OPEN UNIVERSITY OF MALAYSIA
PhD Viva Voce
BY
JOSEPH TEYE IGNATIUS BUERTEY BSc., MSc.
TOPIC:
ESTIMATING COST CONTINGENCY FOR ENGINEERING CONSTRUCTION PROJECTS:
INTEGRATING RISK MODELLING TO IMPROVE BUDGETARY RELIABILITY
JULY 2015
Purpose of the Study
• The study developed and validated a conceptual framework for determining realistic cost contingency margins for Engineering projects.
.
.
Coordinated and integrated national
database on Engineering Projects in
Ghana is non-existent
The substructure, engineering
services installations and finishes
were prone to high scope changes,
system failure and severity
effect on cost overruns
The effect of systemic uncertainty
on project cost overruns is very
high compared to project specific risk
Engineering Professional’s
knowledge about risk management
processes is very low
The Financial impact of scope and
design related risk on cost growth is
very high
SIGNIFICANCE OF STUDY
Practical Implication: The need
to review the curricula of tertiary
institutions and provide CPD
Orientation by Professional bodies
Policy Implications: The need to
establish an Engineering Industry
regulatory body in Ghana
Theory Implications :
Risk partition according to the
DST combination rule
Practical Implication
The need to enhance design
management effort by
practitioners
Practical Implication
The need to research to improve
the estimation process of Cost
contingency margins
Research Problem PROBLEM
SITUATION
No
standardised
methods
High risk
characterization
of industry
High Cost and
Schedule
overruns
Increased cost
uncertainty
Increased
system
engineering
failure
Addressing the Gap is very important because it forms the
basis for informed decision making in the study area and
boost efforts at minimizing cost overruns and client
confidence in Project cost management and serves as a
basis for contingency management
RESEARCH GAP:
HOW TO ESTIMATE RELIABLE COST CONTINGENCY
MARGINGS FOR ENGINEERING PROJECTS
PREVIOUS RESEARCH
• Touran (2003b) and Baccarini (2004): Contingency margins
is estimated as a percentage (10%) of the contract value
• Ali (2005): Proposed that cost contingency estimation must be
undertaken using the integration of risk management process
hence was recommended for further research
• Gunhan and Arditi (2007): Held that Deterministic models are
unrealistic hence risk modelling was required
• Keith (2011): Held that uncertainty in cost growth decreases as
one travels along the project trajectory with significant risk
unveiling. Hence recommended risk integration for modelling
cost contingency
Literature Review Item Reference Key Points from Review
1 Patrascu (1988) Contingency is an amount of money for cost required but uncertain. These
uncertain cost includes design changes, design growth, variations due to
method of construction, scope creep and specification changes.
2 Malik (1994) Used quantitative methods to determine uncertainty of project cost. Thus
BOQ items and usage resources and their prices were presented as
moments . The method did factor risk items
3 Touran (2003a) Presented a probabilistic model that considers the random nature of
change orders and their impact on the cost and schedule overruns. He
used poison correlation for design stage budgeting
4 Baccarini (2004) A 3-tier contingency model was developed. The model was based on
project variables such as project size, bid variability, bids received, project
duration, project location, year of execution, etc
5 Sonmez et al (2007) Reviewed the financial impact of risk factors at the bidding stage . Used
the Pearson Correlation Coefficient
6 Nasirzadeh et al
(2008)
Developed a model for the expected cost of the project . The risk outcome
was deduced by multiplying the probability of occurrence by the maximum
expected cost (expected value)
7 Keith (2012) Proposed a 3-tier risk modelling must be used. as risk identification;
qualitative risk analysis. Type III a quantitative risk analysis and active
contingency management
Literature Review-Contingency Methods
. Traditional
Method
Detailed
Percentages
with Probability
of occurrence
Risk Analysis
and Modelling
Detailed
Percentages
• Method of moments (Diekmann, 1983), (Moselhi, 1997), (Yeo, 1990)
• Range estimating, (Curran, 1989)
• Analytical hierarchy process (Dey, Tabucanon & Ogunlana, 1994)
• Fuzzy sets/logic (Peak, Lee, Ock, 1993)
• Monte Carlo simulation (Lorance & Wendy, 1999), (Clark, 2001)
• Influence diagrams (Diekmann & Featherman, 1998)
• Factor rating (Hackney, 1985; Oberlander & Trost, 2001)
• Regression Analysis (Merrow & Yarossi, 1990; Aibinu & Jagboro, 2002)
• Artificial neural network (Chen & Hartman, 2000; Williams, 2003)
• Theory of constraints (Leach, 2003)
• Risk Analysis(Touran, 2003a; Rashed, 2005; Gunhan& Arditi, 2007, Kaith
(2012)
Literature Review- Theories
Dempster Shafer
Theory AP Dempster (1967) &
G. Shafer (1976)
Probability
Theory Gerolamo Cardano
(1657-game of Chance)
Fuzzy Set
Theory Nasir Zadeh (1965)
Decision Theory
Random variables, stochastic process and event.
Focused on goal-directed behavior in the presence of options
Uncertainties related to uncertainties, vagueness and ambiguity
Belief function allows one to base degrees of
belief for one question on the probabilities for
related questions- mass, belief and plausibility
Evidence is associated with multiple event at a
higher level of abstraction
In Summary:
• The process of cost contingency estimation needs further exploration.
• Previous studies have focused on how to improve errors in the existing deterministic methods at the expense of bringing out new ideas to overcome its unreliability.
• The need to introduce into the cost-risk equation a combinatorial matrix of qualitative and quantitative risk modelling in relation to detectability of risk factors and work sections
• These gaps have been addressed by undertaking an integrated risk studies using the FMEA and belief function of DST combination model
Methodology-Philosophy
Realism Positivist
Approximation towards Idealism Facts and values are distinct
The rationale for using realism is that the varied bases for estimating
contingency Margins for construction project exist but the systematic
risk procedure to be adopted for the estimating process is lacking
The Positivist position was chosen with the belief that the intriguing
process of estimating cost contingencies could be explored further
through a systematic and cognitive approach
METHODOLOGY- RESEARCH PROCESS
. Research Approach: Deductive: Application of
DST
Inductive: Develop CCM
Framework design
Testing and validation
Instrument:
Questionnaires
Distribution &
unstructured interviews
Research Design:
Cross-section:
observation of how cost
Contingency is
estimated in the industry
Data Gathering
Method
Primary and
Secondary Data
Research Strategy
Qualitative
(Interviews/
Content Analysis)
Quantitative methods
Sampling Method:
Kish (1965) & Clarke
(1998)
Findings and
Conclusion
Data Analysis:
Qualitative and
Quantitative
Research Methodology (Data Analysis)
RQ Research Question Analysis Method Reasons
RQ1 How do construction professionals
allow for contingency margins in the
engineering and construction
industry
Univariate
Statistical
Analysis
To determine ethnographical
practices of Built Environment
professionals
RQ2 Why do practitioners adopted the
current methods to estimate cost
contingency
Content Analysis To evaluate the enterprise
environmental factors of built
environment professionals
RQ3 What is the level of knowledge of risk
management by Engineering and
construction professionals?
Content Analysis Measure risk orientation in the
built environment
RQ4 How can risk factors affecting project
cost contingency be categorised
qualitatively ?
Content Analysis Risk categorisatioin for a RBS
RQ5 Which risk factors significantly affect
project cost contingency
Multivariate
statistical analysis
FMEA
Qualitative Risk analysis for
further quantitative modelling
RQ6 Which construction work sections are
prone to high scope changes?
Multivariate
statistical analysis
FMEA
To measure risk severity and
impact of the various work
sections
Research Methodology (Data Analysis)
RQ Research Question Analysis Method Reasons
RQ7 What is the approximate probability
of occurrence of the risk factors
affecting contingency margins?
Multi-variate
statistical analysis &
Risk Modelling
To assess the mass, belief
and plausibility of the
Dempster’s combination
RQ8 To what extent does the risk factors
associated with contingency
margins, financially impact the
project?
Multi-variate
statistical analysis &
Risk Modelling
To determine probability
impact matrix for the
Dempster’s combination
RQ9 What risk management process
should one follow in the
determination of an adequate
contingency margin?
Risk Modelling To determine a cohesive and
systematic method of
determining and evaluating
contingency drivers
RQ10 How can the process of cost
contingency estimation be
improved?
Risk Modelling To develop a frame framework
for the cost risk management
RQ11 What steps should be considered
when developing a model for the
determination of contingency
margins for construction projects in
Ghana?
Risk Modelling To validate and confirm a
systematic process for the
cost risk modelling
ANALYSIS • Based on objective data source which is representative and free from bias, DST
calculus is estimated by means of mapping
m:2 Ω →[0,1]; With m(A) > 0 called the focal element.
• The function m is called the basic assignment and fulfills: ∑AϚΩm(A)=1
• Bel (A)= ∑AϚA; B≠ØΩ m(B) AND Bel (¬A) = 1- Pl (A), Pl (A)= 1- bel (¬A)
With bel (¬A)≤ pl (¬A)
• Where uncertainty is construed as the difference between pl (¬A) and bel (¬A)
Pl(A)= ∑A∩B≠Ø m(B)=1
• The measure of pl(A) is not understood as the complement of bel (A).
Only AϚΩ|m(A)>0≠Ø→bel (A)≤ pl (A)
• For any set of defined hypotheses, represented by a set Ω= a1, a2, a3
• 2Ω = Ø, h1, h2, h3, h1,h2 , h1, h3,, h2, h3 , h1,h2, h3, Ω
• Based on data entry and transfer, several algebraic equations were be generated
Y1= a1x1+b1x2+c1x3+d1x4+α1 ………………………………………………..(1)
Yn= a1xn+bnx2+cnx3+dnx4+αn ………………………………………………..(n)
• With the above frame of discernment, an m x n matrix would be deduced
ANALYSIS • 71% of built environment professionals have no knowledge about risk management theories
and techniques.
• Only 7.07% of professionals had ever applied a risk management tool in practice
• There are no standardised methods for estimating cost contingency.
• Approximately 90.06% of respondents used deterministic tools based on self-intuition,
experience and organisational process asset to estimate cost contingency
• There are no coordinated and integrated national database on Engineering Projects
• Approximately 64% of the cost drivers were systemic in nature whilst projects specific risk
accounted for 36% of the risk impact.
• 8 out of the 31 Risk factors significantly affected the cost contingency and 4 work sections
were identified as prone to high scope changes.
• Based on categorisation, design and Economic risk had the highest likelihood of occurrence
• Design and financial risk had the highest financial impact
• Natural and financial risk had the highest Hideability
• The substructure and services had the highest likelihood of occurrence
• Services and finishes had the highest financial cost impact
• Substructure and services were the most hideable work sections
ANALYSIS
• Based on the basic assignments of the DST combinatorial matrix, the likely cumulative financial impact of the most significant risk were determined.
• The estimated overall cost growth ranges from 13.1% to 17.88% using 4 work sections and 8 risk factors
Substructure
( 3.8%),
Finishes
( 2.8%),
Electrical
engineering
work (3.3%)
mechanical
installations
(3.2%)
Incomplete
scope
definition
(0.261%)
Changes in
scope
(0.242%)
Inflation and
micro
economic
instability
(0.176%)
Changes in
specification
(0.128%)
Delayed
payment
problems
(0.164%)
Differing site
conditions
(0.171%)
.
.
ANALYSIS: CONCEPTUAL COST RISK EVOLUTION PROCESS
.
.
ANALYSIS: INFORMATION FLOW FOR IMPLEMENTED RISK MODEL
CLIENT BREIF
CONCEPT FORMATION
FEASIBILITY STAGE
PROJECT INITIATION
PRELIMINARY DEFINITION STAGE
DETAILED DESIGN/SCOPE DEFINITION
DESIGN MANAGEMENT
DESIGN DOCUMENT
DESIGN REVIEW
CONTRACT STRATEGY
PROJECT CHARTER
COST PLANNING
COST FORCASTING
COST ESTIMATE
COST BUDGETING
DETAILED DESIGN/SCOPE DEFINITION
COST MODELLING
FINANCIAL TREATMENT
COST RESERVE
PROJECT PLANNING STAGE
RISK IDENTIFICATION
QUALITATIVE RISK ANALYSIS
QUANTITATIVE RISK ANALYSIS
FINANCIAL IMPACT
RISK RESPONSE PLANNING
RISK UPDATE REVIEW
ENDOGENOUS
EXOGENOUS
SYSTEMIC
PROJECT SPECIFIC
INPUT PROJECT PHASE
RISK MANAGEMENT PROCESS OUTPUT
RISK EVALUATION
RISK QUANTIFICATION
TESTING AND VALIDATION OF MODEL
. This was to verify the usability and
reality of the model results obtained
based on the Evidential reasoning
Theory. Users propounded their own
basic belief assignment and defined
their own hypothesis
PILOT TESTING OF
MODEL FOR QS, COST
ENGINEERS & PM
• Data of completed project was used
• Planed contingency and Actual Contingency.
• Actual average of cost overruns was 49.61%
with ranging from 1.62% to 332.52%. With Most
of the values higher than the modal figures
• An average of 5.1% had 10% contingency
• Another 76.5% had 26.43%, with the rest higher
• The modal contingency was 17% with a range of
8% to 29%.
• Comparing actual cost to model figures were
very close with variability of 0.5% to 5.25%
VALIDATION OF
MODEL
• An action research
method
• A focus group of 12
QS & CE was used
for the validation
exercise
DISCUSSIONS OF THE STUDY RESULTS
Research Findings Deductions from the Results
RF1: Most professionals and respondents in the industry
used deterministic tools based on self-intuition, experience
and organizational process asset
RF2: There are not standardised tools or methods to be
adopted. No National historical data on cost
This could be attributed to the lack of
coherent methods of determining cost
contingency.
Practitioners may adopted the easier way
of determining the cost contingency
RF3: Most built environment professionals have no
knowledge about risk management theories and
techniques, only an insignificant proportion have ever
applied a risk management tool in their professional
practice
The background training of the
professional has resulted in the use of
unreliable methods resulting in project cost
overruns.
Guestimating and deterministic models
may be the only option in practice with
unreliable project output
RF4: Risk factors may be categorised based on orientation
in the RBS as drivers.
RF5: Based on FMEA, Scope related design risk had the
highest Risk Priority categorisation. This was followed by
economic, financial risk and technical risk.
A cogent risk breakdown structure may be
the first step to cost risk estimation
Risk identification using brain storming
and Delphi techniques, an effective
qualitative and quantitative method
required
DISCUSSIONS OF THE RESULTS
Research Findings Deductions from the Results
R6: Using FMEA, 4 work sections were identified as prone to
high scope changes. These are Substructure, Electrical and
Mechanical installations and Finishes.
Critical review of scope definition
and Integrated Design
management may be considered
RF7: Design and Financial risk accounted for the highest
probability of occurrence of cost risk driver affecting cost
contingency
RF8: Design risk had the highest risk impact categorisation
followed by financial risk. Systemic risk had the highest
Financial Impact Matrix
The need to review the effect of
systemic risk on project cost
overruns
An effective Risk response
planning process required to
mitigate the effect of these risk
RF9: Qualitative Risk analysis using FMEA and
Quantitative risk analysis using DST by means of probability
impact matrix
RF10: Conceptual cost risk evolution onions was developed
following risk management planning, Risk identification;
qualitative risk planning, quantitative risk analysis, risk
response planning, risk review, risk update
The impact of financial risk may
be mitigated through risk
modelling
The use of the DST was exploited
DISCUSSIONS OF THE RESULTS CONT Relationship of Research Findings to Other Research
.
RESEARCH
FINDING
PREVIOUS RESEARCH CORRELATION BETWEEN PREVIOUS
RESEARCH
RF 1,2 & 3 Gunhan and Arditi (2007); Estimating contingency
margins for construction projects using percentages
Ford (2002): there is no evidence of formal standardized
models for estimating Cost Contingency
There are no formal process of estimating project
cost contingency contingencies in Ghana
Practitioners use deterministic models
RF 4 & 5 Rifat et al (2007): established that differing site
conditions, safety, defective design, changes in work,
delayed payment of the contractor, and quality of work
as the high important risk factors.
Burroughs & Juntima (2004) established project
definition, use of technology, process complexity,
contracting and execution strategy, equipment
percentage
31 risk descriptors categorised into 8 risk
groupings. Key risk factors affecting project cost
contingency are scope and design related .
Systemic risk significantly affected cost overruns .
Substructure the key project specific risk indicator
RF 6 Kim, An & Kang (2004) established that Gross floor area,
storeys, total units, duration, roof types, foundation type,
basement, finishing grade affected cost growth
Using the FMEA, the work section prone to high
scope changes are substructure, services and
finishes
RF 7 & 8 Ali (2005) modeled cost contingency using probability,
Monte Carlo simulation and triangular cost distribution by
integrating the floor Area per building component
Design and financial risk has the highest likelihood
and impact. Environmental and natural risk was
the most difficult to detect
RF 9,10,11 Keith (2007) proposed with various tools, a risk
management framework of risk identification, risk
analysis, risk mitigation and plan, risk allocation and risk
monitoring and control for the risk cost risk management
process
A 7-step risk management process was
developed using the Dempster combination rule.
The Mass belief and plausibility of the various
work sections and risk were determined for the
purpose of the Cost contingency model
CONCLUSIONS AND SUMMARY Research Objective Research Finding Research Implication
1. Identifying and documenting the
current methods adopted by
construction professional in the
determination of project cost
contingencies
There is no structured models or prescriptive
methods adopted except for their reliability on
historical data, organizational process asset and
reliance on expert knowledge
PRACTICE: The findings can contribute to
restructuring of the curricula for higher
education in Ghana and CPD orientation by
the professional bodies in Ghana
2. Identify rate and rank the most
significant factors that affect
contingency levels of construction
projects
Systemic risk factors were identified to have the
highest severity affect the cost contingency. These
are mainly design, scope, economic risk. Natural
risk had highest pproject specific indicators
POLICY: The result can contribute to the
need for the establishment of Construction
Industry Regulatory body in Ghana
3. Determine the works sections that
are prone to high scope changes and
likely impact of these changes
Substructure, services and finishes were identified
as work sections which were prone to high scope
changes
PRACTICE: it would serve as a driver for an
enhanced design management effort for Built
environment professionals
4. To estimate the likely probability
of occurrence and financial impact of
the combined risk factors and work
sections using evidential reasoning
methods.
Using FMEA and DST, basic belief assignments
were developed, with mass and plausibility. Design
and Economic risk had the highest likelihood of
occurrence and severity impact Substructure,
finishes and services had the highest likelihood and
impact
THEORY: The findings can form a basis for
theoretical partitioning of the cost risk drivers
based on the Dempster’s combination
5. Develop, document and validate a
conceptual framework for
construction cost contingency
estimation.
A matrix of the risk factors matched with work
sections based on the basic belief assignments was
developed. Forthwith, multi-stage CCC risk
management model was developed for the purpose
of cost contingency estimation
PRACTICE: The findings can serve as a
basis for regulating risk and actuarial studies
on project prior to commencement by
Engineering and designs actors
6. Develop a computer based model to
test the practicability of the
conceptual framework for
construction cost contingency
estimation.
A Cost Contingency model, PROCOM Model was
developed and tested and be could be accessed via
www.costcontingencycalculator.com
PRACTICE: these could help review the
Cost contingency estimation method by using
basic belief functions
CONTRIBUTION TO KNOWLDGE. The GAPS in knowledge have been filled by the study as follows:
• It has filled a theoretical gap by providing a structured method of determining cost risk for
construction projects • A tabulated financial impact of various risk and their indices has as well been developed
as a guide during cost contingency estimation. The research has filled the gap in term of the extent of contingency definition a professional can influence in the form of systemic risk.
• Using evidential reasoning, an innovative model has been developed for the estimation of construction cost contingency with much variability. This uniquely models the likelihood, impact and detectability of risk factors using FMEA and DST. The matrix of the major risk factors and work section have been combined to develop basic belief assignments. To enhance uniformity of the model, the key characteristics of the construction industry was used as permissible parameters. Users would be required to input a set of hypothesis based on their belief and pieces of evidence based on risk and work section. This method provides a higher level of detail and reliability in the risk definition of contingency estimating
• The estimated cost of contingency for a project may not necessarily 10% but depending on the project design definition and risk parameters being modeled. Analysis of field studies with qualitative and quantitative risk analysis revealed an approximate cost contingency range of 13.1% to 17.88%, this could be higher or lower depending on the extent to systemic risk could me managed. Thus the above accounts for the high cost growth, project failure and system engineering failure in most Engineering projects.
• An innovative model has been proposed, tested and validated for cost contingency definition based on the degree uncertainty such as known known, known unknown, unknown unknown. Again the work redefined contingency in terms of categorization as: construction, design, management or financial contingency.
7 NUMBER SCIENTIFIC PUBLICATIONS .
SP1 Buertey, J. T.I, Abeere-Inga, E., Adjei Kumi, T. (2012). Construction cost contingency
estimating in Ghana: a review of work sections prone to scope changes, International
Journal of Management, Volume 3, Issue 1, pp. 237-253. ISSN 0976 – 6367(Print), ISSN
0976 – 6375(Online)
SP2 Buertey, J. T.I, Abeere-Inga, E, Adjei Kumi, T. (2012), Practical application of risk
management techniques in infrastructural delivery: a case study of the Ghanaian
Construction Industry. Journal of Construction Project Management and Innovation, Vol 2,
No. 1, ISSN 2223 7852
SP3 Buertey, J. T.I, Abeere-Inga, E., Adjei Kumi, T. (2012), Estimating cost contingency for
construction projects: the impact of systemic and project specific risk. Journal of
Construction Project Management and Innovation, Vol 2, No. 1, ISSN 2223 7852
SP4 Buertey, J. T.I, Abeere-Inga, E.; Adjei Kumi, T. (2012). Successful delivery of Infrastructural
Projects: Epistemic overview of cost risk and uncertainties, Journal of Civil Engineering and
Architecture, ISSN 1934-7359, Volume 6, No. 2 (Serial No. 51), pp. 121–131, USA
SP5 Buertey, J. T.I, Abeere-Inga, E.; Adjei Kumi, T. (2012). Project Cost contingency estimation
in Ghana, an integrated Approach. Science Journal of Civil Engineering and Architecture,
ISSN, 227-6332
SP6 Buertey, J. T.I, Abeere-Inga, E.; Adjei Kumi, T. (2013). The financial impact of risk factors
affecting project cost contingency: evidential reasoning method. (Journal of Engineering,
Project, and Production Management). ISSN 2221-6529 (Print), ISSN 2223-8379 (Online)
SP7 Buertey Joseph Teye (2014), Project Cost Risk and Uncertainties: Towards a Conceptual
Cost Contingency Estimation Model, International Journal of Construction Engineering and
Management 2014, 3(5): 144-155 DOI: 10.5923/j.ijcem.20140305.02
5 NO. CONFERENCE PAPERS .
CP1 Buertey, J. T.I, Abeere-Inga, E.; Adjei-Kumi, T. Successful Delivery of Infrastructural
Projects: Epistemic Overview of Cost Risk and Uncertainties, Proceedings of 1st
International Conference on Infrastructure Development in Africa at KNUST, Kumasi,
22nd – 24th March 2012
CP2 Joseph T.I. Buertey, T. Adjei-Kumi: “Modelling cash flow prediction in Ghana: A Case
Study of the District Assembly Common Funded Projects”. West Africa Built Environment
Conference, University of Reading. 27th- 28th June 2010
CP3 Buertey, J. T.I, Abeere-Inga, E.; Adjei-Kumi, T. Successful Delivery of Infrastructural
Projects: Epistemic Overview of Cost Risk and Uncertainties, Proceedings of 1st
International Conference on Infrastructure Development in Africa at KNUST, Kumasi,
22nd – 24th March 2012
CP4 Buertey, J. T. I, Abeere-Inga, E.; Adjei-Kumi, T, Project cost risk and uncertainties:
towards a conceptual cost contingency estimation model. Proceedings of WABER 2013
Conference in Accra, Ghana, British Council, 12th-14th August 2013
CP5 Buertey J.T.I (Accepted). A conceptual cost contingency estimation model: a risk
modelling approach. Accepted for presentation at the West Africa Built Environment
Conference, 10th -12 August 2015 at the University of Ghana
.
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