (Mor-ris & Jamieson, 2005). While PMO forms show a variety of structures in different organizational settings and transformations over time within the same organizational setting, there is still no clear understanding of how PMO evolves in its short life span in conjunction with PfM practices. How-ever, the increasing popularity of PMOs among project based organizations shows the importance of PMOs in successfully managing portfolios of projects and achieving better organizational per-formance (Aubry, Hobbs, & Thuillier, 2007; Hobbs & Aubry, 2008, 2010; Petit & Hobbs, 2012).
r Stephane Tywoniak Professor at Groupe ESC La Rochelle (La Rochelle, France) and Queensland University of Technology (Brisbane, Australia)
r Christophe N. Bredillet Scientific Director, Société française pour l’avancement du Management de Projet (Paris, France) and Adjunct Professor, Queensland University of Technology (Brisbane, Australia)[email protected]
Grasping the dynamics of CO-EVOLUTION BETWEEN
PMO AND PfM:
a box-changing multilevel exploratory research GROUNDED IN A ROUTINE PERSPECTIVE
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However, the understanding of co-evolution processes of PfM practices and PMOs as organi-zational settings remains unclear, partly because the past and current studies rely on it being ontology in order to study change and evolution (Tsoukas & Chia, 2002) and because they are mostly based on single level or flat perspective lo-cating phenomena in “a web of interconnections” (Seidl & Whittington, 2014, p. 1415). Taking a routine perspective and a process and appropriate view we propose the following in this exploratory study:
ff 1. A multi-level conceptual framework unveiling the dynamics of co-evolution between PfM practices and PMO as organizational / organizing entities. To this regard, this research could be called a box changing research (Alvesson & Sandberg, 2014) since if the primary point of reference is the PfM and PMO literature, we call for routine lens.
ff 2. An assumption challenging approach (Alvesson & Sandberg, 2013) based on a process and becoming ontology, supporting a somehow innovative research, involving a multilevel or major view (Biesenthal & Wilden, 2014; Seidl & Whittington, 2014) linking local praxis level, i.e. PfM routines, with higher level of structure and system, i.e. PMO.
On the above basis, we suggest a multiple case study strategy and a mixed methods approach in three stages. In a later empirical phase, outside the scope of this paper, the propositions, forming the conceptual framework, will be first qualita-tively validated. Then the subsequent refined con-ceptual framework and the resulting hypotheses will be quantitatively tested; and finally, through multiple case studies the final conceptual frame-work will be operationalized.
1. Literature ReviewPortfolio management (PfM)
In project based organizations, late and over budget projects (Lawrence & Scanlan, 2007), cost overruns (Cantarelli, Flyvbjerg, Molin, & van Wee, 2010; Flyvbjerg, 2005), poor estimation during the planning phase (PWC, 2012), insufficient resource allocation, poor risk and change management (Lawrence & Scanlan, 2007) are mentioned as the reason for the projects’ failure. To manage project portfolios simultaneously, top managers need to have a holistic project oriented perspective of the organization (Jerbrant & Gustavsson, 2013;
Sanchez, Robert, Bourgault, & Pellerin, 2009) in order to adapt to the changing environment. PfM is a dynamic decision process to prioritize the shared resources, reduce the uncertainty and coordinate the interfaces (Cooper, Edgett, & Kleinschmidt, 1997; Martinsuo & Lehtonen, 2007; Müller, Martinsuo, & Blomquist, 2008). Although the benefits of portfolio management have been highlighted in former research (Cho & Shaw, 2013), there are some general challenges, such as resource balancing, prioritizing the projects and poor information management that portfolio managers face in organizations (Campbell, 1970; Cooper, Edgett, & Kleinschmidt, 2000; Elonen & Artto, 2003). Former studies on portfolio manage-ment have also focused on methods, strategy and control tools (Moore, 2010; Teller, Unger, Kock, & Gemünden, 2012). The complexity of manag-ing project portfolios and the need for improved project coordination resulted in establishing Project Management Office (PMO) (Artto, Kulvik, Poskela, & Turkulainen, 2011; Singh, Keil, & Kasi, 2009).
Project Management Office (PMO)
PMO, known as centre of excellence or exper-tise, is an organizational entity to implement PfM practices, methodologies and strategic choices (Ward, 2000). The main goal of PMO is improving organizational project management effectiveness (Stanleigh, 2006).
Former research shows that PMOs have a wide range of typologies, functions and roles. First, the empirical research of Hobbs and Aubry (2006) within 500 PMOs found no specific pattern for PMO typologies. Explorative research of Müller et al. (2013) based on several case studies develops a relational typology of PMOs based on three roles of servicing, controlling and parenting. Second, Bates (1998) mentions that while support and leadership are the basic functions of PMOs, PMO functions depend on specific requirements of the organization. Kwak & Dai (2000) also state that functions of PMOs depends on the size of the organization and the organization’s management objectives. The empirical study of Dai and Wells (2004) among 234 PMI members shows a series of PMO functions, such as providing project man-agement standards, project historical archives, project administrative support, Staffing, train-ing and monitoring. Later, Hobbs and Aubry’s (2007) descriptive survey among 500 PMOs demonstrates that the most important func-
tions of PMOs are: report project status to upper management, monitoring and control of project performance, implement and operate a project information system, and develop and maintain a project scoreboard. Third, a wide range of roles and responsibilities, from providing clear project management guidelines to conducting portfolio management practices, are assigned to PMOs (Santosus, 2003). Turner and Keegan’s (2001) ob-served the management of projects in 4 case stud-ies and proposed the broker and steward roles for managing project based organizations. The qual-itative study of Desouza & Evaristo (2006) among 32 IT organizations shows that PMO roles fall within three tactical and operational levels. Later, Hobbs and Aubry (2010) proposed a descriptive PMO model and suggested the controlling or the supportive role. The quantitative study of Ward & Daniel (2012) among 157 managers demonstrates that the main PMO roles regard involvement in identifying and quantifying benefits, planning the technology implementation, planning the busi-ness changes and benefit delivery, post implemen-tation review of TCQ (time, cost and quality), post implementation review of business changes and benefits. The quantitative research of Unger, et al. (2012) among 278 portfolios identifies the coor-dinator, controller and supporter roles of PMOs. As a result, with the variety of PMO roles and functions, the PMO work scope is not clear.
Former research (Kerzner, 2003; Turner & Keegan, 2001), following classical perspectives suggest that PMO functions shall be institution-alized. Other research (Aubry, Hobbs, & Müller, 2010; Hurt & Thomas, 2009) look at PMO’s fre-quent transformation and patterns of change and suggest a progression of states for PMOs. First, an empirical research (Hobbs & Aubry, 2006) within 500 PMOs demonstrates that there is no specif-ic pattern associated to roles and functions of PMO. Second, as the list of active projects is being updated regularly, managing the portfolio is a dynamic process (Cooper et al., 1997). As a result, PMOs are transformative organizational entities because their roles and functions are constantly evolving with regards to environmental change. This study looks at PMO through a new lens and defines PMOs as transformative entities with a pattern that is emerging from change. Adopting an appropriate paradigm (Tsoukas & Chia, 2002) is suitable to investigate this ongoing change. In this appropriate perspective, organizations and entities are just instantiations of emerging pro-
cesses in a “state” (Langley, Smallman, Tsoukas, & Van de Ven, 2013; Tsoukas & Chia, 2002). A grow-ing number of recent researches have studied how processes change over time (Langley et al., 2013; Sergi, 2012; Vaagaasar & Andersen, 2007). Process studies focus on the evolving phenomena and organizations are viewed as ongoing process-es (Langley et al., 2013); therefore, change can be modelled on motion.
Therefore:
Proposition P1.1: PMO, as organizational entity, has a permanent transformative nature.
Although former research shows the positive relationship between the existence of PMO and organizational (Aubry & Hobbs, 2011; Aubry, Richer, Lavoie-Tremblay, & Cyr, 2011) and sin-gle project (Dai & Wells, 2004; Ward & Daniel, 2012) performance, the short life span of PMOs calls for further exploratory research. While it is temporality central to PMO studies (Aubry, Hobbs, et al., 2010; Aubry, Müller, Hobbs, & Blom-quist, 2010; Hurt & Thomas, 2009; Pellegrinelli & Garagna, 2009) considering PMO dynamics as paradigm and substantive ontology reflects the change patterns as fixed identifiable entities (Langley et al., 2013). In contrast, considering PMO dynamics under appropriate paradigm and process ontology views PMO as a set of processes; an entity (like PMO) is continuously in the state of being suitable (Langley et al., 2013). Hobbs, and Müller (2010) recommended as an alternative the-oretical foundation to investigate the dynamics of PMO. This study adopts organizational routines as an alternative analytical lens to examine PMO dynamics in regards to PfM.
Routines as the analytical lens of study
An early study of Nelson and Winter (1982, p. 14) describes organizational routines as all the regular and predictable behavioural patterns of firms. Routines have been defined as the repet-itive, recognizable pattern of interdependent actions that involve multiple actors by some authors (Feldman & Pentland, 2003) which has been widely adopted by other studies (Becker, La-zaric, Nelson, & S.G., 2005; Dionysiou & Tsoukas, 2013; Pentland, 2011; Pentland & Feldman, 2005, 2008b, 2008c). Nelson and Winter (1982) concep-tualize organizations as repositories of routines. Routine has also been defined as organizations’ generic capacity in some studies (Becker, 2004; Levitt & March, 1988; Nelson & Winter, 1982).
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Although the seminal work of Nelson and Winter (1982) has been cited in the recent re-search of organizational routines, the concept of routines has been documented in older stud-ies. Schumpeter (1927) was the first author who recognized the routine work in organizational life. Later, March and Simon (1958) focused on the ability of organizational routines to change in regards to environmental change. Cyert and March (1963) focused on the behavioural perspec-tive of routines as the repetitive pattern of actions by multiple actors which are guided by organiza-tional rules.
Routines can be described at two different lev-els. Routines can be described as a recurrent ac-tion pattern at a particular place and time or can be described as a pattern at a general level (Becker et al., 2005). These two aspects (elements) of routines were later called ostensive and perform-ative (Feldman & Pentland, 2003). The ostensive element is the schematic or narrative description form or principle of routine. Performative element includes “the specific actions done by specific people at specific times and places” (Feldman & Pentland, 2003, p. 101). Ostensive and per-formative aspects of routines correspond and are mutually constitutive (Pentland & Feldman, 2008b). The two aspects of routines are inter-re-lated. Some studies (Pentland & Feldman, 2005) mention artefact as the third aspect of routine; routine process is encoded in artefacts.
In addition, routines are seen as the source of stability and change (Becker, 2004, 2008; Cohen-det & Llerena, 2003; Feldman, 2000; Feldman & Pentland, 2003; Nelson & Winter, 1982; Pentland & Feldman, 2005, 2008c). Routines are concep-tually a source of change (Feldman & Pentland, 2003; Nelson & Winter, 1982). Nelson and winter (1982) state that change in a routine causes inno-vation. Empirical research (Feldman, 2000; Feld-man & Pentland, 2003) shows that routines can change over time. Other studies (Becker, 2004,
2008; Becker, Knudsen, & March, 2006; Becker et al., 2005; Pentland & Feldman, 2008b, 2008c) also accept the notion of change in routines. Therefore, routines are dynamic processes that create stabil-ity or change. As organizational routines provide a good infrastructure to study organizational change at micro level (Becker et al., 2005), this study will focus on the dual function of routines as the source of stability and/or change. Change within organizations can be explained by routines (Becker, 2004). Organizational routines in the literature are mentioned to be both the source of flexibility and change (Bresnen, Goussevskaia, & Swan, 2005; Feldman & Pentland, 2003).
Multi-level collective entities
In field studies, it is hard to see the whole rou-tine because each participant performs different parts of a routine (Pentland & Feldman, 2008a). Feldman and Pentland (2003) argue that in many cases the overarching pattern of the routines is relatively stable while specific parts of the routine pattern show considerable change. In order to explain the dual function of routines as a source of stability and change, Miner, Ciuchta and Gong (2008) state that a routine is a system of sub-rou-tines; Therefore, while the overall pattern may seem stable, specific sub-routines may change. The other explanation for sub-routines is that a routine has a set of options (sub-routines) that can be chosen for implementation (Feldman, 2000) by different actors. It can be concluded that routines are implemented to gain a specific outcome; sub-routines are a selection of repetitive pattern of actions. On the other hand, Salvato (2009) and Nelson and Winter (1982) suggests to conceptu-alize routines as sequences of individuals’ action over time.
Organizational capabilities are usually seen as a set of routines (Becker, 2008; Salvato & Rerup, 2011). Grunt (1996, p. 377) defines organizational capability as “…a firm’s ability to perform repeat-
edly a productive task which relates either directly or indirectly to a firm’s capacity for creating value through effecting the transformation of inputs into outputs”. Winter (2003) states that the con-cept of organizational capability is founded on the concept of routines. Salvato and Rerup (2011) dis-cuss how evolution in routines results in evolution in capabilities. A capability is a high level routine or a collection of routines to produce specific type of outputs (Winter, 2003).
Figure 1 shows the relationship between the mentioned organizational concepts.
Routines and organizational change
The concept of routine as the main replicator of organizational phenomena has been mentioned in the literature (Hodgson, 2013a, 2013b; Nelson & Winter, 1982; Salvato, 2003). Previous stud-ies (Becker, 2004; March & Simon, 1958; Nelson & Winter, 1982) propose that organizational capabilities are a collection of routines. Hodgson (2013a, 2013b) states that organizational routines play a serious role to adapt the organization to the changing environment. Therefore, routines are the proper unit of analysis to study change at a micro level (Becker et al., 2005).
Although Nelson and Winter (1982) are the fundamental authors of organizational routines, earlier research has mentioned the ability of or-ganizations to respond to environmental change by changing their routines (March & Simon, 1958). While Nelson and Winter (1982) define routines as regular and predictable patterns, which is mostly understood as a stable pattern, creation of any sort of novelty in organizations consists of a recombination of existing routines (Nelson & Winter, 1982, p. 130); other authors mention the notion of a choice of both stability and change (Feldman & Pentland, 2003; Pentland & Feldman, 2005). Becker, et al., (2005) states that the notion of change of stability of routines depends on the industry; routines involved in in-ventory management may seem more stable while firms competing for new products have more transformative routines.
Routines, as unit of analysis, provide a theo-retical framework to understand organizational change (Nelson & Winter, 1982). The changing capacity of routines as the unit of analysis has the potential to understand the constant transforma-tive nature of organizations.
The literature (Van de Ven, 1992) suggests four perspectives to analyse change within organiza-
tions, namely: evolutionary theories, life cycle, teleological and dialectic perspectives. Dialectic and evolutionary views of routines are both rele-vant to examine organizational change. However, looking at change at the micro level through the lenses of routines, teleological view does not seem adequate. Teleological view may be a good candi-date to examine change at the macro level; for ex-ample to examine whether portfolio management fits within the organizational business objectives. The life cycle theory for not having an analogue view to the change process is also not adequate.
Dialectic theory states that competing agendas or conflict are the driver of change (Van de Ven, 1992); therefore, participants with conflicting views engage in political behaviours to change the norms and regulations (Hargrave & Van De Ven, 2006). The new regulation or the modified ones would be dictated to the routine’s actors. When the actors of routines decide to change the osten-sive element to change the way they do their jobs, the variation is intentional (Feldman, 2000). As a result, actors of routines vary the performances which are referred to as selective variation (Feld-man & Pentland, 2003). In this case a change in an ostensive element of the routine as a result of contextual influences causes the performative el-ement of the routine to change accordingly. Over-all, dialectic theory focuses on power relations, which make this theory the proper candidate to study how dominant organizational actors, rules and procedures can transform routines.
The evolutionary theories look to the natu-ral environment to find how change occurs in foreseen organizations as a collection of routines (Salvato, 2003). Most authors embrace Darwinism or natural selection approach to study change through routines (Hodgson, 2013a, 2013b; Hodg-son & Knudsen, 2004; knudsen, 2002). Natural selection approach looks to the environment to understand how the populations respond to environmental conditions and compete for local scarce resources which may lead to environmen-tal change (Van de Ven, 1992). When actors of routine choose different sub-routines to fulfil their roles, involve in a trial and error learning or respond to a situation the unintentional varia-tion takes place (Aldrich & Ruef, 2006). In this case, a change in the performative element of the sub-routine is retained in the ostensive element (Feldman & Pentland, 2003). Darwinism has been challenged by other authors (Scholz & Reydon, 2013). Scholz and Reydon (2013) state that change
FIGURE 1. From capability to actions (adapted from Salvato and Rerup (2011))
(as collection of routines)
(as repertoire of sub-routines)
(as sequence of actions)
Capability Routine Sub-Routine Individual actions
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process cannot be easily explained by biological evolutionary theories and the definition of popu-lation should be defined more cautiously. How-ever, Hodgson (2013a) believes that Darwinian perspective in biology is the core principle and the scope should be expanded to be applied to a spe-cific phenomenon, like organizational routines. The other problem of evolutionary perspective is that it has not been empirically applied (Miner et al., 2008). Evolutionary theories are recommend-ed to be appropriate to study dynamics of change through organizational routines (Feldman & Pentland, 2003; knudsen, 2002; Nelson & Winter, 1982; Pentland & Feldman, 2005) .
Routine adaptation models
There are three possible models to investigate the transformation of routines.
First, Weick (1979) states that organizing is a systematic sets of convention in which some in-terlocked behaviours form the social process. The essential building block of organizing is interac-tions between two people to reduce uncertainty. The four elements of organizing are ecological change, enactment, selection and retention. This model (Weick, 1979) suggests assembling ongoing interdependent actions into sensible sequences that generate sensible outcomes. Organizing demonstrates the natural selection process. This model can be a candidate for investigating the creation of routines.
Second, Miner, et al. (2008) follows Nelson and winter’s (1982) suggestion to fit the concept of routines into evolutionary framework. This model pursues the replication of routines and shows how routines are varied, selected, and retained. This
model assumes that an organization begins with a collection of routines which will change based on individuals’ experience. Miner, et al. (2008) suggests the need for empirical research to inves-tigate the interaction between different levels of routines. Interaction of routines at different levels affect the transformation of routines and increas-es the variation of routines (Miner et al., 2008).
Third, Dionysiou & Tsoukas’s model (Diony-siou & Tsoukas, 2013) focuses on creation and recreation of routines based on the suggestion of Feldman and Pentland (2003) through the reciprocal relationship between the performative and ostensive aspects of routines. This study investigates the internal dynamics of routines and Mead’s symbolic interaction (Mead & Morris, 1934); so, people act on the basis of meaning and by role taking, the meaning is modified through social interactions which leads to the selection of different sub-routines by routine actors. Dionysi-ou and Tsoukas’s (2013) shows how the perform-ative, ostensive and artefact elements of routines are interacting.
The three phases of variation, selection and retention can be tracked in transformation models that follow evolutionary theories. Varia-tion is created among the available routines. Any institutionalized experimentation or incentives to innovate creative enactment of organizational routines (Aldrich & Ruef, 2006). Organizational members select among the new collection of rou-tines. Changes in the selection phase create new processes (Aldrich & Ruef, 2006). Retention of the mixture of routines takes place. Retention phase completes the transformation by storing the required knowledge for reproducing the process in an organizational form (individuals, groups,
structures, policies or networks) (Aldrich & Ruef, 2006). Dionysiou & Tsoukas (2013) and previously Miner, et al. (2008) try to justify routine transfor-mation through the lenses of evolutionary theory. As Feldman & Pentland (2003, p. 94) state, “…the relationship between ostensive and performative aspects of routines creates an on-going opportu-nity for variation, selection, and retention of new practices and patterns of action within routines and allows routines to generate a wide range of outcomes, from apparent stability to considerable change”. Both Miner, et al.’s (2008) and Weick’s (1979) model emphasis on natural selection, i.e, the Darwinian perspective. The ecological change phase in Weick’s (1979) model is a part of the variation phase in Miner, et al.’s (2008) model. In addition, the retention process involves evaluating the performance of a routine (Furneaux, 2012) which can be covered by the performative aspect of routines. The three phases of variation, selec-tion and retention of routine transformation has been investigated in former studies (Aldrich & Ruef, 2006; Furneaux, Tywoniak, & Gudmunds-son, 2010). All these three models show how change at micro level is adapted by the organiza-tional members and how the context affects the change process. Although Weick (1979) did not directly use the routine phenomena, his defini-tion of organizing, as the interlocked behaviours which form a social process, suits the definition of organizational routines with a behavioural reg-ularities interpretation. Dionysiou and Tsoukas’s (2013) model has the similar stages of Weick’s (1979) model. All these three models show how change at the micro level is adapted by the organi-
zational members and how the context affects the change process. Although Weick (1979) did not directly use the routine phenomena, his definition of organizing, such as the interlocked behaviours which form a social process, suits the definition of organizational routines with a behavioural regu-larities interpretation.
Dionysiou and Tsoukas’s study (2013) shows how the performative, ostensive and artefact ele-ments of routines are interacting. Dionysiou and Tsoukas’s (2013) model has the similar stages of Weick’s (1979) model. Variation can be intentional or unintentional.
ff 1. When the actors of routines decide to change the ostensive element to change the way that they do their jobs, the variation is intentional (Feldman, 2000). As a result, actors of routines vary the performances which is referred to as selective variation (Feldman & Pentland, 2003). In this case a change in the ostensive element of the routine as a result of contextual influences causes the performative element of the routine to change accordingly. Therefore, actors of the routine are intentionally changing an institutional arrangement (a rule, norm or procedure) (Van de Ven & Hargrave, 2004) by changing the artefact or the ostensive element of the routine. According to Hargrave & Van De Ven (2006) dialectic theory is the proper lens to study institutional change.
ff 2. When actors of routine choose different sub-routines to fulfil their roles, involved in a trial and error learning or to respond to a situation the unintentional or blind variation happens (Aldrich & Ruef, 2006). In this case, a change in the performative element of the sub-routine is retained in the ostensive element (Feldman & Pentland, 2003). Miner, et al.’s (2008) model provides a good understanding of change over time in organizational
FIGURE 2. The relationship PMO and Pf
PMO functions Artefact definition
Implement PfM processes (Ward, 2000) Implement new routines for formalization (March, Simon & Guetzkow, 1993)
Developing standards (Dai and Wells (2004); Kwak and Dai (2000)); control and monitoring of processes (Dai and Wells (2004); Hobbs and Aubry (2007); Kwak and Dai (2000))
Codify rules and procedures (Pentland and Feldman, 2005)
TABLE 1. Comparing artefacts and PMO functions
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forms, change within organizations and more important change within routines (Feldman & Pentland, 2003). As Feldman & Pentland (2003, p. 94) state, “…the relationship between ostensive and performative aspects of routines creates an on-going opportunity for variation, selection, and retention of new practices and patterns of action within routines and allows routines to generate a wide range of outcomes, from apparent stability to considerable change”. The evolutionary theory through the process of variation, selection and retention is the right perspective to study unintentional variation (Hargrave & Van De Ven, 2006).
PfM and PMO relationship
This study adopts Barley and Tolbert’s (1997) model to investigate the co-evolution of PMO and PfM. PfM is an ongoing dynamic process (Cooper et al., 1997) which can provide the op-portunity of studying change at the micro level. Barley and Tolbert (1997) investigated how a new institution emerges from patterns of actions and interactions. This research (Barley & Tolbert, 1997) looks at institutionalization as an ongoing process. Four movements can be observed in each
time interval (T1, T2…), namely: encoding of the organizational principle, enactment of the script, replication of the script and externalization of the pattern behaviour. By comparing the scripts at different time intervals, the change in the pattern behaviour can be uncovered.
The change at PMO level can be studied through dialectic theory; the change at PfM level can be studied through evolutionary theory. Any change at PMO level will be followed by a change at PfM level. Therefore, it is necessary to look at PMO and PfM co-transformation through a collective perspective of both evolutionary and dialectic theories.
In order to study PMO and PfM co-trans-formation through the lenses of routines, PfM processes should be routinized. PfM qualifies or-ganizational routines by serving Pentland’s (2011) test. In addition, PfM is an organisational capabil-ity that helps organisations to align their projects with organizational strategy and to manage projects’ shared resources (Killen & Hunt, 2012; Levine, 2005). PfM also serves the characteristics
of organizational capabilities (Teece, Pisano, & Shuen, 1997).
Therefore:Proposition P2.1: PfM, as organizational
capability, is a collection of routines.
PMO and PfM relation through the lens of routines
Besides the PMO definition, it is an organiza-tional entity to implement PfM processes (Ward, 2000). The Artefact element of an organizational routine has the purpose of creating new routines (March, Simon, & Guetzkow, 1993). Table 1 shows how different PMO functions can be compared with artefact definition in different studies.
By comparing the PMO functions and artefact definition, this study concludes that PMO (regard-less of its typology) supports PfM by providing rules and procedures and recording the archival data. PMO acts as a meta-artefact to provide formal support for PfM. Therefore, this study proposes two directions of change.
First, PMO can be the driver of change in PfM routines. Any change required from the organiza-tional level can be codified in new procedures to be implemented in PfM. This intentional varia-tion happens when organizational members try to generate alternatives and seek solutions to prob-lems; this variation depends on the individuals’ problem-solving behaviour or cognitive element
of routines that have worked in the past (Aldrich & Ruef, 2006).
Besides Table 1, PfM process groups have the two major processes:
ff 1. Defining the portfolio, the portfolio charter and the portfolio roadmap
ff 2. Developing portfolio strategic, management, performance management, communication management and risk management plans
Therefore, it is expected that changes at the level of PMO would contribute to the two major process groups of PfM activities. Any change at PMO level, in regards to organizational require-ments, will first transform the artefact element of PfM routines; the ostensive and performative elements will transform consequently. This study suggests that PMO is an organizational level artefact.
Therefore:
Proposition P1.2: Changes at the level of PMO are the driver of change in PfM routines.
Second, existing routines accommodate small changes in the individuals’ actions (Aldrich & Ruef, 2006). This changes in individuals’ actions can be because of conflicts, passion, luck, imita-tion, misunderstandings and surprises (Weick, 1995). This study suggests that unintentional variations in the performative element of PfM routines are the driver of change at PMO level.
FIGURE 3. The proposed conceptual framework
TABLE 2. Summary of research questions and propositions
RESEARCH OBJECTIVE RESEARCH QUESTIONS PROPOSITIONS
Understanding the
co-evolution of PMO and
PfM.
Q1. How do changes at PMO
level transform PfM routines?
P1.1. PMO, as organizational entity, has a permanent transforma-
tive nature.
P1.2. Changes at the level of PMO are the driver of change in PfM
routines.
Q2. How do transformations
of PfM routines lead back to
changes at PMO level?
P2.1. PfM, as organizational capability, is a collection of routines.
P2.2. Changes in performative element of PfM routines are the
drivers of change at PMO level through the ostensive element’s
transformation.
P2.3. Changes in performative element of PfM routines are directly
the driver of change at PMO level.
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First, recurrence of variations in performative element of PfM routine will change the ostensive element (Dionysiou & Tsoukas, 2013). This change will be realized by the portfolio manager or PMO members and will cause a change at PMO level.
Therefore:
Proposition P2.2: Changes in performative element of PfM routines are the driver of change at PMO level through the ostensive element’s transformation.
Second, the variations in the performative el-ement of PfM routine may positively affect single project or portfolio performance. This variation will be realised by PMO members and will direct-ly cause a change at PMO level.
Therefore:
Proposition P2.3: Changes in performative element of PfM routines are directly the driver of change at PMO level.
The intentional and unintentional variation among PfM routine and changes at PMO level are continuous processes. When an unintentional variation in PfM routine is the driver of change at PMO level, PMO members will provide new for-mal procedures or guidelines for PfM. This formal support will be the start of an intentional varia-tion in PfM routines because in the first place not all the PfM routine actors were following the best practice; or the variation may affect more actors of the routine. Because of contextual influences, the actors of PfM routine will adopt new sub-rou-tines to fulfil their duties; and this is again the beginning of an unintentional variation in the routine. By looking at transformation of PfM and PMO through the existing lens (Tsoukas & Chia, 2002), it can be understood that the uninten-tional and intentional variation of PfM routines’ elements are just phases of the co-evolution of PfM and PMO. This co-evolution is an on-going process, which requires both evolutionary and dialectic theories to be considered.
Figure 3 shows the research propositions and the relationships between different components. The arrow between performative element of the PfM routine and the organization shows the participants’ performances in the context of the joint activity at large (Langley et al., 2013). This change on the performative element of the routine constrains the participants’ actions in the future, which may produce change at the organizational level (Weick, 1979).Two paths of variation have
been demonstrated in the research framework. The two paths of intentional and unintentional variations are a part of PMO and PfM change cycle.
By adopting a social constructivist perspec-tive and process ontology, organization is seen as a dynamic bundle of qualities (Langley et al., 2013); therefore, the questions is how processes (routines) evolve over time. The process ontology models the change as a matrix of interrelated processes (Langley et al., 2013).
Overall, this study suggests the dynamic co-evolution of PfM and PMO as a result of contextual influences. PMO helps PfM to better adapt to the contextual changes and in return PfM helps PMO to better understand the re-quirements of single projects and portfolios. This perspective to co-evolution of PMO and PfM justifies the reasons behind the effects of PMO involvement on single project performance (Dai & Wells, 2004; Desouza & Evaristo, 2006), portfolio success (Unger et al., 2012) and organisational performance (Aubry & Hobbs, 2011; Aubry et al., 2007; Aubry et al., 2011; Kendall & Rollins, 2003; Koh & Crawford, 2012; Thomas & Mullaly, 2008).
2. Research Approach Research paradigm
Unit of analysis
The purpose of this exploratory study is to investigate change through the lenses of organi-zational routines, the unit of analysis. By looking at the definitions of organizational routines as the recurrent or repetitive interaction pattern (Becker, 2004; Feldman & Pentland, 2003; Nelson & Winter, 1982) the focus should be on interaction processes. Therefore, a ‘appropriate” ontological and constructivist epistemological perspective of portfolios through social interaction will be suitable (Sergi, 2012).
Research paradigm
Research paradigm is a world view or a set of linked assumptions to investigate the world (Deshpande, 1983). The three elements of a paradigm are ontology, epistemology and meth-odology (Guba & Lincoln, 1994). As methods and techniques are reflected in paradigm, a paradigm
TABLE 3. Research paradigm
Ontology Knowledge claims Main methodology Strategy of inquiry Methods
Process based/
appropriateSocial constructivism
Multilevel Case study
Mixed methods design
Interviews, archival data, focus groups
provides a conceptual framework for values and social beliefs (Kuhn, 1996). In order to investigate the dynamics of PMO in regards to PfM, social constructivism is the proper paradigm.
Ontological perspective
From an ontological perspective, reality con-sists of multiple realities and this reality depends on the interactions between the researcher and the participant (Healy & Perry, 2000).
Process ontology is adequate to study change over time (Langley et al., 2013; Tsoukas & Chia, 2002). As investigating change through the lenses of routines requires the researcher to pay atten-tion to the context (Pentland, 2003a, 2003b), pro-cess ontology is relevant for studying PMO/PfM transformation. The emerging process oriented ontology in project related study perspective (Packendorff, Crevani, & Lindgren, 2014; Sergi, 2012; Vaagaasar & Andersen, 2007) helps this study to investigate the dynamics of PMO/PfM as an on-going transformation.
This study’s process oriented ontological view helps to capture PMO and PfM dynamics. In addition, this study contributes to the emerg-ing project research with a process orientation perspective (Packendorff et al., 2014; Sergi, 2012; Vaagaasar & Andersen, 2007). According to Langley et al. (2013, p. 5) organizational entities are “…temporary instantiations of ongoing pro-cesses…”. Therefore, this study suggests that pro-cess ontology can enable PMO/PfM research to investigate the on-going change. Process ontology helps the research to pay attention to the research context (Packendorff et al., 2014).
The concreteness of ‘being’ ontology (Chia, 1995) is not appropriate to study change over time (Langley et al., 2013); instead, this study follows the appropriate ontology (Tsoukas & Chia, 2002) to investigate dynamics of PMO/PfM evolution.
Epistemological perspective
Social constructivism emphasizes on under-standing the context of the studied phenome-non (Derry, 1999). In social constructivism, the participants’ view of the context is the basis of the research (Creswell, 2003) which helps this study to capture the performative aspect of routines. This constructivism perspective is rooted into the seminal work of Simon (1997), Weick (1979) and Nelson and Winter (1982). Simon (1997) assumes organizations as a human enterprise and questions the rationality of behaviour at a micro level. Weick (1997) assumes organizations as a systematic set of conventions; therefore, the social process is the interlocked behaviour of people to reduce uncertainty. Nelson and Winter (1982) focus on behavioural patterns of firms; in their perspective the repetitive patterns of human behaviour act like genes of an organism and shape its possible behaviour. Looking at organizational routines through behavioural regularity inter-pretation provides the proper infrastructure to study the organizational members’ patterns of behaviour to deal with their everyday challenges. This study will also consider the proper view of strategy-as-practice research which are practices such as organizational resources (Rouleau, 2013); following Mintzberg and Waters (1985), organi-zational processes (routines and capabilities) are
TABLE 4. Comparison of two data analysis lenses
Routine element Recommended methods in former routine researchRecommended methodology in institutional change study
Performative Field observation, archival record (Pentland, 2003a), Multivariate, Historical
Ostensive Interview, focus groups and survey (Pentland, 2003a), Interpretive, Historical
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sources of strategic advantage. This view (prac-tices as organizational resources) looks at strat-egy-as-practice at micro level (Rouleau, 2013). Constructivism is linked to a subjective episte-mology (Healy & Perry, 2000).
Methodology
Thus, this research explores the dynamics of PMO/PfM change and the epistemological sub-jectivism and the constructivism nature of this study requires longitudinal data to observe the change processes over time and mixed methods (Langley et al., 2013), relying on case study, in-depth interviewing, focus groups and instrumen-tal case research (Healy & Perry, 2000). In doing so we follow studies of practices as organizational resources view to strategy-as-practice research, embracing case-studies as the main methodology with routines and capabilities being the unit of analysis (Rouleau, 2013).
The research methodology has been planned to delve into, interpret and make sense of PMO/PfM dynamic co-evolutions. Longitudinal data are required to observe how PfM routines unfold over time and similarly PMOs transformations over time; therefore, mixed methods combining interviews, archival data and field observation will examine the routine processes in depth (Langley et al., 2013). To answer the research questions, empirical case studies will be conducted among several PMOs. Case studies can provide reliable information and can be used for detailed testing of single examples (Flyvbjerg, 2011) and more of-ten contain some narrative elements to approach complexities (Flyvbjerg, 2006). As a single case study cannot contribute to scientific development (Flyvbjerg, 2006), this study will include a few case studies. Flyvbjerg (2011) states that case study can be used in the early stages of research to gener-ate hypotheses but not for hypotheses testing or theory building. Also, case studies contain a bias toward falsification of propositions rather than toward verification (Flyvbjerg, 2006). Therefore, case studies are the proper approach to examine the propositions. The proposed model will be refined based on the findings and the hypotheses will be developed.
Table 3 summarizes the research approach in the research paradigm.
Research design
In addition to Edmondson and McManus’s (2007) theory continuum, this exploratory study
achieves a methodological fit within interme-diate theory. Intermediate theory is positioned between nascent and mature theories and draws from former studies. The goal of data analysis is testing the propositions and mixed methods are proposed for data collection. This study has been designed to delve into, interpret and make sense of PMO/PfM dynamics of co-evolution in three stages.
Stage one: Multiple case studies
Case studies are “the suitable research strate-gy to understand the dynamics of a single setting” (Eisenhardt, 1989, p. 534). Multiple case studies provide a stronger infrastructure for theory build-ing and the results are more generalized; exam-ining the same phenomenon in different samples enables the cases to be compared and contrasted (Eisenhardt & Graebner, 2007). As a result, a multiple case study strategy with mixed methods approach is proper to investigate the research questions.
Case studies can be used for generating and testing theory (Eisenhardt & Graebner, 2007). Theory building happens through the recursive relation between the emerging theory, case data and the extant literature (Eisenhardt, 1989). As this study is extending the existing theories of PMO/PfM transformation, the research ques-tions will be better addressed by theory building. Different sources of data yield different kinds of insights. Case studies are proper for mixed meth-ods research to combine interviews, observations, surveys and archives (Yin, 2009).
Organizational routines are the unit of anal-ysis to investigate the research questions. The recommended research methodology to capture the ostensive element of routine is interviews and focus groups (Pentland, 2003a, 2003b). In addi-tion, Pentland (2003a, 2003b) recommends field observation and examining the archival record to capture the performative element of routines. Document and database analysis is the recom-mended methodology to examine artefacts (Pent-land & Feldman, 2005). Pentland (2003a) argues that surveys can measure the ostensive element of routines and while behavioural observations indicate the performative element; the reason is that people from outside the routine (like superi-ors or researchers) normally describe the ostensive aspect of the routine while actors of the routine describe what they do (the performative aspect).
On the other hand, following Barley (1997) and Suddaby and Greenwood (2009) all the four methodologies of studying institutional change will be applied to capture the transformation of all elements of the PfM routines. The recom-mended data collection methods for each meth-odology are:
ff 2. Interpretive: observation, longitudinal case studies, ethnographic techniques
ff 3. Historical: archival data of primary and secondary sources and retrospective interview.
ff 4. Dialectic: extended case method based on the everyday practices and experiences of individuals, ethnography or participants’ observation.
Table 4 illustrates the one by one comparison of recommended methods of studying routines in former research with the proper methodology of studying routine transformations through the perspective of institutional changes. This com-parison shows that both direction of research recommend the same methods.
Stage two: Quantitative study
As case studies and statistical methods togeth-er can offer a plurality of perspective and support to the achievement of deeper scientific under-standing(Smelser & Baltes, 2001), in this stage the hypotheses will be tested through quantitative approach.
Stage three: Case studies
In order to operationalize the final framework, the final “reality check” of the model on the basis of its use to interpret and make sense of past cases will be done. Re-applying the final model lets the research find a possible richer picture of what has happened in the past in some industries.
3. Concluding commentsThere are limitations that apply to this study.
The paper focuses on the development of a con-ceptual framework and develops an innovative research approach. On this basis, we suggest a multiple case study strategy and a mixed meth-ods approach in three stages, but these empirical stages are outside the scope of this paper. All limitations associated with case studies will apply to these upcoming stages. As a result, general-
ization of the findings will not be possible, but the research conceptual framework and research approach may be applicable in various settings allowing rich dialog and new insights. In addition, the limited number of participants will also limit the ability to infer general theory(ies) for other samples, but once again the understanding of patterns of co-evolution between PMO/PfM may lead to value middle range theories.
Former research has fully focused on PMO typologies, functions, roles, success criteria and the contribution of PMO existence with single project/organizational performance (Aubry et al., 2007; Hobbs & Aubry, 2008, 2010; Petit & Hobbs, 2012). Absence of PMO is associated with projects’ failure (Stanleigh, 2006). There are a few studies (Aubry, Müller, et al., 2010; Hurt & Thom-as, 2009; Pellegrinelli & Garagna, 2009) that have focused on PMO transformation. Do Valle, Da Silvia and Soares (2008) state that because of the evolving concept of PMO there is always enough room for more research. Aubry, Hobbs, and Müller (2010) also recommend future research to analyse PMO transformation through new theoretical perspective. On the other hand PfM, objectives, challenges and success criteria have also been covered in previous studies (Cooper et al., 1997; Martinsuo & Lehtonen, 2007; Mesken-dahl, 2010; Teller et al., 2012; Turner, 2009) on the basis of being a view.
Taking a routine perspective and a process and appropriate view we offer a box changing ex-ploratory study making two main contributions, firstly in offering a multi-level conceptual frame-work unveiling the dynamics of co-evolution between PfM practices and PMO as organization-al / organizing entities; and secondly an assump-tion challenging approach based on a process and suitable ontology, supporting a somehow innova-tive research, involving a multilevel or tall view linking local praxis level, i.e. PfM routines, with higher level of structure and system, i.e. PMO. A third contribution should be mentioned here: the research approach including the research para-digm and related research design. As said above, there is little empirical research on routines as the unit of analysis to study change in an organi-zational setting (Becker, 2004, 2008; Becker et al., 2005) and this research will contribute to test an innovative research approach.
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authors
r Stephane Tywoniak is the MBA Director at Queensland University of Technology (QUT, Brisbane, Australia). He is a founding member and former aca-demic director of the Execu-
tive Master in Complex Program Leadership at QUT. He has extensive experience in executive education and consulting with major project-based organizations. His research focuses on the strategic inter-actions between business and society in complex projects/programs and strategic decision making, with a specific interest on how business organizations gain, maintain, and lose legitimacy. He has been published in Organization, International Journal of Project Management, and other international journals. He serves as the associate editor for project management on the editorial board of Systems Research and Behavioral Science.
r Mahshid Tootoonchy received the B.S. degree in electrical engineering from Tehran University, Iran, the M.B.A. degree from Sharif University Technology, Iran, and M.A. Degree in
Management from Queensland University of Technology, Australia. She is currently a PhD student in project management at Queensland University of Technology, Australia. She worked as instrument and control engineer and also project manager at several companies in Iran and Australia. Her research interests include project/portfolio management.
r Christophe N. Bredillet is the Scientific Director, Société française pour l¹avancement du Man-agement de Projet (SMAP) and adjunct Professor at Queensland University of Technology’ (QUT) Project
Management Academy. He specializes in the fields of Portfolio, Program & Project Management (P3M). From 2012 to 2015, he was the Director of the QUT Project Management Academy. Before joining QUT, he was Senior Consultant World Bank and, from 1992 to 2010, he was the Dean of Postgraduate Programs and Professor of Strategic Management and P3M at ESC Lille. His main interests and research activities are in the field of Philosophy of Science and Practice in P3M, including dynamic of evolution of the field, bodies of knowledge, standards, and their link with capability development, capacity building, governance and performance. He was Ex-ecutive Editor of Project Management Jour-nal® between 2004 and 2012. In 2012, he received the prestigious Manfred Saynish Foundation for Project Management (MSPM) – Project Management Innovation Award for his contribution to a philosophy of science with respect to complex project management.
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KEYWORDS f schedule risk analysis f Monte-Carlo simulation f change impact analysis
OVERVIEW ARTICLE
r A B S T R A C T
The purpose of this paper is to give an overview on the existing literature and recent developments on the research on Schedule Risk
Analysis (SRA) in Project Management (PM) to measure the sensitivity of activities and resources in the project network. SRA is a tech-
nique that relies on Monte-Carlo simulation runs to analyze the impact of changes in activity durations and costs on the overall project
time and cost objectives. First, the paper gives an overview of the most commonly known sensitivity metrics from literature that are
widely used by PM software tools to measure the time and cost sensitivity of activities as well as sensitivity for project resources. Sec-
ond, the relevance of these metrics in an integrated project control setting is discussed based on some recent research studies. Finally, a
short discussion on the challenges for future research is given. All sections in this paper are based on research studies done in the past
for which references will be given throughout the manuscript.
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ject forecasting and control setting. In section 3, the main challenges for future research are highlighted and section 4 draws overall conclusions.
1. Schedule Risk Analysis Schedule Risk Analysis is a Project Management meth-
odology to assess the risk of the baseline schedule and to forecast the impact of time and budget deviations on the project objectives. It can be easily performed on a computer using standard Monte-Carlo simulation runs based on user input on the uncertainty in activity durations and/or costs. The approach is described in various literature sources (see e.g. Hulett (1996)) and consists of a four step procedure that is displayed in figure 1 and can be summarized as follows:
ff Step 1. Baseline schedule: The project baseline schedule consists of a timetable for each project activity and plays a central role in any project simulation study since it acts as a point of reference for all calculations done during the simulation runs (step 3). It provides information about the expected time and cost of a project and start and finish times of activities, as well as the use of the various types of over time resources.
ff Step 2. Define uncertainty: While the time and cost estimates for the baseline schedule assume deterministic values, real project progress, however, is flavoured with uncertainty, leading to unexpected changes and problematic time and cost overruns. This behaviour must be mimicked in a Monte-Carlo simulation by defining distributions on the unknown time/cost parameters.
ff Step 3. Simulation: During the Monte-Carlo simulation runs, the stochastic values are generated from the predefined distributions of the previous step to reflect the real uncertainty in the estimates. In each run, the project has a different duration and cost and a different critical path, and the simulation
r Mario VanhouckeGhent University (Belgium) Vlerick Business School (Belgium) University College London (UK)
Integrated Project Management and Control is a Project Management (PM) concept to refer to the necessary integration of various quantitative techniques to improve the performance of the control process of the project during its progress. It requires a sound methodology for the construction of the project baseline schedule that acts as a point of reference for two other essential phases in the management of a project. One of these phases is done prior to the start of the project to assess the risk inherently embedded in the baseline schedule using a tech-nique known as Schedule Risk Analysis (SRA) (Hulett, 1996). The other phase
is known as project control and is per-formed at periodic intervals during the project progress using Earned Value Management (EVM) (Fleming and Koppelman, 2010) or Earned Schedule (ES) (Lipke, 2003) calculations (further abbreviated as EVM/ES). This inte-gration between the construction of the baseline schedule, the analysis of risk using SRA and the project con-trol phase using EVM/ES is known in the literature as Dynamic Scheduling (Uyttewaal, 2005; Vanhoucke, 2012) and is recently referred to as Integrat-ed Project Management and Control (Vanhoucke, 2014).
In this paper, the focus lies on the relevance and use of the SRA method
for improving the quality and effi-ciency of the project control process. More specifically, the focus lies on the formulas of various metrics to measure the time and cost sensitivity of project activities and the renewable resources used by these activities. Most of the work is based on research published in academic literature, for which referenc-es will be given throughout the text.
The outline of this paper is as follows. Section 1 gives an overview of the most commonly known sensitivity metrics to measure the time and cost sensitivity of project activities as well as the sensitivity of their renewable resources. Section 2 provides a discus-sion of their use and relevance in a pro-
ON THE USE OFSCHEDULE RISK
ANALYSIS
FIGURE 1. The 4 step procedure of SRA (Source: Vanhoucke (2012))
FOR PROJECTMANAGEMENT
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OVERVIEW ARTICLE /// ON THE USE OF SCHEDULE RISK ANALYSIS FOR PROJECT MANAGEMENT
engine stores all possible data in its memory to calculate sensitivity metrics after the simulation process is finished.
ff Step 4. Sensitivity output: The data captured during the simulation runs is now ready to be processed, and sensitivity metrics on time and cost behaviour for individual activities and resources can be calculated. The calculations of these metrics are discussed in the next section of this paper.
The importance of analyzing the risk of a baseline schedule comes from the need of any project manager to restrict his/her attention to the most influential activities of the project that might have the biggest impact on the initial time and cost constraints. It enables them to have a better management focus and it supports a more accurate response during project progress that positively contributes to the overall project performance (Vanhoucke, 2010a).
The metrics
This section gives an overview of four commonly used and well-known sensitivity metrics obtained from a SRA. The schedule risk metrics contain relevant information that can be used to assess the quality of the risk predictions and to monitor and control the performance of a project. This paper will use these four metrics for the following three purposes:
ff Time risk analysis: Expected impact of activity duration changes on the total project duration
ff Cost risk analysis: Expected impact of activity cost changes on the total project cost
ff Resource risk analysis: Expected impact of resource use disruptions on the total project cost
It should be noted that this paper has no intention on providing an overview on the literature and existing tech-niques on risk management in projects, but instead only provides insights into the use of four well-known SRA met-rics in a project control setting. Much of the work presented in the following subsections dates back to the relatively old but still very relevant work from Williams (1992) and Williams (1995) who has presented some of the metrics to measure criticality in stochastic project networks and has provided a classified bibliography of project risk manage-ment. One metric is proposed in PMBOK (2004). This work was presented in previously mentioned references and has been used in a SRA validation study (Vanhoucke, 2010b) and in a project control efficiency study of (Vanhoucke, 2011) for which parts were embedded in the books by Vanhoucke (2010a, 2012, 2014). However, this restricted focus on the four risk metrics does not mean that no other work has been published in the literature. Extensions to other risk metrics or more advanced risk analysis methods are available in the literature, but will not be discussed in the current paper. A short yet incomplete discussion on extensions of the four risk metrics used in this paper is given in the “critical view on sensitivity measures” section of Vanhoucke (2010a).
Time
Schedule Risk Analysis metrics for time risk analysis refine the black-and-white view of the critical path (which defines that an activity is either critical or not) to a degree of criticality/sensitivity as a percentage between 0% and 100%. Each metric gives an indication of how sensitive the activ-ity is towards the final project duration as defined by the sensitivity metric. Apart from the sensitivity metric values, an SRA sensitivity scan also shows the probability that the project reaches a certain deadline, expressed in a cumulative project duration graph, which will not be further discussed in this paper. The four metrics that are often used for meas-uring the time sensitivity of project activities are as follows:
ff Criticality Index (CI): Measures the probability that an activity is on the critical path.
ff Significance Index (SI): Measures the relative importance of an activity.
ff Schedule Sensitivity Index (SSI): Measures the relative importance of an activity taking the CI into account.
ff Cruciality Index (CRI): Measures the correlation between the activity duration and the total project duration, in three different ways:
ff CRI(τ): Kendall’s tau rank correlation coefficient.
Cost
In many practical settings, uncertainty in activity du-rations also has an influence on the (variable) cost of the activity. Unlike the time sensitivity metrics CI, SI and SSI, the cost sensitivity cannot be measured by network or crit-ical path analyses, and hence only the cruciality index can be used for measuring this sensitivity. The three versions of the Cruciality Index, CRI(r), CRI(ρ) and CRI(τ) are valuable alternatives since they measure correlations between two variables and do not require a project network. Rather than measuring correlations between activity durations and total project duration, they now measure the correlation between the activity cost and the total project cost (known as Budget at Completion (BAC)) based on all data obtained from the various runs in the simulation.
Resources
Uncertainty in activity durations has an influence on the resource costs of the activity. Activities require resources and therefore the total activity cost can consist of various parts, including the fixed or variable costs for the renew-able resources connected to these activities. A renewable resource is defined as a resource that has a strict limit at each period of the project horizon, but it is not consumed by activities and hence its limited availability is ‘renewed’ every period. A typical example is the use of people but machines,
cranes and limited space such as dockyards are also renewable resources. Rather than measuring the total cost sensitivity of an activity as shown in the previous section, it is often interesting how sensitive each resource is with respect to the global project budget. The way resources are connected to activities and how their costs are calculated might differ from project to project, but a general overview is given in the resource chapter of Van-houcke’s book (2012). Recently, an overview of past experiences on the use and importance of renew-able resource scheduling on real data is given in a paper published in the journal of Modern Project Management (Vanhoucke, 2013). In this paper, we will not discuss these detailed issues any further as they do not add fundamental insights to the resource sensitivity metrics presented here. Similar to the general activity cost, the resource cost sen-sitivity can be measured by the three versions of the Cruciality Index, CRI(r), CRI(ρ) and CRI(τ), but they will now be calculated for each type of renew-able resource rather than for each project activity.
The formulas
Each sensitivity metric is given as a value bounded between two extremes (0 or 1 for the SI, CI and SSI and -1 and +1 for the CRI) for each pro-ject activity or resource, obtained after the Mon-te-Carlo runs of a simulation engine available in software tools. The simplicity of such tools results in an intensive use by project managers, often with-out much knowledge of the underlying technique and the formulas of these metrics. However, it is my firm belief that some basic knowledge of the formulas helps in understanding the difference in meaning between each metric. More important than the formulas and the calculations however, is to understand their relevance and their potential use in controlling projects. In the next subsections, the formulas of the metrics are shown in detail, while a discussion on their relevance for project control is made in section 3.
Figure 2 shows an illustrative SRA report for an artificial project made by the ProTrack software tool and shows the time and cost sensitivity for all project activities (no resources are taken into ac-count). The project has a serial/parallel value of 50%
FIGURE 2. Illustrative time/cost schedule risk report for activities of an artificial project of 17 activities (Source: Vanhoucke (2014))
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as measured by the SP indicator1. This indicator measures the structure of the network and has a major influence on the time sensitivity indices. Up to date, no research has been done to test whether this indicator also impacts the accuracy of cost sensitivity. It is conjectured by the author that this influence will not be detected for cost sensitivity, since cost is rarely related to the topological structure of a network. This conjecture is confirmed by an empirical study on a set of 52 projects in Batselier and Vanhoucke (2014).
Criticality Index (CI)
The Criticality Index is probably the most straightfor-ward and intuitive metric and expands on the concept of the criticality of an activity in a project network. The construc-tion of a project baseline schedule results in a critical path, and each project activity is either critical (i.e. it lies on the critical path) or not (i.e. it has a positive value for its slack). This black-and-white view suffers from simplicity since each non-critical activity has the potential to become critical once the project is in progress, and therefore, a more refined metric could give much more information. Therefore, the Criticality Index measures the probability that an activity lies on the critical path. It is a simple measure expressed as a percentage denoting the likelihood of being critical.
Although the Criticality Index has been used throughout various studies and implemented in many software tools, the CI often fails in adequately measuring the project risk. The main drawback of the CI is that its focus is restricted to measuring probability, which does not necessarily mean that high CI activities have a high impact on the total project du-ration (e.g. think of a very low duration of an activity always lying on the critical path, but with a low impact on the total project duration due to its negligible duration).
Significance Index (SI)
In order to better reflect the relative importance between project activities, the Sensitivity Index of a project activity has been proposed as an alternative to and an extension of the CI, and can be calculated as follows:
with E(x) used to denote the expected value of x. The SI has been defined as a partial answer to the criticism on the CI. Rather than expressing an activity’s criticality by means of the probability concept, the SI aims at exposing the signif-icance of individual activities on the total project duration. In some examples, the SI seems to provide more acceptable information on the relative importance of activities. Despite this observation, there are still examples where counter-in-
1 This indicator is initially proposed as the I2 indicator by Vanhoucke et al. (2008) and is later renamed to SP by Vandevoorde and Vanhoucke (2006).
tuitive results are reported and the reader is referred to examples and a critical view in Vanhoucke (2010a).
Schedule Sensitivity Index (SSI)
The Project Management Body Of Knowledge (PMBOK) mentions quantitative risk analysis as one of many risk assessment methods, and proposes to combine the activity duration and project duration standard deviations (StDevAc-tivityDuration and StDevProjectDuration) with the Criti-cality Index. The Schedule Sensitivity Index is calculated as follows:
In the study of Vanhoucke (2010b), the quality of the 4 metrics for measuring the activity time sensitivity has been compared and benchmarked using a simulation study. The quality of the metrics has been measured by their ability to make a distinction between project activities with a low expected impact on the total project duration and activities with a high expected impact. The results show that the SSI outperforms on average all other metrics for activity time risk analysis. To the best of my knowledge, no such infor-mation based on computational experiments is available for activity or resource cost sensitivity metrics.
Cruciality Index (CRI)
The cruciality index is somewhat different than the three previous metrics and is therefore much more general in its use. As previously mentioned, the CI, SI and SSI metrics are inherently linked to the project network structure and can therefore be used to calculate the impact of changes in the duration of activities on the total duration, using the concepts of the activity slack and the critical path. The CRI simply measures correlations between two variables and does not explicitly use the network structure in its calcu-lations. Therefore, the CRI can measure both the time and cost sensitivity of individual activities as well as the cost sensitivity of the renewable resources used by the activities. Obviously, the variables used by the CRI differ for time ver-sus cost sensitivity calculations as well as for activity versus resource sensitivity calculations. More precisely, the activity time sensitivity CRI requires the activity duration and the total project duration as input values to calculate correla-tions. Likewise, the activity cost and resource cost sensitivity can be measured by an alternative version of the cruciality index where the duration parameters are replaced by the cost parameters. Consequently, the cruciality index can be calculated as follows:
CRI = |correlation(ActivityDuration, ProjectDuration)| for activity time sensitivity
CRI = |correlation(ActivityCost, ProjectCost)| for activity cost sensitivity
CRI = |correlation(ResourceCost, ProjectCost)| for resource cost sensitivity
These metrics reflect the relative importance of an activ-ity in an intuitive way as the portion of uncertainty in the outcome variable (total project duration or total project cost) that can be explained by the uncertainty in an activity or resource. Three versions of this correlation metric are used in literature as discussed along the following lines.
Pearson’s product-moment CRI(r) is a traditional meas-ure of the degree of linear relationship between two varia-bles. The correlation is 1 in the case of a clear positive linear relationship, -1 in the case of a clear negative linear relation-ship, and some value in between in all other cases, indicating the degree of linear dependence between the activity dura-tion and the total project duration. The closer the coefficient to either -1 or 1, the stronger the correlation between these two variables.
However, the relation between an activity duration and the total project duration often follows a non-linear relation. Therefore, non-linear correlation metrics such as the Spear-man rank correlation coefficient or Kendall’s tau metric can also be easily calculated on the same data. These two corre-lation metrics can be computed as follows:
Spearman’s rank correlation CRI(ρ) (rho) assumes that the values for the variables (i.e. activity durations and project durations) are converted to ranks, followed by the calcula-tion of the difference between the ranks of each observation on the two variables. The metric is a so-called non-paramet-ric measure to deal with situations where the strict statis-tical assumptions of the parametric CRI(r) metric are not met. The CRI(ρ) metric has a similar meaning to the CRI(r) metric, i.e. −1 ≤ CRI(ρ) ≤ 1.
Kendall’s tau rank correlation CRI(τ) (tau) index meas-ures the degree of correspondence between two rankings and assesses the significance of this correspondence. This nonparametric metric has a similar meaning to the CRI(r) metric, i.e. −1 ≤ CRI(τ) ≤ 1.
2. Relevance It goes without saying that any project manager who re-
lies on Monte-Carlo simulations to analyze the project’s risk should be careful with the sensitivity information obtained from these runs. As previously mentioned, an activity/re-source time/cost sensitivity scan gives information about the potential effect of uncertainty on the final project duration or cost, but since all metrics potentially differ in value even for the same activity or resource, it is often hard to interpret the results and understand their value in a real-life setting. Hence, it is important to correctly interpret these values for your project, to recognize the weaknesses but also to appre-ciate and fully exploit their merits for project management
and control in order to better support decisions for projects in progress.
Pitfalls
All metrics discussed in this paper are the result of a Monte-Carlo simulation which is a well-known and validat-ed technique but suffers from the garbage-in garbage-out problem2. Hence, a clever choice of the input parameters to define the distributions on activity durations is key to the validity of the obtained values for the metrics (Williams, 1999). A complete overview of activity duration distributions that are often used in the academic literature is outside the scope of this paper. Recent research has suggested the use of generalized beta distributions (Kuhl et al., 2007), log-normal distribution (Mohan et al., 2007), a combined beta and uniform distribution (Hahn, 2008), a double truncated normal distribution (Kotiah and Wallace, 1973) as well as the Parkinson distribution with lognormal core (Trietsch et al., 2012).
While the Monte-Carlo simulation technique often pro-vides accurate and useful results in a research setting per-formed under a controlled design, simulation results used in a real setting might be affected by case-specific settings. An illustrative and common example is the use of calendars specified in the agenda such that small delays in activity du-rations might lead to larger project delays in case the delay spans a weekend or a holiday period, resulting in bias values for the metrics. Another typical example is the occurrence of activity constraints (due dates or ready times) that force activities to start or finish not earlier or later than a specified time, which leads to infeasibilities during the simulation due to the violation of some of these constraints.
Probably a more important pitfall is the lack of incorpo-rating constraints on resources while using SRA. Indeed, most simulation studies are based on a simple baseline schedule in which the limited availability of renewable re-sources is completely ignored. Instead, the simulation mostly starts with the generation of an earliest start schedule (ESS) for all project activities in which each activity is scheduled at its earliest possible starting time, given the logic of the project network. However, if some activities are delayed due to specific reasons, or due to the unavailability of resources at certain moments in time, the concept of the critical path sometimes gets a completely new meaning3 and the metrics often do not measure exactly what they initially represent. As an example, the criticality index will often report zero values for many activities since they do not lie in any of the simulation runs on the critical path, but instead are shifted further in time due to resource constraints.
2 It should be said that most, if not all, techniques used in management suffer from this principle and hence care should be given to the data input process.
3 In case all so-called resource conflicts are resolved by shifting activities in time, the longest path is then known as the critical chain and is based on the logic of the network as well as on the availability of the resources.
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Merits
Despite shortcoming and pitfalls, the simple and elegant SRA technique has been proven useful in various settings for its ability to improve the forecasting accuracy of project outcome variables as well as for taking corrective actions more efficiently during project performance measurement and control. These merits are discussed along the following paragraphs.
Forecasting
Traditionally, duration and cost forecasting is done using EVM/ES forecasting metrics such as the Estimate At Completion metrics EAC (for cost) and EAC(t) (for time). In Vandevoorde and Vanhoucke (2006), the time forecasting techniques have been split up in three classes, known as the Planned Value (Anbari, 2003), the Earned Duration (Ja-cob, 2003; Jacob and Kane, 2004) and the Earned Schedule (Lipke, 2003) methods. Each of these classes can be used under various settings according to the assumptions made about the unknown future performance of the project (this assumption must be made by the project manager and is known as the performance factor of the forecasting method). The nine different forecasting formulas have been used and validated in a simulation study published in Vanhoucke and Vandevoorde (2007) and Vanhoucke (2010a). Similar formulas exist for cost forecasting, and in Vanhoucke (2014), eight different methods have been presented, based on the original work of various authors such as Christensen (1993) and Zwikael et al. (2000).
None of these time and cost methods rely on SRA to pre-dict the future, and to the best of our knowledge, literature had to wait until the paper written by Elshaer (2013) before the SRA technique had been used and integrated with the previously mentioned forecasting methods to improve the overall time/cost forecasting accuracy. The integrated fore-casting system of this author combines the EAC(t) formulas with SRA metrics to improve the predictive power of the forecasting methods. The author starts with the observation of Vanhoucke and Vandevoorde (2007) who have shown that the traditional forecasting methods perform much better for serial network than for parallel networks. The main reason for this observation is that false warning signals caused by non-critical activities (which occur more in parallel network relative to serial networks) bias the predictions and lead to a lower accuracy. However, the SRA metrics show exact-ly the opposite behaviour, and are much more reliable for more parallel structured networks in comparison with the networks with a more serial structureVanhoucke (2010a). Based on these observations, Elshaer (2013) has combined the SRA and EVM/ES techniques into a single integrated system, hereby trying to use the best of both techniques decreasing the false warning effects caused by the non-crit-ical activities. In doing so, he proposed a system using the four previously mentioned metrics CI, SI, SSI and CRI as weights in the original EAC(t) formulas, hereby improving
the performance of the earned schedule method in predict-ing the final project’s duration, regardless of the topological structure of the project network.
3.2.2 Project control: corrective actions
While time and cost forecasting is undoubtedly a crucial step in the control of a project in progress, it is mostly relevant when it can be used to trigger actions by the pro-ject manager to bring projects in danger back on track or alternatively, to exploit opportunities of projects performing better than expected. Consequently, the ultimate goal of project control is not performance measuring nor forecast-ing but taking corrective actions in an efficient and effective way to deliver project on time and within budget.
In a paper written by Vanhoucke (2011), two alternative control methods have been proposed. The top-down control method is based on EVM/ES project performance data that are used as early warning signals and triggers for the need for corrective actions. In case the data points indicate a cer-tain deviation from the expected performance, it should lead to a drill-down in the work breakdown structure to search for the underlying reasons of this unexpected behaviour. To that purpose, a threshold should be set that indicates a significant deviation from the desired project performance based on manual and/or statistical methods (Colin and Vanhoucke, 2014).
The alternative bottom-up control method is more rele-vant for the current paper since it relies on SRA data instead of EVM/ES data to report variations from expectations that trigger actions. In a bottom-up control method using SRA metrics, the detection of sensitivity information is crucial to steer a project manager’s attention towards the most sensitive parts of the project. These highly sensitive activities should then be the subject of intensive control since they are expected to have an immediate impact on the project time/cost objectives. Other less sensitive activities require less or no attention during project execution. Consequently, the metrics presented in this paper can play a crucial role in efficiently taking corrective actions since they define thresholds, similar to the EVM/ES top down thresholds, that trigger actions once exceeded. The previously mentioned outstanding performance of the SSI on projects with a more parallel structure has been observed in computational stud-ies using this bottom-up project control method.
3. Challenges Cost control
The previously mentioned bottom-up project control study has solely focused on the efficiency of the control process for monitoring the final duration of the project, and no attempt has been done whatsoever to set up a similar
study for (activity or resource) cost control. A straightfor-ward extension and therefore future research challenge lies in measuring the ability of the cost sensitivity metric CRI for project cost control and its potential beneficial effect it might have in comparison with the traditional EAC top-down project control using EVM/ES. While this extension might sound like a simple copy-paste study of the time study of Vanhoucke (2011), it probably is more complex due to the lack of structure in the cost increase of the project compared to the strong project network structured link of time fore-casting and control. Therefore, other drivers than the serial/parallel (SP) indicator should be found and/or developed to measure the behaviour of cost control using SRA.
Resource constraints
It has been previously mentioned that very practical features such as activity constraints or calendars could lead to unreliable or strange results. While it is practically impossible and probably undesirable to incorporate every case-specific detail in a simulation study, the extension to resource-constrained simulation is so crucial and obvious that it can no longer be ignored. Resource-constrained baseline scheduling has been investigated widely in the literature (for an overview, see e.g. Hartmann and Briskorn (2010) and Vanhoucke (2012)) and has led to thousands of papers with algorithms and methods to construct a resource feasible baseline schedule under certain predefined assump-tions. However, the use of metrics obtained from schedule risk analysis is to the best of our knowledge completely void.
Again, this extension is challenging since the straightfor-ward use of Monte-Carlo simulations that generate multiple runs with resource-feasible schedules is easy to implement, but results in biased, unreliable and often meaningless val-ues for the four indicators mentioned in this paper.
Big data
Given the recent evolutions in data science and cloud methodologies, the extension to big data analysis is an obvious step to take. Certainly when big data is seen as a set of methodologies that can now be performed on a large amount of data in a reasonable amount of time, this evo-lution cannot go unnoticed in schedule risk analysis and project control. In a recent paper written by Alleman and Coonce (2014), an approach to forecast the time and cost of projects using analysis of trends, cost and schedule forecasts, and Autoregressive Integrated Moving Average (ARIMA) algorithms (provided by the R programming system) have been proposed as big data meets EVM research presented at the ICEAA 2014 Workshop in Denver Colorado (US). Prob-ably the most promising use of large amounts of data lies in the use of data science and artificial intelligence methods to analyse historical and/or simulated data to improve the accuracy of risk and control methods in PM. While many of these techniques are often easy to implement on large datasets, the translation of a project management setting requires research and testing and is therefore a promising future challenge.
FIGURE 3. An overview of the relevance and challenges of Schedule Risk Analysis metrics in Project Management
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refe
renc
es
author
r Mario Vanhoucke is Full Professor of Business Management and Operations Research at Ghent University (Belgium), Vlerick Business School (Belgium, Russia, China) and University College London (UK). He has a Ph.D. degree in Operations Management from the University of Leuven (Belgium). At Ghent University, he is the program
director of the Business Engineering program where he teaches Project Management and Applied Operations Research. At Vlerick
Business School, he teaches Decision Making for Business and Dynamic Project Planning to Master and MBA students. He has given lectures at various universities and management schools in Europe, Asia and the US and has published more than 50 papers in various international journals and is the author of three project management books published by Springer (see www.or-as.be/bookstore). Mario Vanhoucke is also a founding member and director of the EVM Europe Association (www.evm-europe.eu) and partner of OR-AS (www.or-as.be).
In the recent years, these methods have gradually found their way into the project control research. As an example, in the study for the research published in Vanhoucke (2010a), terabytes of data have been generated using the Flemish Supercomputer Center4 before any analysis could be done. Other examples of huge statistical data analysis and the use of artificial intelligence in project control are, for example, the use of statistical methods (Colin and Vanhoucke, 2014) for statistical project control, support vector machines for the accuracy of EVM/ES forecasting (Wauters and Vanhoucke, 2014)) and the Kalman filter of project duration predictions (Kim and Reinschmidt, 2010). However, in contrast to project control research, not much work on the use of big data meth-odologies in schedule risk analysis has been done. A search on studies in project risk management using quantitative methods, however, results in various papers that make use of techniques such as reference class forecasting (e.g. (Flyvb-jerg, 2006)), traditional statistical methods (e.g. (Wang and Huang, 2000)) or Bayesian statistics (Khodakarami and Abdi, 2014), and it is therefore conjectured that future research will probably extend these methods to big data analysis and artifi-cial intelligence techniques, hopefully resulting in increased knowledge on this interesting topic.
4. Conclusions In this paper, an overview of recent research on schedule
risk analysis is given using four well-known and easy to use time/cost sensitivity metrics. Rather than giving a full over-view of the literature, the paper focuses on the calculations of the metrics (to understand what they exactly mean), on their use and relevance for project management and control and on the main challenges for future research. An overview picture is given in figure 3 and is briefly summarized along the following lines.
Both time and cost sensitivity metrics have been pre-sented that are widely used by project managers and their software tools. While the time sensitivity can be measured by well-known metrics such as the CI, SI and SSI, the cost sensitivity measurement for activities and resources is re-stricted to the CRI metric.
4 More information on this Flemish Supercomputer Center (VSC, Flemish = Vlaams) is available at http://www.ugent.be/hpc/en.
Moreover, the paper refers to studies in which the four sensitivity metrics have been tested on their usefulness to improve the forecasting accuracy of projects in progress, as well as to efficiently control projects using the so-called bottom-up method. In doing so, it has been shown that simple metrics are able to act as identifiers for sensitive parts in projects, and their distinctive power between insensitive and sensitive activities enables the project manager to more efficiently control projects in progress. From some of the re-search mentioned in the paper, it is known that these metrics can be best used for projects with a structure that resembles a parallel structure than a serial structure. Moreover, the best performing metric is currently known as the Schedule Sensitivity Index and is praised for its high discriminating between low and high sensitivity for project networks com-pared to the others. In a recent study in 2013, the combined use of EVM/ES and SRA metrics has led to an integrated approach which outperforms all separate approaches on the forecasting accuracy.
Strengths and weaknesses of the use of these metrics are described and future research avenues are highlighted as major challenges for academics to further improve the current state of knowledge in this domain. The main re-striction of computational experiments on time control has been mentioned and a call for more attention on cost control should further improve our knowledge on the main drivers of accuracy. Moreover, the constraints on resource availabilities that is well considered during scheduling but largely ignored using SRA is a second possible future research avenue. Fi-nally, the obvious extension to big data analysis and artificial intelligence might lead to new insights and overall improve-ments.
As previously mentioned, this paper does not serve as a literature overview on project risk management. Instead, it should be seen as only a small subpart in project risk man-agement, and many other excellent papers have been pub-lished in this domain that use other often more elaborate techniques to analyze and assess the inherent risk of projects.
Acknowledgements
The support by the concerted research action (CRA) funding received in 2012 at Ghent University (Belgium) for the project titled “Searching for static and dynamic project drivers to predict and control the impact of management/contingency reserve on a project’s success” and the National Bank of Belgium is acknowledged.
Uncertainty and complexity; shorter and shorter lifecycles; frequent coexistence of evolution and innovation of rupture; a greater need for
collaboration at different levels; more and more standardized and subject to
greater traceability; increase and flexibility of the products’ portfolio, services
and customers; increasing consolidation of the lean and agile culture; and
finally, a broadening of risk sharing make survival of enterprises more defiant.
This means that to enable the projects’ success, the approach to the problems
should be more aligned with these changes in the business environment.
Organizations must also manage and measure otherwise, which affects the
whole supply chains and creates new opportunities for added value in projects.
Modern project management requires the design and implementation of a
combination of strategies, methodologies and tools to ensure the deliveries
according to the changing needs of customers.
ThemeMODERN PROJECTMANAGEMENT• Management of the complexity of the
project• Strategy and project management• Project management risk• Design and management of sustainable
projects • Quality and standards in projects• Analysis and decision making in projects• Management and cost measurement in
projects• Governance in projects• Portfolio management • Performance indicators in projects• Management of Lean and agile in
projects• Project management maturity • System engineering and project mana-
gement
ThemeMANAGEMENT OF AERONAUTICAL PROJECTS• Aeronautical projects
and standards
• Strategy and management of maintenance projects & MROs
• Traceability in aeronautical projects
• Project management of products and aviation services and additive manufacturing
• Management of innovative projects in the aeronautical industry
• Aeronautical supply chain project management
• Aeronautical project configuration management
Theme PROJECT MANAGEMENT OFFICE (PMO)• Methodology for
implementation of Office Project
• Tools for Office Project
• Performance Indicators for Office Project
• Organisation of Office Project
• Office Project Interface with functional services
• Case studies (implementation) for Office Project
Theme PRODUCT LIFECYCLE MANAGEMENT (PLM)• PLM strategies and
methodologies
• PLM Project
• PLM tools
• PLM and strategic management project
• PLM and logistic chain management
• The cost of life cycle management (total cost of ownership) in project
• PLM and DSM (Design Structure Matrix)
• PLM Tool Performance
• PLM choice of project
THEMES can also focus on the concepts, methodologies, methods and research tools in project management, with relation to the other disciplines of management sciences.
AUTHORS are invited to submit a 2-page abstract (in English or French) on their topic no later than October 31, 2014 to Professor Alejandro Romero.
FINAL ABSTRACTS can be submitted in English or French. Final communications may be submitted in English or French.
THE BEST PRESENTATIONS at the conference will be published in the Journal of Modern Project Management: www.journalmodernpm.com
MODERN PROJECT MANAGEMENT:
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Université du Québec à Trois-Rivières (UQTR, Canada) : May 28 & 29, 2015www.uqtr.ca/conference/gestionprojet
MAJOR THEMES:MODERN PROJECT MANAGEMENTMANAGEMENT OF AERONAUTICAL PROJECTSPROJECT MANAGEMENT OFFICE (PMO)PRODUCT LIFECYCLE MANAGEMENT (PLM)
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Recently, I was investigating the relation between project management problems (i.e. any kind of project or-ganizing questions (e.g. Puranam et al, 2014)), competent project managers and ethics (Bredillet, 2014; Bredillet et al., 2015). I argued that deontological ethic (“what ought be”, doing “right”, i.e. using “ the right means”) and conse-quentialist ethic (“right” outcome, i.e. focusing on the best possible “end”) perspectives were not sufficient in order to fully support project managers in their problem solving and decision making processes. I suggested shifting to Aristotelian ethics of character and practical philosophy, acknowledging the role of phrónêsis, i.e. practical wis-dom, in order to fully equip competent project managers in their ability to cope with project problems, where the particular always takes precedence over the general.
A good illustration of “tough” problems to be ad-dressed and involving practical wisdom are the so-called “wicked problems”. This concept, like usually for such umbrella constructs (Floyd et al., 2011, Rouleau, 2013) and buzz words part of the management fashion (Abra-hamson, 1996), is worthwhile discussing in order to fully grasp the managerial consequences attached to its under-standing.
1. Defining “wicked problems”If we take Rittel’s definition as exposed in Churchman
(1967, p. 141), the concept of “wicked problem” refers to a class of social system problems which are ill-formulated, where the information is confusing, where there are many clients and decision makers with conflicting values, and where the ramifications in the whole systems are thor-oughly confusing”.
They are “wicked” because they resist to solutions. They are “difficult or impossible to solve because of in-complete, contradictory, and changing requirements that are often difficult to recognize”. Conklin (2006) defines their characteristics as follow:
ff The problem is not understood until after the formulation of a solution.
ff Wicked problems have no stopping rule.
ff Solutions to wicked problems are not right or wrong.
ff Every wicked problem is essentially novel and unique.
ff Every solution to a wicked problem is a ‘one shot operation.’
ff Wicked problems have no given alternative solutions.
(A)Musing… Wicked problems and project management
2. Projects and “wickedness”Classically wicked problems occur in any socio-organ-
izational system and “chaordic” environment (Hock, 1995) where the organizing context shows increasing volatility, uncertainty, complexity, and ambiguity (VUCA) affect-ing organizations and the socio-economic environment. These wicked problems can emerge at any time (Taleb, 2007) as, by nature, they are not foreseeable. You know they will happen but you can’t predict which form they will take (a bit like influenza pandemic or natural haz-ards).
Every project has its part of “wickedness” as each project involves some uniqueness and novelty. However projects embedded in particular “VUCA” context and environment are good candidates – for instance projects linked to political & societal contexts and involving mul-tiple stakeholders with divergent interest (Flyvbjerg, 2014) such as international development projects (e.g. Dams), or major infrastructures (e.g. Channel Tunnel, Olympic Games), or social reform systems (e.g. Obamacare), or major acquisitions in the Defence sector (see Joint Strike Fighter), or any organizational change including mergers or acquisitions of organizations…
3. Coping with wicked problemsThinking “project” may lead the various stakeholders
– part the wicked problem ecosystem and usually with
divergent pluralistic or coercive values (Jackson, 2010) – to find some commonalities and way forward through politics, vested interests and power games. However this may require huge amounts of efforts (see European coun-tries and EU discussions around economic development plan(s) as a good example). However project management decisions may be part of the wicked problems or ecosys-tem and to some extent awake the “sleeping dragon” or contribute to open pandora’s box… for good or bad.
Using traditional PM methodologies? Forget about them… they are not designed to tackle such problems, but to address problems when a certain level of consensus does exist amongst stakeholders, when the “problems” are identified, when information is available and when a cer-tain level of stability in the socio-economic environment makes things somehow predictable. In short, in order to apply traditional PM approaches, we need a certain level of order and things (variables, factors, …) should be know or knowable (French, 2013).
We need to turn to alternatives approaches acknowl-edging project managers’ practical wisdom as a landmark.
Authors emphasize moving from functionalist para-digm to Creative Holism & Total Systems Intervention and Critical Systems Practice (Jackson, 2003). This author suggests a set of approaches to be used in context and “with practical wisdom”, e.g. interpretive and soft systems thinking (which enable accommodating different view-points and alternative perspectives, learning & change - focus on: social systems, people purposes, interpretations of situations, people act & interact / interpretations), emancipatory systems thinking approaches (who benefits
JANUARY – APRIL 2015 | THE JOURNAL OF MODERN PROJECT MANAGEMENT REVIEW A 121
Prof Christophe N. Bredillet
PhD, D.Sc., IPMA Level A, FAPM, FIoD
Scientific Director Société française pour l’avancement du Management de Projet (SMaP)
First assessor, IPMA Certification of Project Management Consultants (CPMC)
Lead assessor, IPMA Four Level Certification (4-L-C)
Adjunct Professor, Queensland University of Technology (QUT)
from / is affected by the system design – focus on: emancipate oppressed individuals & groups, reveal forms of power & domination, discrimination) or Postmodern Systems Thinking (No methodology can guarantee improvement, diversity encouraged & suppressed viewpoints to be surfaced – focus on: challenge any totalizing attempts to provide comprehen-sive explanations / organizations function. Emphasize having fun. Learn much by bringing conflict to surface, space for disregarded opinions, encouraging variety and diversity).
Last (but not least…) thoughts
Therefore, in these “wicked problems” contexts, we need to move from the classical “sense-categorize-respond” or “sense-analyse-respond” problem solving and decision making processes to “probe-sense-respond” process supported by the above mentioned paradigms and methodologies such as problem-structuring methods, exploratory data analy-sis, expert judgment, metagames… or even to “act-sense-respond” process supported by exploratory practice and trial & error (Kurtz & Snowden, 2003, French, 2013). In order to do so, we acknowledge project managers’ practical wisdom as a landmark.
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7(4), 548-565.Bredillet, C., Tywoniak, S., & Dwivedula, R. (2015). What is a good project manager? An Aristotelian perspective. International Jour-
nal of Project Management, 33(2), 254-266.Churchman, C. W. ( 1967). Guest Editorial. Management Science 14(4): 141-142.Conklin, J. (2006). Dialogue mapping : building shared understanding of wicked problems. Chichester, England: Wiley.Floyd, S. W., Cornelissen, J. P., Wright, M., & Delios, A. (2011). Processes and practices of strategizing and organizing: Review, devel-
opment and the role of bridging and umbrella constructs. Journal of Management Studies, 48(5), 933-952. Flyvbjerg, B. (2014). What You Should Know About Megaprojects and Why: An Overview. Project Management Journal, 45(2), 6-19.French, S. (2013). Cynefin, statistics and decision analysis. The Journal of the Operational Research Society, 64 (4): 547-561Hock, D. W. (1995). The Chaordic Organization: out of control and into order. World Business Academy Perspectives – 9(1): 1—9.Jackson, M. C. (2003). Systems thinking: creative holism for managers. Chichester, England: John Wiley and Sons.Jackson, M. C. (2010). Reflections on the Development and Contribution of Critical Systems Thinking and Practice. Systems Research
and Behavioral Science, 27(2): 133-139Kurtz, C. F., & Snowden, D. J. (2003). The new dynamics of strategy: Sense-making in a complex and complicated world. IBM Systems
Journal, 42(3), 462-483.Puranam, P., Alexy, O., & Reitzig, M. (2014). What's "new" about new form of organizing? [Article]. Academy of Management Review,
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