Project Decision ChainAsbjørn Rolstadås, Department of Production and Quality Engineering, Norwegian University of Science and Technology, Trondheim, NorwayJeffrey K. Pinto, Sam and Irene Black School of Business, The Behrend College, Penn State University, Erie, PA, USAPeter Falster, DTU Compute, Technical University of Denmark, Lyngy, DenmarkRay Venkataraman, Sam and Irene Black School of Business, The Behrend College, Penn State University, Erie, PA, USA
INTRODUCTION ■
In all projects, a number of decisions are made. Many think of decision making as one of the core activities of a project manager; thus, competence in decision making and tools to aid the decision-making process are of crucial importance for project success.
But how formalized is the decision-making process in projects? Do we have a clear understanding of the various types of decisions? How are deci-sions documented? What methods are available for the decision maker?
Fischer and Adams (2011) studied engineering-based decisions in con-struction and claim that the (construction) industry has a crying need for engineers to ensure that field decisions are made using the required level of technical analysis. They also point to the fact that decisions must be made “in the heat of the battle”; every minute counts, and there is no place to “hide.” Arroyo (2014) conducted interviews and case studies and found that:
• Decisions are rarely documented.• Rationale is not clear.• Formal decision-making methods are seldom used.• The decision-making process usually lacks transparency and does not help in
building consensus or continuous learning.• Multiple stakeholders with different perspectives and conflicts of interests are
involved.
In this article we will argue that a more formal and rigorous approach to decision making is needed. This will improve the quality of the decision and develop commitment and understanding in the project organization. Rol-stadås, Pinto, Falster, and Venkataraman (2014) make a distinction between the process of producing the project results and the management and lead-ership of the human resources involved. The first is referred to as technical aspects; the second is organizational aspects. (This is in line with the distinc-tion between project success and project management success, as we will dis-cuss later.) This means that there are decisions made concerning the choice of technology, budget spending, and time allocation. These decisions may be different from the decisions made regarding organizational structure, person-nel allocation, and contractor involvement.
Whether the focus is on technical aspects or on organizational perfor-mance, we need to distinguish between a prescriptive or adaptive approach to management of the project (Rolstadås, Tommelein, Schiefloe, & Bal-lard, 2014). The Pentagon model for analyzing organizational performance ( Schiefloe, 2011) is used to explain the difference between these two approaches. Factors such as structure and technologies are formal qualities.
conceptualized into a project decision chain resembling the well-known value chain found in the manufacturing indus-try. We will base our discussion on a capital value process model as shown in Figure 1 (Rolstadås, Pinto, et al., 2014). The business cycle is a preproject stage during which business opportunities are explored. The project cycle covers the project execution through different proj-ect phases. The operation cycle covers the use of the project results and repre-sents the benefit stage.
Literature ReviewDecision theory is a general research field covering applications in various areas; typically, it is considered as oper-ations research or systems theory. Our research is limited to decision making in projects. The rationale for this is that we believe that project success is more likely if the decision process in a project is better formalized and man-aged; thus, we have not reviewed the general decision theory literature, but limited our literature search to what has been published in the main project management journals. We found a large number of publications. When we took a closer look at the articles, however, we found the main focus to be on the deci-sions connected to ranking or selection of alternatives. We sorted the articles according to their main decision focus:
• Decision environment—looking at how and when decision support is benefi-cial, requirements for decision making, and the impact of different factors on decisions
• Decision-making process—looking at the decision process itself
• Decision-making tools—looking at the tools, methods, and models used in decision making
• Applications—looking at the spe-cific applications or cases of decision making
Decision Environment
The structuring of decision activities is recognized in the literature as one of
Marques, Gourc, and Lauras (2010) are looking at projects as complex sys-tems and claim that when complexity becomes sufficiently large, the possibili-ties and interrelations become so fuzzy that decision making has to be assisted by the appropriate tools and skills. They argue for the need for a modeling approach to decision making. Brady and Davies (2014) also support this view.
The quality of a decision depends on the capacity of the decision maker to perform a twin evaluation (Marques et al., 2010):
• The current situation of the project versus the initial objectives (a priori evaluation); and
• The possible evolution of the project according to decisions and events (a posteriori evaluation).
Most of the project management literature on decision making is con-cerned with project selection. We claim that decision making in project man-agement is much broader and embed-ded in the whole life cycle, beginning with the business opportunity through the obtained benefit from the project.
The above discussion shows that decision making in projects is a multi-faceted problem; however, there are two different aspects:
• The lenses through which the deci-sion problem is regarded (descriptive/adaptive approach, schools of thought, risk focus); and
• The process of making the decision (prescriptive/descriptive, a priori/a posteriori).
The lenses through which we regard the problem represent the mindset of the decision maker. Even though the success criteria are the same, two peo-ple with two different mindsets may make different decisions.
In this article, we will take a closer look at the different types of decisions and the associated decision-making techniques. We will show how the decisions can be
The prescriptive approach enters the project through these formal qualities, which means that a full project gov-ernance system is available and that project execution is based on the orga-nizational experience this represents. Factors such as culture, interaction, and social relations and networks are infor-mal qualities. The adaptive approach enters the project through these infor-mal qualities. Project stakeholders are then selected based on intent to cooperate and social relations, and the organization is adapted to a govern-ing system during project execution. Decision making is, of course, differ-ent depending on which of these two approaches is selected.
Söderlund (2010) also supports the view of difference in approach and defines seven project management schools of thought: optimization, fac-tor, contingency, behavior, governance, relationship, and decision. Obviously, the thinking of the decision maker is influenced by the school he or she rep-resents; again, this will “color” the deci-sion making because he or she will be looking at the situation through differ-ent lenses.
Similarly, Divekar, Bangal, and Sumangala (2012) discuss the prescrip-tive and descriptive models of deci-sion making and claim that prescriptive decision scientists are concerned with prescribing methods for making opti-mal decisions. Descriptive decision researchers are concerned with the bounded way in which decisions are actually made.
All projects carry risk. Risk is closely connected to the decision-making process (Rolstadås, Hetland, Jergeas, & Westney, 2011). Any decision intro-duces some risk or presents some kind of opportunity. At the same time, deci-sions are often made to mitigate risk or to capitalize on an opportunity. Manag-ing risks may lead to changes to plans. Such decisions require a higher level of understanding and creative thought than the original plan (Fischer & Adams, 2011).
The study by Steffens, Martinsuo, and Artto (2007) explores the use of decision criteria for change requests of product development projects. The project management literature suggests change management processes dur-ing the project execution. The prod-uct development literature has mainly covered planned decisions and go/no-go decision criteria during a phased product development process. The article investigates decision criteria including project efficiency, customer impact, business success, preparing for the future, and the project portfolio in change management in complex prod-uct development projects. Only the last decision criterion was not actively used as an evaluation criterion.
Decision-Making Process
Project management, confronted with a sudden change in project scope or an unexpected development, must make a series of agile interrelated decisions in response. Knowledge about the content of and interconnections between deci-sions made in similar circumstances in the past can help the project manager keep in mind all the decisions that need to be made. Karni and Kaner (2005) have developed an agile knowledge-based, decision-making approach based on an integrated case- and cluster-based architecture for supporting these decisions.
and quality of decision making; they found that project overload, as well as information overload, is positively, but weakly, related to PMIS information quality.
Human decision makers are often stubborn and very often overly optimis-tic about the influence they have over the outcomes of projects. Meyer (2014) presents the findings from an experi-ment, which investigated to what extent decision makers suffer from optimism bias when escalating a commitment to failing projects. He proposes a new form of optimism bias, called post-project optimism bias. It is an overly optimistic belief that a project will deliver bet-ter business benefits than what was planned or that can be proven. It is fur-ther confirmed that both post-project and in-project optimism biases have significant effects on the escalation of commitment to failing projects.
Earlier product development litera-ture has largely covered planned deci-sions and go/no-go decision criteria in line with a phased product development process. Project management literature, in turn, suggests change management processes and practices during the proj-ect. Earlier research has not sufficiently covered criteria for change decisions that are needed between product devel-opment gates, nor a holistic approach for making such decisions in complex product development projects.
the most important components of the decision-making process. Based on lit-erature studies on the decision-making process in a distributed team environ-ment, Bourgault, Drouin, and Hamel (2008) developed a theoretical model that links team autonomy and formal-ization of the decision-making process to the quality of decisions and team-work effectiveness with varying levels of geographical dispersion.
Karim (2011) explore and measure the impact of project management information system (PMIS) factors on an efficient and effective project man-agement decision-making process. The factors investigated are information quality, analytical quality, system qual-ity, technical quality, communication quality, and decision maker’s quality. The results showed a significant contri-bution (effect) of PMIS to better project planning, scheduling, monitoring, and controlling.
The project manager’s dissatisfac-tion with PMIS was the background for a similar study conducted by Caniëls and Bakens (2012) to gain a better understanding of the elements of PMIS that contribute to decision-making quality in a multi-project environment. They investigated the relationships between six factors: project overload, information overload, information quality, project manager satisfaction with PMIS, use of PMIS information,
Screening business
opportunities
DemolitionOperation and maintenance
NegotiationBid preparation
Conceptualization Planning Execution Termination
Business cycle
Project cycle
Operation cycle
Figure 1: Capital value process (Rolstadås, Pinto, et al., 2014, p. 34).
comparing all feasible alternatives or actions by pair building up some binary relations—crisp or fuzzy—then exploit-ing these relations appropriately in order to obtain final recommendations.
Value-Focused Thinking: A Path to Creative Decision Making by Keeney (1996) claims that the conventional approaches to decision making focus on alternatives. However, alternatives are only the means to an end, namely to achieve values. Decision making, there-fore, should begin with values because they are fundamental and the driving force behind decision making.
Designers develop alternatives, and gradually narrow down the alternatives by eliminating alternatives until they come to a final solution (Sobek, Ward, & Liker, 1999). In Set-Based Design (SBD), designers are encouraged to explore alternatives collaboratively and keep them open until the last possible moment, thereby reducing negative design iteration. This is in contrast to Point-Based Design, where one alterna-tive is selected earlier in the design pro-cess and presented to the next design specialist.
Barton and Love (2000) use the idea of decision chains to argue that product design decisions are part of a chain of decisions that extend to the design and operation of the “downstream” processes that ultimately manufacture and support the product. The decision process moves from the abstract to the concrete with the solution on one level becoming part of the requirements and constraints on the next level.
Applications
Applications of decision support tools in project management are concerned with selection problems in portfolios and projects, contractor selection, and the selection between different activi-ties. Because our research focuses on a decision chain model, we present only limited article reviews on various appli-cations.
Khalili-Damghani and Tavana (2014) propose an integrated approach
practically all types of decisions. Sound methods base decisions on the impor-tance of prospective differences among the alternatives. CBA postpones “value” judgment about alternatives as long as possible. In contrast, value-based meth-ods set the weights of the factors early in the decision-making process.
The GRAI method (Doumeingts, 1984), developed at the University of Bourdeaux, was originally designed for production management. The articles by Ridgway (1992) and Wortmann, Muntslag, and Timmermans (1997) demonstrate that it can also be used to analyze decision centers and informa-tion flow in the management of a large project. It identifies inconsistencies in the management structure and the flow of information between decision cen-ters. The GRAI method is explained in the next section.
Arroyo (2014) has evaluated the ability of Multiple-Criteria Decision- Making (MCDM) methods to help design teams choose a sustainable alternative during commercial building design. The work identifies several types of MCDM methods in the literature and those with potential application for the selection decision problem are:
• Goal-programming and multi-objective optimization methods
• Value-based methods (including Analytical Hierarchy Process [AHP] and Weighting Rating and Calculating [WRC])
• Outranking methods• Choosing By Advantages (CBA)
In particular, Arroyo claims that CBA is shown to be one of the most promising methods.
Dealing with practical problems and difficulties experienced with the value function approach, the Outranking Methods were developed in France in the late 1960s (Figueira & Roy, 2002) and are closely associated with the name of Bernard Roy, who developed the family of Electre Methods, which mainly feature modeling preferences; in other words,
Mafakheri, Nasiri, and Mousavi (2008) propose a decision aid model using fuzzy set theory for assessment of project agility. This model consid-ers multiple dimensions for agileness, and the proposed model provides an opportunity for using a range of aggre-gation operators to determine the index of agility.
Based on Simon’s principles of bounded rationality to decision mak-ing (Simon, 1960), Gidel, Gautier, and Duchamp (2005) developed a decision-making framework methodology for project risk management in new prod-uct design. The decision-making frame-work facilitates the process of deciding whether to take risks, take action to reduce the causes of risks, take action to reduce the consequences, or take action to improve the detection of risks.
Decision-Making Tools
Multiple criteria decision analysis (MCDA) is a discipline in its own right and extensive literature exists on the subject. Some of the original writing on decision making goes back to the work by A. M. Wellington on The Economic Theory of Railway Location (1887) (Grant, Grant, & Leavenworth, 1990). The work developed into the discipline of engineering economics, described in the book Principles of Engineering Eco-nomic (1990) by Eugene Grant and has also been reported by Jim Suhr (1999).
Saaty (1980) developed the Analyti-cal Hierarchy Process (AHP) as a multi-criteria decision-making method. This method aims at quantifying relative pri-orities for a given set of alternatives on a ratio scale, based on the judgment of the decision maker; it stresses the importance of the intuitive judgments of a decision maker as well as the con-sistency of the comparison of alterna-tives in the decision-making process.
An original decision-making system based on Choosing By Advantages (CBA) was developed by Suhr (1999). The CBA system is a complete set of tools for the decision maker, including definitions, principles, models, and methods for
Drucker (1955) defined a similar model with six steps by adding an action and follow-up activity to Simon’s model:
1. Define the problem2. Analyze the problem3. Develop alternative solutions4. Decide on the best solution5. Convert decisions into effective
actions6. Follow-up on actions taken
Rolstadås, Pinto, et al. (2014) pub-lished a model of the decision-making process (as shown in Figure 2). A core part of this is the decision preparation, taking input from the results achieved, future plans, problems identified, and policies deployed. The preparation uses decision factors, which are variables to take into account; it is facilitated by the application of decision-making meth-ods. The decision will produce some action. The outcome of this is, however, influenced by uncertainty. The uncer-tainty may either be imposed by nature or come as a result of stakeholders’ influence or actions.
The case study by Strang (2010) develops and examines a mixed-method, integrated qualitative and quantitative portfolio selection model, applied to a “nuclear tritium extraction facility” project concept. The study presents a new model, weighted normalized port-folio selection, and evaluation method, by building on an existing AHP theory using statistical principles.
By applying the AHP theory, Al-Harbi (2001) describes how the prequal-ification criteria can be prioritized and a descending-order list of contractors can be made in order to select the best con-tractors to perform a specific project.
Decision problem often has to take conflicting views into account. The study by Mota, Almeida, and Alencar (2009) presents a model focusing on the main tasks of a project network by assigning priorities to activities using a multiple criteria decision aid (MCDA) approach. The model is illustrated by a case study of the construction of an electricity substation.
Decision-Making ProcessTraditionally, decision making is about identifying problems and analyzing, developing, and choosing between alternative courses of action to achieve a desired objective. As we will see, how-ever, our scope is broader: decision making is more than selecting from a list of alternatives. Several models for
for strategic and sustainable project portfolio selection, which is composed of first using strategic planning and sus-tainability concepts to select a set of promising projects and, second, using a project portfolio selection procedure to choose among the promising projects identified. A structural equation model is used to analyze and explain the rela-tionships among different factors in the proposed framework.
Abbasianjahromi, Rajaie, Shakeri, and Chokan (2014) explore allocating the best portion of tasks to subcontrac-tors while optimizing the risk and cost in the fixed project schedule. The main finding demonstrates that subcontractor selection without attention to the order allocation is not a realistic approach; therefore, a hybrid model that applies continuous ant colony and fuzzy set theory is proposed.
Satisfying objective values in real-world project management decisions would often be imprecise because the cost coefficients and parameters are imprecise. The work of Liang (2009) focuses on the application of fuzzy sets to solve fuzzy multi-objective project management decision problems. The proposed possibilistic linear program-ming approach attempts to simultane-ously minimize total project costs and completion time with reference to direct costs, indirect costs, relevant activity times and costs, and budget constraints.
Resultsachieved
Problemsidentified
Policies
Decisionpreparation
Decisionmethods
Decisionfactors
Inpu
ts
Action
Stake-holders
Result
Uncertainty
Nature
Futureplans
Decision
Fi gure 2: Decision-making process (Rolstadås, Pinto, et al., 2014, p. 82).
information on the physical and deci-sional activities and provides informa-tion to these activities. The decisional system is composed of all the control decision centers aimed at managing the physical system.
The GRAI model uses two formal-isms (Wortmann et al., 1997): The GRAI grid and the GRAI nets.
The GRAI grid (Figure 3) is a macro-model of the decisional structure. The decision centers are linked together with arrows denoting flow of information. Decisions need to be made with refer-ence to a horizon of time. Therefore, the criterion of vertical decomposition is based on time—the decision horizon and the decision period. The horizontal decomposition is based on the type of management decisions (manage prod-ucts, plan, manage resources).
The GRAI nets represent the micro-model of the decisional struc-ture. There are two types of nets: “to do” for programmed decisions and the more advanced “to decide” for non-programmed decisions, which relies on the decision maker’s ability to make a decision using certain inputs as shown in Figure 2.
Decision MethodsAs our review of literature shows, there are numerous publications on selection decisions; most use multiple criteria, some consider uncertainty.
Fülöp (2005) claims that making a decision implies that there are alter-native choices to be considered and distinguishes between single and mul-tiple criteria, and whether there are a finite or infinite number of alterna-tives. He breaks down multiple crite-ria methods according to cost benefit analysis, elementary methods, multi-attribute utility theory, and outrank-ing methods. Arroyo (2014) has also studied multiple criteria decision methods and compares four types: goal programming and multi-objective optimization, value based methods, outranking methods, and choosing by advantages.
included, for example, a budget or the preferred choice of a contractor. Inher-ent in a plan decision is an authoriza-tion to initiate the plan; in this way it differs from the authorization deci-sion, which is merely a go/no-go deci-sion. Plan decisions initiate activity that will accomplish the project objectives and deliver the results by synchroniza-tion of products and resources. Plans are also used to monitor work progress and decide on the necessary corrective actions. Plans may be changed accord-ing to a range of factors: requests from the owner (changed authorization); decisions to correct errors (e.g., a design that proves to be infeasible); to improve the project results (e.g., taking advan-tage of new technology); or to reduce costs. Plan decisions also include deci-sions to initiate corrective actions and incorporate approved changes in the plans and, in addition, to initiate and monitor any actions following from these changes. The formal approval of a single change order is a go/no-go deci-sion (authorization decision).
To study the decision-making pro-cess, we can benefit from a modeling approach (Marques et al., 2010; Brady & Davies, 2014). The generic GRAI model (Doumeingts, 1984) has been developed to model decision making in produc-tion management. Ridgway (1992) and Wortmann et al. (1997) have shown how the GRAI model can be applied to proj-ect management.
The GRAI model splits the produc-tion management and the project man-agement system into three subsystems:
• The physical system• The information system• The decisional system
The physical system consists of all the activities related to the realization of a project. The physical flow is both the flow of documents and information in the conceptualization and planning phases and the material flow in the exe-cution phase of the project cycle. The information system collects and stores
This model is in line with the model published by Drucker (1955). Decision preparation corresponds to Drucker’s steps 1, 2, and 3; decision corresponds to step 4; and action corresponds to steps 5 and 6.
The capital value process model in Figure 1 is, from a generic point of view, also a decision-making process.
Most decision-making models are designed to aid a choice or a selection process; however, all projects have at least one sanction point. This is a deci-sion to proceed or not and is somewhat different from a selection (although it is a selection between two alternatives), as the purpose is to obtain an authoriza-tion in contrast to ranking or selecting among alternative solutions to a prob-lem. In addition, the project execution plan represents a decision to carry out activities and spend resources, which shows that there are at least three dif-ferent types of decisions to be made in a project (Rolstadås, Pinto, et al., 2014):
• Selection decisions • Authorization decisions• Plan decisions
Selection decisions are the selection of one (or more) alternative(s) from a list of options. Typically, they are con-cerned with finding “the best” solutions from a number of potential alternatives. Often a selection decision includes ranking the alternatives as well; this decision is fundamentally what is dis-cussed and covered by the rational decision-making model developed by Simon (1960) and Drucker (1955).
Authorization decisions are go/no-go, yes/no, or go/stop decisions. Some decision background data are developed and used for the decision making. An example could be the inves-tor looking at a prospect and review-ing its capital exposure, payback time, net present value, and internal rate of return, before saying yes or no.
Plan decisions are the approval of plans that establish what to do, how, and when. Other details may also be
To support our wider view on deci-sion making, including authorization and plan decisions, we will use a clas-sification of methods from Rolstadås, Pinto, et al. (2014, p. 86):
• Direct evaluation techniques, where the preferred alternative is checked or decided on directly.
• Criteria-based evaluation techniques, where specific criteria (normally more than one) are used to select and rank alternatives. There are methods using quantitative or qualitative criteria. Criteria may be both stochastic and deterministic.
• Deterministic and stochastic planning techniques, which are planning tech-niques based on either deterministic data or data with uncertainty.
Table 1 provides an overview of some decision techniques according to this classification and also indicates the type of decision where the techniques are applicable.
Success Factors and Decision Making Unfortunately, there is neither a uni-versal recipe for achieving project success, nor a universally valid defini-tion of what a successful project looks like. As Pinto and Slevin (1988, p. 67) observed: “There are few topics in the
is, there is no need to link them to broader portfolios of projects or higher order programs. When a company is confronted with the need to manage a particular event, it can gain consider-able advantage from focusing exclu-sively on actions oriented toward these criteria. Second, this approach enables organizations to capture data on proj-ects as they are completed and allows them to analyze performance histori-cally, across multiple projects. In this way, it is possible to determine what is “working” and where the organization suffers from systematic problems (e.g., if project after project comes in over budget, this may point to some embed-ded flaws in the planning process, our cost estimation methods, or project control systems). Thus, the first step in our assessment of project management efficiency is understanding success as a function of organizational practices.
2. Project Success—Was the right project done? A recurring theme in modern project management is the notion of “value.” Does the value now pro-vided by the project for the owner justify its undertaking? Treating proj-ects from the perspective of effec-tiveness (as opposed to internal efficiency) broadens and enhances our understanding of our inten-tions when deciding to undertake a project. Projects, of course, are not initiated without regard for corpo-rate goals, strategies, and the bot-tom line. The “right project” criterion allows the organization to consider additional concepts of value man-agement, risk assessment, and the corporate benefits of the project. In effect, when we consider if the right project is being initiated, projects are allowed to take on a broader, strategic role in the organization. The man-ner of project selection and evalua-tion in many organizations further demonstrates the importance of the effectiveness criterion. In such cir-cumstances, project alternatives are evaluated through financial model-ing (such as net present value) and
success” is linked to process models of critical success factors that identify those managerial actions that can go far in setting the right conditions in which a project team can constructively per-form its tasks and increase the likeli-hood of successful project outcomes. So, for example, taking one of Pinto and Slevin’s (1988) original 10 project critical success factors—top manage-ment support—their findings suggest that the greater the degree to which members of top management take an interest in the project and support it with personal social capital (sponsor-ship) or resources (people and money), the greater the likelihood that the proj-ect will succeed.
Nearly three decades of work on project success, critical success fac-tors, and various assessment models for project performance have contributed to a wealth of information and perspec-tives on this phenomenon. Although it is impossible to fully summarize the knowledge of the field, we can touch on the most important perspectives for academics and practitioners who want to get the most out of this information. Let us consider the summary of project success made by De Wit (1988), Shen-har and Dvir (2007), and Cooke-Davies (2004):
1. Project Management Success—Was the project done right? The most common means for assessing the success of a project is to focus on the efficiency with which it was completed. This is the area to which the bulk of practitioner literature devotes its attention; that is, making sure that the project comes in on or near budget and schedule, while performing with an acceptable degree of functionality (quality). This assess-ment of success closely conforms to the original triple-constraint ideas we started with; however, conceptualiza-tion is still useful for several reasons. First, it adheres to the idea that projects in many organizations still represent discrete, one-off ventures that have to be managed in their own right; that
field of project management that are so frequently discussed and yet so rarely agreed upon as the notion of project success.” Projects are always exploring new territories in which the resolution of some critical issues may simply cause others to pop up; thus, the array of success factors is dynamic and tends to vary over the project’s life cycle and between different projects.
It is first important to distinguish between the ideas of project success and project management success. For example, it is critical to define how the project is intended to deliver stake-holder value. As Cooke-Davies (2002) observed, project management success can be measured against traditional performance gauges (i.e., time, cost, quality, and stakeholder satisfaction). We will discuss these ideas in more detail below. Project success, on the other hand, is measured against the overall objectives of the project. This distinction is important because many projects, although viewed as successful in the broader sense, do not look very much like project management success stories. The Sydney Opera House, for example, overran its initial budget by a factor of 14 and ran 15 years behind schedule, experiencing numerous con-struction problems and setbacks (Khar-banda & Pinto, 1996). Nonetheless, it is still proudly held up as an engineering masterpiece and the architectural high-light of Sydney Harbor.
The above discussion illustrates the need to distinguish between process and outcome models of success factors. Project success is routinely measured through company-specific key perfor-mance indicators (KPIs), which serve as our baseline for assessment of the viability of project performance. Did the project yield 15% return on invest-ment? Was the project completed to performance standards or predeter-mined schedule or budget constraints? Answers to KPIs such as these allow us to make a reasonable determination of the success and value of the project. On the other hand, “project management
process of integrating the flow of raw materials from the suppliers to the final delivery of the finished product to the end-customer (Gourdin, 2001). The purpose of supply chain management is to have an integrated view on how materials and products flow from dif-ferent suppliers to the manufacturer and to the final customer in order to make a decision to find optimal solu-tions that are competitive. There are direct similarities between supply chain management, project management, and the decision-making process. Just like supply chain management, project management and decision making are also structured processes of managing workflow in a predetermined sequence across time and place, and have a defi-nite beginning, end, and specific out-comes (Henrie, 2007). The supply chain is often referred to as a value chain, as value is added as the product/services progress through each phase of the sup-ply chain.
We would like to extend this con-cept of adding value to decision making in projects. We will call the sequence of decisions made during the vari-ous stages of a capital value process, a project decision chain. In this decision chain, decisions made in each phase of the actual life cycle (business, proj-ect, operation) should add value to the overall project. In other words, the deci-sions made during each phase should contribute to the project’s three key per-formance indicators (KPIs) of reducing cost, on-time completion, and ensuring project performance.
The building block in a project decision chain will be a primitive ele-ment, shown in Figure 4. The element is triggered by an authorization deci-sion shown as a decision gate in the figure. The element includes work to be performed, which involves a selec-tion of work process and the execution of a task. The process selection can be regarded as a choice between several alternatives; in our terminology, that is a selection decision. The execution of the task is based on a plan, which
example, stage-gate reviews (Cooper, Edgett, & Kleinschmidt, 2001) would be just such a decision-making process that has been argued to lead to success-ful outcomes. In gated reviews, critical success factors relate to the decision process itself as a means for assessment.
An alternative example of poorly conceived decision making is demon-strated through the work of Flyvbjerg and associates (Flyvbjerg, Bruzelius, & Rothengatter, 2003; Flyvbjerg, 2012; Pinto, 2013), who identified a number of decision “pathologies” in project selec-tion and planning. Their arguments, that many project initiation and plan-ning decisions are a product of fal-lacious reasoning, optimism bias, and strategic misrepresentation reflects on a similar flawed decision-making process with predictable outcomes for the final (and rarely well-completed) project. That is, when critical decision-making success factors are ignored or downplayed, the results have a signifi-cantly negative effect on the viability of successful project outcomes. As a result, the idea of the project decision chain is predicated on the viability of a decision-making process that takes into consideration both the process for how to most effectively and efficiently derive workable decisions as well as a means for assessing the decisions themselves, taking corrective action, and employing a learning methodology.
Project Decision Chain The concept of supply chain man-agement is well understood in the manufacturing sector. “Supply chain management is the process of managing relationships, information, and materi-als flow across enterprise borders to deliver enhanced customer service and economic value … (by implementing) a specific ordering of work activities across time and place, with a begin-ning, an end, clearly identified inputs and outputs, and a structure of action” (Mentzer, 2001). Simply defined, in the context of a manufacturing environ-ment, supply chain management is the
market research (customer assess-ment), bringing maximum clarity to the selection of the “right” projects. In other words, project success must incorporate this idea of not simply “doing the project right” but also “doing the right project.”
3. Ongoing Project Success—Consistently pursuing the right projects and doing them right, time after time. A newer feature of assessing project success has been to move the focus further up the corporate ladder, looking at projects not simply as discrete undertakings but also examining an organization’s proj-ect portfolio and how they are selected and managed in a unified, holistic way. When our conception of project suc-cess combines efficiency and effective-ness over extended periods of time, our understanding of project management deepens to include other key elements: organizational learning and knowledge management (passing project wis-dom down through the organization), as well as new methods and models that allow organizations to systematize their project management processes. For example, initiatives such as gated review processes and project manage-ment offices are efforts made to embed correct project management method-ology throughout an organization.
Just as such distinctions have been made between the ideas of project suc-cess and project management success, it is necessary to consider a similar dichotomy as it relates to the notion of successful decision making in projects. That is, we can examine ex post, out-come decisions to assess their “success” in terms of how well they conformed to their original intention; that is, did enacting a particular decision solve the target problem? Alternatively, we can assess decision making as an enacted process; in other words, was decision making done correctly; that is, did the decision-making process itself follow required guidelines or standards that we have a priori determined to be valid for problem recognition and solution? For
of decisions made during these vari-ous phases of the project cycle in more detail in the next section.
The project conceptualization phase includes decisions on all activi-ties, beginning with origination of the project idea to the final decision on financing the project. In this phase, one of the major decisions is selecting a sub-set of projects (project portfolio) from a variety of available projects. This is an inherently complex problem, because the project selection decision should consider multiple and often conflict-ing objectives. A number of decision- making tools, including checklists; weighted scoring models; Analytic Hierarchy Process (AHP); Choosing By Advantages (CBA); profile models; and financial models, such as net pres-ent value analysis and internal rate of return can be used to make project screening and selection decisions. Dur-ing the initial stages of the project life cycle, however, when there is significant uncertainty about the future, heuristic decision rules can be the most useful tools to making reasonably sound deci-sions (Williams & Samset, 2010).
The underlying theme behind a project decision chain is that deci-sions made about the project con-cept are linked to the decisions to be made during all subsequent phases of the product cycle. Therefore, dur-ing the project planning phase, all the activities relating to the project work—broadly outlined in the scope of work specified during the concep-tualization stage—must be spelled out
tion). Figure 5 shows an example of the project cycle.
Each task in the primitive element can in itself contain subtasks, which in turn will comprise new primitive ele-ments. These may be bound together in a network and may exist at varying levels of detail in accordance with a WBS. An example of the conceptualiza-tion phase of the project cycle is shown in Figure 6.
The project decision chain and the more detailed project activity network express the ordered sequence of events on a timeline. This is one aspect of time. The other aspect of time is expressed by the GRAI grid, where decision making of planning and control is hierarchically decomposed into levels of varying hori-zons and time periods. The primitive element can therefore also be repre-sented by a grid for each of the phases of the project cycle, as illustrated in Figure 7. The overall management of the decision chain is a strategic deci-sion element above the project decision chain (Wortmann et al., 1997).
Thus, we have structured the deci-sions according to two aspects of time:
• Horizontal—a chain of primitive ele-ments
• Vertical—primitive elements in a deci-sion cycle
We will now concentrate on the project cycle, which has four distinct phases: conceptualization, planning, execution, and termination (see Figure 1). We will discuss the sequence
includes both a schedule and a budget based on the synchronization of “prod-ucts” (i.e., documents and materials and resources). It also includes adjust-ments of the plan based on feedback from the execution. This is what we have referred to as a plan decision. The primitive decision element thus involves all the three types of decisions discussed earlier.
The task to be executed will require resources and will be subject to con-straints. Resources can be:
• Personnel• Materials• Equipment• Money
Constraints can be:
• Time• Precedence relations• Limited resources
The primitive elements can be fit-ted into the capital value process for each of the three cycles indicated in Figure 1 (business, project, and opera-
Alt. A
Alt. B
Alt. C
Execution
Process selection(Selection decision)
Task execution(Plan decision)
DG Authorization decision
F igure 4: Primitive element in a project decision chain.
consumed. All of the decisions in this phase focus on monitoring and con-trolling project progress to ensure the technical requirements, budgetary, and schedule constraints established in the project plan are met. In addition, deci-sions are made to ensure the effective and efficient management of resources. To facilitate decision making in the project execution phase, several project control techniques, including S-curves, milestone analysis, Gantt charts, and Earned Value Analysis (EVA) are used. Similarly, techniques such as resource loading and resource leveling are used to ensure project resources are effec-tively utilized.
Project closeout or termination occurs when the project is stopped due to its successful (or unsuccessful) conclusion. Assuming successful proj-ect conclusion, decisions made dur-ing this phase focus on the process of transferring the completed project to the customer and conducting the final phase-out activities. In addition, deci-sions have to be made on assigning the current project team members to new projects, and how to disperse the mate-rial assets deployed in the completed project. Checklists are useful tools to ensure that all final project close-out activities are completed.
plan that has clearly defined goals and objectives, scope of the project, a work breakdown structure, a logical project network and schedule, a feasible proj-ect budget, clearly defined project team responsibilities, resources required, and a risk management strategy.
Once the project plan is in place the next phase of decisions focuses on executing the project plan. It is during this phase that the actual work of the project as laid out in the project plan is performed. It is also the stage in the project life cycle where the majority of resources allocated to the project are
in detail and properly scheduled and sequenced. Given that projects oper-ate in an environment of uncertainty, there is always a risk that a project can run into trouble; therefore, an impor-tant decision is to determine a risk management strategy that will be used to identify, analyze, and respond to risk factors throughout the project life cycle. A variety of charting and network analysis techniques are used during this phase to aid the decision maker in coming up with a sound project schedule. The outcome of all these decisions is a well-integrated project
FunctionHorizonPeriod
H = P =
H = P =
To manageproducts
To manageresourcesTo plan
FunctionHorizonPeriod
H = P =
H = P =
To manageproducts
To manageresources
To planFunction
HorizonPeriod
H = P =
H = P =
To manageproducts
To manageresources To plan
Figu re 7: Project decision chain management structure.
Primitiveelement
Primitiveelement
Primitiveelement
Primitiveelement
Primitiveelement
Primitiveelement
Primitiveelement
Conceptualization
Fig ure 6: Network of decision elements in a project decision chain element.
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ConclusionThe art of decision making is pivotal to effective project management. Dur-ing every stage of the project life cycle, project managers face a huge array of choices, such as which supplier to use to improve the quality of the new product to be developed, whether the project work should be done in-house or outsourced, and so forth (Intaver Institute, 2014). Furthermore, elements of decision analysis such as analysis of potential alternatives and assessment of project risk are critical during each stage of the project life cycle. A well-established decision analysis process integrated into the overall project man-agement process is vital for improving project performance. In this article, we have proposed a project decision chain framework (similar to a supply chain) that will ensure that decisions made at each stage of the project life cycle add value to overall project performance.
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