A master thesis project designed, executed and presented by Ir. Dimitrios Kordas for the degree of MSc in Construction Management and Engineering at the University of Twente, the Netherlands. Study details: √ questionnaire-based survey study √ 27 individual on-site risks assessed √ 5 cost elemental categories √ product-based cost model √ Tools: MC method, AHP, Sensitivity diagrams √ Sample: 22 building projects from the Greek Construction Industry Under the supervision of: Prof. Joop Halman (chairman) Assoc. Prof. Saad Al-Jibouri (direct supervisor)

1. “RISK SHARING IN TRADITIONAL
CONSTRUCTION CONTRACTS FOR
BUILDING PROJECTS.”
A CONTRACTOR’S PERSPECTIVE
IN THE GREEK CONSTRUCTION INDUSTRY.
ENSCHEDE, 29-04-2015
Dimitrios Kordas (M-CME/s1231901)
MSc: Construction Management and Engineering

2. 1. INTRODUCTION
29-04-2015 2

3. 29-04-2015 3
■ Failure on: “Triple Constraints” (Cost-Time- Scope) target?
■ Cost overruns in traditional construction contract?
▪ Delay risks: 50% projects suffered by time overruns in UAE’s CI (Faridi 2006)
▪ 61% of Australian contractors claim bearing misallocated cost risks (Atkin 2006)
AlSalman & Sillars (2013)
1. INTRODUCTION

4. “Traditional procurement”
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Ferry et al. (1999)
1. INTRODUCTION
“Project estimate”

5. 29-04-2015 5
“A practical case in traditional building procurement in the Greek CI”
Step 1: Client submits a Request for Proposals invitation
Step 2: Contractors: “Closed books” bid-competition
Step 3: Client and awarded contractor: “Open books” Post-bid
3.1 Compensation mechanism agreement
3.2 Risk sharing agreement
For the contractor:
Price (Contract value) = Fixed amount (Base estimate) + Profit
For the client:
Price = Contract value ± 30-35%×Contract value
“Maximum cost risk allowance”
1. INTRODUCTION
Pipattanapiwong (2004)
■ No risks → Fixed amount paid.
■ On-site risks → always →
sharing agreement → if unfair
risk allocation by client →
contractors: reserve amounts
(contingencies) or arbitration
path.
High contingencies pre-bid
↔ More/Less risks shared
Revise profits & estimates? ⇒
Project cost performance
Cotnigencies
Contingencies ↔
Incentive profits Contingency reduction →
Project delivery efficiency

6. 2. RESEARCH DESIGN
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“Problem scope”
√ Type of projects: Buildings
√ Phase: Execution (Construction)
√ Post-bid period
√ Cost-side
Chang & Ive (2002)
PMI (2013)

7. 29-04-2015 7
“Cost estimating”
■ Cost estimates: (1) Design estimates, (2) Bid estimates, (3) Control estimates
2. RESEARCH DESIGN
“Building a cost model”
Product-based Vs. Process-based
COST PLAN
PROJECT: (Type), (Location) Note: This cost plan is based upon the attached outline
DATE OF COST PLAN: X/X/2015 specification, and both documents should be read together.
ASSUMED DATE OF TENDER: X/X/2015
TOTAL INTERNAL FLOOR AREA: 2,390 m²
Cost
Unit Quantity Unit Cost (₤) Subtotal (₤) Total (₤) Elemental cost (₤) / m²
1. WORK BELOW LOWEST FLOOR FINISH
Ground floor area 390 m² 321.00 125,19 52.38
2. STRUCTURAL FRAME
2,390 m² 125,6 52.55
3. UPPER FLOORS
225 mm Hollow pot 386 m² 60.00 23,16
150 mm in-situ RC 1,585 m² 41.00 64,985
88,145 36.88
4. STAIRCASES
RC Staircases 25 m 1225.00 30,625
1 No 25 m rise
1 No secondary
21.5 m rise 21.5 m 900.00 19,350
49,975 49,975 20.91
“Cost elements”
▪ Land preparation
▪ Foundations
▪ Substructure
▪ Superstructure
▪ Finishes
RICS (2014)

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Ferry et al. (1999)
2. RESEARCH DESIGN
“Components in a project cost estimate”
1. Identification
2. Assessment = Analysis + Evaluation
3. Monitoring
4. Control

9. 29-04-2015 9
2. RESEARCH DESIGN
“Research goal”
“Assessing the effect of risk sharing decisions taken (in post-bid) by contractors and
examine projects’ performance in terms of project delivery efficiency and cost performance.”
“Research question”
“How do risk-sharing decisions of contractors’ in the post-bid context affect building
projects cost performance when these projects are traditionally procured?”
√ Revise the minimum
required change in
profit
√ Revise risk sharing
and base estimate
amounts
√ Reduce contingency
below the 10% of
base estimate
“Research objectives”
√ Cost risk transfer
√ Probability to meet the base estimate
√ Contingencies
√ Incentive profits
√ Any relation: Incentive profits ↔ Contingencies
√ Improvements on Project performance?
√ Reduce project delivery inefficiency?
pre-ΔΜ
pοst-ΔΜ
“Proposal”

10. 29-04-2015 10
Joustra (2010)
3. LITERATURE REVIEW
Definitions
“Uncertainty”
√ The variability and ambiguity of future
outcomes.
√ No probability distributions can be assigned
Vs. Risky situations
“Risk”
√ Expected value = Probability × Impact
√ The “fallacy” of expected value:
Risk events Magnitude Probability Impact Expected
Value
Earth. 1 3 Richter 5% € 10000 € 500
Earth. 2 6 Richter 0.02% € 1500000 € 300
RiskCompound command (@RISK)
“Contingency”
√ A budget reserve above the estimate to
reduce the risk of overruns at a desired
confidence level.
√ Included in bid price → Total commitment
on tender
√ Arbitrary amount, usually set as the 10% of
base estimate.

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3. LITERATURE REVIEW
“Systematic review”
No specific RM
framework applied.
27 on-site risks were
collected: tailored list
4 cost risk drivers
√ Quantity
√ Unit Cost
√ Schedule
√ Global
Risk importance criteria
√ Propensity
√ Perception
√ Performance
Quantitative risk assessment
√ MC simulation
√ PDFs, CDFs
√ Sensitivity diagrams
AHP
Risk misallocation
literature consensus.

12. 12
No previous study in risk sharing in D-B-B contracts
Risk sharing mainly as an optimization parameter of contracts.
“Risk transfer degree” and “Project delivery efficiency” not examined.
P (price of contract) = F (fixed amount) + b×(E – C)
Incentive profit
3. LITERATURE REVIEW
“Motivation”
Degree of risk transfer →
Project delivery inefficiency →
Witt & Liias (2011)
Efficient delivery of project ⇒ ΔP=ΔM=ΔC=0
P (price) = C (cost) + M (margin)

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4. MODEL DESIGN
“Stochastic process: 5 questions” (Diekmann 1983)
(1) Data available for each risk (Xi)? → Probability (RiskCompound) Range of Impact = Individual
risk effect level
(2) Correlated Xi s → No correlations assumed among the 27 individual risks
(3) Data required for Yj? → Yj = Ej + n×CRi
(4) Additive or Multiplicative combination of Xi → Additive:
(5) How many cost elements (Yj)? → 5 cost elements
Project final cost = Iterations number: 10000
Confidence level = 50% + Average risk effect level
Input distributions:
Base estimates Risk Probability Risk Impact
(Risk Triang) (Risk Discrete) (Risk Trigen)
Land
preparation
Foundations
Substructure
Superstructure
Finishes
Fixed: Risk Discrete Variable: P=100%

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4.4 Cost risk analysis model
for contingency estimation
Model adopted from Hobbs (2010)

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Construction phase Risk factors Drivers Type Risk Probability Input Distr. Risk impact (% on E change) Input Distr. Overall risk effect Amount of individual line cost impacted by risk (€) Estimated effect of individual risk on line item or Cost Risks (€) Percentage impacted Total Cost
Occuring Not occuring Discrete / 1 (F / V) Low Most likely High Trigen RiskCompound Risk Prob/ty × Cost line = Amount Impacted Overall effect × Amount impacted = Cost effect (€) E + Cost Risks
% (€)
Land preparation R1 Quantity F 30% 70% 0 -5% 5% 10% 0,028488 0,008739 0,3 × 18300 5490 0,008738705 × 5490 47,97549008 0,002622 0,2621611
E (€) 18300 R2 Quantity F 30% 70% 0 -5% 5% 10% 0,028488 0,008739 0,3 × 18300 5490 0,008738705 × 5490 47,97549008 0,002622 0,2621611
(-5%,+5%) 18300 R3 Quantity V 100% 0% 1 -5% 5% 10% 0,028488 0,028488 1 × 18300 18300 0,02848774 × 18300 521,3256419 0,028488 2,848774
R4 Quantity V 100% 0% 1 -5% 5% 10% 0,028488 0,028488 1 × 18300 18300 0,02848774 × 18300 521,3256419 0,028488 2,848774
R5 Unit cost F 30% 70% 0 -10% 2% 10% 0,002814 0,001092 0,3 × 18300 5490 0,001092299 × 5490 5,996721855 0,000328 0,032769
0,075545 7,55% 1144,598986 6,2546393 19444,60
Foundations R6 Unit cost V 100% 0% 1 -10% 2% 10% 0,002814 0,002814 1 × 54900 54900 0,002813841 × 54900 154,4798472 0,002814 0,2813841
E (€) 54900 R7 Global V 100% 0% 1 -5% 1% 5% 0,001407 0,001407 1 × 54900 54900 0,00140692 × 54900 77,23992359 0,001407 0,140692
(-2%,+2%) 54900 R8 Global V 100% 0% 1 -5% 1% 5% 0,001407 0,001407 1 × 54900 54900 0,00140692 × 54900 77,23992359 0,001407 0,140692
R9 Global F 10% 90% 0 -5% 1% 5% 0,001407 0,000182 0,1 × 54900 5490 0,000182032 × 5490 0,999353697 1,82E-05 0,0018203
R10 Quantity F 10% 90% 0 -5% 5% 10% 0,028488 0,002913 0,1 × 54900 5490 0,00291261 × 5490 15,99023084 0,000291 0,0291261
R11 Quantity V 100% 0% 1 -5% 5% 10% 0,028488 0,028488 1 × 54900 54900 0,02848774 × 54900 1563,976926 0,028488 2,848774
R12 Quantity F 30% 70% 0 -5% 5% 10% 0,028488 0,008739 0,3 × 54900 16470 0,008738705 × 16470 143,9264702 0,002622 0,2621611
0,045949 4,59% 2033,852675 3,7046497 56933,85
Substructure R13 Unit cost V 100% 0% 1 -10% 2% 10% 0,002814 0,002814 1 × 54900 54900 0,002813841 × 54900 154,4798472 0,002814 0,2813841
E (€) 54900 R14 Unit cost F 40% 60% 0 -10% 2% 10% 0,002814 0,001456 0,4 × 54900 21960 0,001456253 × 21960 31,97931831 0,000583 0,0582501
(-7%,+7%) 54900 R15 Unit cost F 40% 60% 0 -10% 2% 10% 0,002814 0,001456 0,4 × 54900 21960 0,001456253 × 21960 31,97931831 0,000583 0,0582501
R16 Schedule F 60% 40% 1 -10% 10% 50% 0,186049 0,112997 0,6 × 54900 32940 0,112996663 × 32940 3722,110068 0,067798 6,7797998
R17 Schedule V 100% 0% 1 -10% 10% 50% 0,186049 0,186049 1 × 54900 54900 0,18604904 × 54900 10214,0923 0,186049 18,604904
R18 Quantity V 100% 0% 1 -5% 5% 10% 0,028488 0,028488 1 × 54900 54900 0,02848774 × 54900 1563,976926 0,028488 2,848774
0,33326 33,32% 15718,61777 28,63 70618,62
Superstructure R19 Schedule F 50% 50% 0 -10% 10% 50% 0,186049 0,094126 0,5 × 45750 22875 0,09412622 × 22875 2153,137282 0,047063 4,706311
E (€) 45750 R20 Schedule F 30% 70% 0 -10% 10% 50% 0,186049 0,056481 0,3 × 45750 13725 0,05648138 × 13725 775,2069424 0,016944 1,6944414
(-7%,+7%) 45750 R21 Schedule F 30% 70% 0 -10% 10% 50% 0,186049 0,056481 0,3 × 45750 13725 0,05648138 × 13725 775,2069424 0,016944 1,6944414
R22 Global V 100% 0% 1 -5% 1% 5% 0,001407 0,001407 1 × 45750 45750 0,00140692 × 45750 64,36660299 0,001407 0,140692
R23 Global V 100% 0% 1 -5% 1% 5% 0,001407 0,001407 1 × 45750 45750 0,00140692 × 45750 64,36660299 0,001407 0,140692
0,209903 20,99% 3832,284373 8,3765779 49582,28
Finishes R24 Schedule V 100% 0% 1 -10% 10% 50% 0,186049 0,186049 1 × 9150 9150 0,18604904 × 9150 1702,348716 0,186049 18,604904
E (€) 9150 R25 Schedule V 100% 0% 1 -10% 10% 50% 0,186049 0,186049 1 × 9150 9150 0,18604904 × 9150 1702,348716 0,186049 18,604904
(-5%,+5%) 9150 R26 Unit cost V 100% 0% 1 -10% 2% 10% 0,002814 0,002814 1 × 9150 9150 0,002813841 × 9150 25,7466412 0,002814 0,2813841
R27 Unit cost V 100% 0% 1 -10% 2% 10% 0,002814 0,002814 1 × 9150 9150 0,002813841 × 9150 25,7466412 0,002814 0,2813841
0,377726 37,72% 3456,190715 37,772576 12606,19
Overall 20,83% Contribution of cost risk drivers
Driver Cost Risk (€) Total (€) Contribution
Q 4426,47 26185,54 0,17
Project final cost 209185,5 UC 430,41 26185,54 0,02
S 21044,45 26185,54 0,80
G 284,21 26185,54 0,01
Overall risk level = Average (Risk effects)
Confidence level= 50% + Overall risk level
Error range limits Input Distr. Triang
Error range limits Input Distr. Triang
Error range limits
Error range limits
Error range limits
Input Distr. Triang
Input Distr. Triang
Input Distr. Triang
Sum of output cells
@RISK cost risk analysis model (adopted from Hobbs 2010)
4. MODEL DESIGN

16. 5. SURVEY DESIGN
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“(Questionnaire) Survey in construction Project Risk Management (PRM)”
24 survey studies were reviewed
√ Extensive use of the 5-Likert scale √ Relative Importance Index
The study collects data with a questionnaire
√Section A: Organization profile
√Section B: Project specific characteristics
√Section C: Direct rating of risks and of cost risk drivers
√Section D: Contact details
“Survey evaluation – Section A”
■ 36 self-administrated questionnaires ■ Validity test → Content Validity Panel 5 Raters
√ 22 valid received → Item-CVI = 0.80>0.78
■ Response rate: 62.3% → ■ Reliability
much higher than the 30% expected ▪ Instrumental: Contingency = 0.546 (satisfactory)
(Uher & Toakley 1999; Zou et al. 2006) ▪ Response: ICC=0.758, a=0.439 (single: weak)
a=0.758 (total: satisfactory)

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5. SURVEY DESIGN
“Sample description”

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5. SURVEY DESIGN

19. 29-04-2015
19
6. DATA ANALYSIS
Kurtosis for standard normal distribution N (0, 1) → k=3
or k=0 → “excess kurtosis”
▪ k>0 → peaked distribution → heavy (excess) in tails (leptokurtic)
▪ k=0 → normal distribution (mesokurtic)
▪ k<0 → flat distribution → light tails (platykurtic)
▪ Standard normal distribution N(μ=0, var.=1)
▪ The skewness for any normal distribution and
“symmetrically” distributed data
g1=0
Kurtosis - Shape parameter
Skewness - Location parameter
Normal distribution → 95.5% of the “Project Final Cost” ⇒
Data normality and data symmetry
“Starting point”
Portfolio level
Project Case 10
Project Case 14
Portfolio level
Chapter 7
Chapter 6

20. 2029-04-2015
► On portfolio level (average values)
(1) Skewness: Land preparation (g1=-0.046), Foundations (g1=-0.047), Finishes (g1=-0.008)
negatively skewed
Substructure (g1=+0.020) and Superstructure (g1=+0.278) positively skewed
Superstructure (comparing to project-level) ⇒ +41.8%
Land preparation (comparing to project-level) ⇒ + 70.7%
If -0.5< g1 <+0.5 ⇒ Symmetrical distribution (Bulmer 1979)
Av. Skewness values
Y1 → g1 = -0.046
Y2 → g1 = -0.047
Y3 → g1 = +0.020
Y4 → g1 = +0.279
Y5 → g1 = -0.008
(2) Mean: Superstructure → highest average cost of € 98402.19
Finishes → largest range equal to € 149808
Land preparation → lowest average cost € 47402.77
Land preparation → smallest range equal to € 28489.27
(3) Coefficient of Variation (CV): substructure → highest CV=27.61%,
superstructure → lowest CV=11.55%
(4) Kurtosis: All cost elements were found with positive approx. k≈3.0
Av. Kurtosis values
Y1 → k = +2.81
Y2 → k = +2.79
Y3 → k = +2.70
Y4 → k = +3.33
Y5 → k = +2.75
6. DATA ANALYSIS
“Summary of statistics” Portfolio

21. 21
► On project level [pre-ΔΜ: Ε = € 263000, post-ΔΜ: E’= € 258857]
(1) Skewness: Substructure, Superstructure, Finishes → positively skewed
Land preparation, Foundations → negatively skewed
Superstructure → largest g1=0.4782, Land preparation → lowest g1=-0.1571
(2) Mean: Substructure → highest average cost with mean = € 100955.90 (=38.4% of E)
→ largest range (max – min) equal to € 178332.90
Land preparation → lowest average cost with mean = € 26725.95
→ smallest range (max – mix) equal to € 19979.64
(3) Coefficient of Variation (CV): Finishes → highest CV of 30.34%
Substructure → CV=26.73%
Land preparation → lowest CV=10.88%
(4) Kurtosis: Superstructure → highest k=3.1, Substructure → lowest k=2.675
6. DATA ANALYSIS
Project Case 10
“Data normality and symmetry tests”
Skewness & kurtosis significance tests → SPSS analysis input: {min, 5% Perc., 10% Perc., 15% Perc., . .
. . , 90% Perc., 95% Perc., and Max}
Skewness → Not significant at 95% c.l. → Symmetry in cost data Ok!
Kurtosis → Significant at 95% c.l. → All cost distributions leptokurtic (k>0)
Data
normality

22. 29-04-2015 22
6. DATA ANALYSIS
“Normality tests”
All cost elements → Follow normal distribution pattern
Kolmogorov-Smirnov (K-S) and Shapiro-Wilk (S-W)
Project
Case 10

23. 29-04-2015 23
“Correlation effect”
■ Impact of correlations among cost elements and individual risks often ignored (Flanagan et al. 1987; Jaafari
1988; Ranasinghe 1994)
■ Treatment of correlations necessary → realistic quantification of uncertainty (Ranasinghe & Russell 1992)
■ Correlation effect, if neglected may lead to:
▪ Underestimation of the total cost’s variance (Touran & Wiser 1992)
▪ Stronger effect of “including correlations” than the choice of distributions (Wall 1997)
▪ Ignoring a whole cost element (Yang 2005)
■ Spearman and Pearson coefficients in SPSS → Input for @RISK correlation matrix
▪@RISK adjusted the matrix so to be positively defined → -0.036% reduction in columns, +0.016%
increase in rows
Land
preparation
Foundations Substructure Superstructure Finishes
Land
preparation
1 0.9993 0.9993 0.9763 0.9923
Foundations 1 0.9943 0.9803 0.9943
Substructure 1 0.9913 0.9993
Superstructure 1 0.9903
Finishes 1
Adjusted self-consisted correlation matrix
6. DATA ANALYSIS
Project Case 14

24. 2429-04-2015
All cost elements strongly and
positively correlated → Sign for a
serious correlation effect.
No negative correlations
Rho = 1 ⇒ √ Monotonicity
√ Linearity
6. DATA ANALYSIS
“Correlation effect”
Project Case 14

25. 25
“Simulation results”
Probability meeting the estimate
+7 % Increase
Range of data captured in 90%
+94.67% more cost data captured
6. DATA ANALYSIS
Project Case 14
■ @ the desired confidence level 71.57%
+3.39% higher estimate when including correlations
■ Two S-curves cross @ 50% c.l.
▪ Below 50% c.l. → tendency for underestimation (with)
▪ Above 50% c.l. → tendency for overestimation (with)
Mean overestimated by +0.1% Variance increased by +3.4%
SD increased by + 1.7% SE increased by +55.1 units → more outliers . . .

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■ Tails exist (Recall: g1>0).
■ Heavier or Thinner? Why?
■ Heavier left tail
■ More skewed to left
■ g1 reduced from 0.1417 to
0.1379
Q-Q plots for normal distributions
Q-Q plots for natural logarithm
Why to transform?
To keep untouched the outliers and
reveal their true effect (Chou et al. 2009)
6. DATA ANALYSIS
Project Case 14

27. 29-04-2015 27
“Risk assessment of cost elements”
■ Substructure → largest SDx = € 15018.91 ⇒ max. effect in project final cost
■ Land preparation → smallest SDx = 1536.28 ⇒ min. effect in project final cost
6. DATA ANALYSIS
“Importance of individual risks”
Simulation (quantitative approach) and AHP
(qualitative approach): 1. Schedule
2. Quantity
3. Unit Cost
4. Global
Project Case 14

28. 29-04-2015 28
“Probability meeting base estimate”
pre-ΔΜ (%) post-ΔΜ (%) Change (%)
Land preparation 22.70 28.45 +25.33
Foundations 53.90 51.25 -4.91
Substructure 14.55 14.33 -1.51
Superstructure 27.18 26.83 -1.28
Finishes 20.95 22.50 +7.39
Project final cost 6.51 6.47 -0.61
pre-ΔΜ (%) post-ΔΜ (%) Change (%)
Land preparation 1575.82 1425.90 -9.51
Foundations 2845.70 2755.70 -3.16
Substructure 30630.40 29256.70 -4.48
Superstructure 7049.20 6801.55 -3.51
Finishes 7123.90 6996.35 -1.79
Project final cost 35790.00 34779.55 -2.82
% Project Final Cost: decreased by – 0.61%
Total contingency: reduced by – 2.82%
Contingency % Estimate: (pre-ΔΜ) 19.55% (post-ΔΜ) 19,49%
“Expected contingencies”
Recall:
Pre-ΔΜ: Ε = € 183000
Post-ΔΜ: Ε = € 178423
⇒ Revised estimate: reduced by -0.02%
7. RESEARCH FINDINGS
Project Case 14
% Cost category: increased by +5%

29. All cost elements, apart from “superstructure” become more
efficient ⇒ Move towards “red line” ⇒ keep in budget
29-04-2015 29
7. RESEARCH FINDINGS
Project Case 14
“Project delivery (in)efficiency”

30. 29-04-2015 30
“Risk transfer degree” “Project delivery inefficiency”
Transfer € 401.40
less to client and
again win the bid.
Contractor delivers by
+2.70% more efficient
the project.
7. RESEARCH FINDINGS
Project Case 14
On project level:
CPI +0.02%
improvement
CPI = BCWP / ACWP
= E / C
“Construction Performance Index ”
Portfolio level
On portfolio-level:
√ 14/22 (64% of sample)
scored improved CPI
√ Average improvement = +2%

31. 31
pre-ΔΜ post-ΔΜ Change (€)
Project ID b×(E-C) b’×(E’-C’) Δ (incentive profit)
1 -3365.27 -5193.15 -1827.88
2 -12214.64 -12253.64 -38.65
3 1087.46 63.97 -1023.49
4 -4029.31 -948.66 3080.65
5 915.71 -140.47 775.24
6 -645.09 -121.43 523.66
7 -3816.10 -1007.83 2808.27
8 -11605.68 -1601.58 10004.10
9 -9366.39 -2283.52 7082.86
10 -14524.03 -3433.48 11090.55
11 -46245.14 -22675.80 23569.30
12 -1243.19 -329.20 913.99
13 -13776.86 -4482.99 9293.87
14 -7419.13 -1960.13 5458.99
15 -4165.99 -1228.13 2937.85
16 -41619.68 -18365.10 23524.58
17 -2165.82 -611.63 1554.20
18 -493.53 -28.43 465.11
19 -23918.83 -14719.64 9199.18
20 -5535.54 -4135.05 1400.49
21 -121662.33 -96137.58 25524.76
22 -14251.51 -2736.29 11515.22
Mean= € 6707.42
Recall:
b=risk sharing ratio (0-100%)
E, E’=initial, revised estimate
C, C’=initial cost, revised cost
On portfolio level:
Additional incentive
profit of € 6707.42 ⇒
1.91% of av. value of
portfolio (349994.32 )
“Incentive profit element – Project Final Cost”
7. RESEARCH FINDINGS
Portfolio level

32. 32
pre-ΔΜ post-ΔΜ Change Improvement
Project ID Contigency (€) Contigency (€) Δ (Contingency) % Change
1 39503.40 34779.20 -4724.20 -11.96
2 37358.42 37290.02 -68.40 -0.18
3 3134.93 5439.93 2305.00 73.53
4 15057.82 13607.94 -1449.88 -9.63
5 4601.73 5122.38 520.65 11.31
6 3548.68 2748.53 -800.15 -22.55
7 28139.81 33820.82 5681.01 20.19
8 71270.91 66799.45 -4471.46 -6.27
9 35746.23 35136.95 -609.28 -1.70
10 67153.60 5682.00 -61471.60 -91.54
11 103241.20 99320.90 -3920.30 -3.80
12 4884.30 10872.79 5988.49 122.61
13 51335.30 41406.40 -9928.90 -19.34
14 35790.00 34779.50 -1010.50 -2.82
15 18391.41 13193.68 -5197.73 -28.26
16 126819.76 62490.22 -64329.54 -50.73
17 9047.79 8057.59 -990.20 -10.94
18 9855.67 9097.49 -758.18 -7.69
19 66914.56 59576.69 -7337.87 -10.97
20 7410.36 6908.80 -501.56 -6.77
21 228178.03 211300.50 -16877.53 -7.40
22 145178.44 121776.82 -23401.62 -16.12
Mean= -3.68%
On portfolio level:
Average reduction of
contingencies
achieved by -3.68%
⇒ Still competitive
⇒ More incentive
profits by clients
⇒ More efficient
delivery
7. RESEARCH FINDINGS
“Contingencies – Project Final Cost”
Portfolio level

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“Research Questions → Research Replies”
R.Q. 1a/2a “How much cost risks transferred from contractor to client?”
On average 35.65% less cost risks transferred from contractors to clients
R.Q. 1b/2b “How much likely to meet the estimate the actual cost?”
On average an increase of +5% probability to meet the base estimate.
R.Q. 3 “Consequences of profit decision on profits and contingencies ?”
On average contigencies reduced by -4.32%.
Reduction of contigencies in each phase:
On average profits increased by +6.17%.
Change in profits in each phase:
7. RESEARCH FINDINGS
Project Case 14
Incentive profit (€) Contingencies (€)
pre-ΔΜ post-ΔΜ % change pre-ΔΜ post-ΔΜ % change
Land
preparation
-341.79 -432.69 -26.60 1575.80 1425.90 -9.51
Foundations -422.42 -780.64 -84.80 2845.70 2755.70 -3.16
Substructure -7723.19 -5226.76 +32.32 30630.40 29526.70 -3.60
Superstructure -785.84 -350.57 +55.39 7049.20 6801.55 -3.51
Finishes -738.54 -335.78 +54.53 7123.90 6996.35 -1.79
Mean +6.17% -4.32%
Changes in profit and contingency elements
Any “generilisable”
conclusions?

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7. RESEARCH FINDINGS
Portfolio levelHypothesis:
“ High changes in incentive profits are associated with low changes in contingencies.”
Hypothesis valid!
Not causation can
be deducted.
Contractor can
predict how a
contingency
decision will
affect incentive
profit.
A grouping of
projects. Outliers
(Pr. 21) why?
Different pattern
with 400 projects?

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8. CONCLUSIONS: A SUMMARY
Portfolio level
√ 18/22 (81.8% sample) projects
overestimated initial base estimates.
√ 18/22 (81.8% sample) projects overestimated
initial contingencies.
√ CPI: +2%
√ Incentive profits: +1.91% portfolio’s av. value
Project Case level
√ -35.65% reduction of cost risks ⇒ +5%
increase in the probability meeting the
base estimate
√ Finishes: highest opportunity for cost risks
reduction by -167.35%
Land preparation: highest opportunity to
meet the base estimate by +25.33%
√ CPI: +0.02%
√ -4.32% contingencies ⇒ +6.17% incentive
profits

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9. LIMITATIONS & FURTHER WORK
“Limitations”
■ Sample → 35 questionnaires sent, 22 valid received: small sample
■ Industry
▪ Structure: fragmented with many SMEs
▪ Contractors: based too much on cost control accounting tools, not on RM systems
▪ Nature: legal framework very rigid and in favor of clients
▪ Decreasing profitability path
■ AHP scale → Linear scale selected
■ No validation and verification of cost risk analysis model
“Further work”
■ Pseudo-code integration into @RISK for optimal
contingency setting.
■ Client recommendation with a bidding proposal.

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“Client recommendation”
(Touran 2003)
Poisson distribution for cost risks
Contingency calculation for desired p(%)
Bid also for:
√ T
√ max. α
√ max. χ → public (database)
→ private (consultant)
√ agree post-bid on p
Calculate Cch
Compare contingencies
within the legal range
8. LIMITATIONS & FURTHER WORK
“Pseudo construction”

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MSc. Construction Management & Engineering
Ir. Dimitrios Kordas
Tel.: +31 649 177 841
E-mail: dimitriskordas@gmail.com