A Forecast Model for the Level of Engineering Maturity Impact on Contractor’s Procurement and Construction Costs for Offshore EPC Megaprojects
Abstract
:1. Introduction
1.1. Existing Literature
1.2. Point of Departure and Research Contribution
2. Research Methodology
2.1. Existing DECRIS Model
= Construction labor hours increased by design change/Planned construction labor hours
= Vendor change order cost/Original purchase order price
= Additional engineering labor hours/Planned engineering labor hours
2.2. Artificial Neural Network
a1(2) = g(θ11(1)x1 + θ12(1)x2 + θ13(1)x3 + b1)
a2(2) = g(θ21(1)x1 + θ22(1)x2 + θ23(1)x3 + b2)
a3(2) = g(θ31(1)x1 + θ32(1)x2 + θ33(1)x3 + b3)
a1(3) = h(x) = h(θ11(2)a1(2) + θ12(2)a2(2) + θ13(2)a3(2))
sM = −2h’M(nM)(t − a) and sm = g’m(nm)(Wm+1)Tsm+1, m = M − 1, …, 2, 1
θ m(k + 1) = θ m(k) − αSm(am−1)T and bm(k + 1) = bm(k) − αSm
S(2) = −2h’(2)(n(2))(t − a(2)) and S(1) = g’(1)(n(1))(W(2))TS2
2.3. Trade-Off Using Monte-Carlo Simulation
3. DECRIS Model for Forecasting Procurement Performances
3.1. Definition of Key Engineering Gates for Major Equipment Procurement
3.2. Data Collection from Historical Projects
3.3. Forecasting Procurement Performance Using Artificial Neural Network
0.239/(1 + e−(0.513x − 1.971)) + 1.467/(1 + e−(2.012x − 2.458)) − 0.893
3.4. Forecasting Construction Performance Using Artificial Neural Network
4. Case Study: Forecasting Cost Performance of an EPC Project at Bidding Stage
4.1. DECRIS Assessments of a Sample Project
4.2. Sample Project Performance Prediction (ANN Network Test)
4.3. Mitigation Plan Using Trade-Off Optimization
- Input parameters taken from historical projects are distributed as normal distribution. Its appliance was already verified as shown in the research of Kim et al. [34].
- Ibbs described that productivity in the design phase is decreased up to 80%~93% of normal productivity when project changes occur [8]. In this research, 80% efficiency was considered in case of engineering resource enhancement.
- The slope of input variables against DECRIS scores is not linear as shown in the previous section taken by neural network. However, we used the constant slope of the trend line of linear regression to perform the trade-off optimization.
- 10,000 iterations were considered per one simulation.
- For verification purposes, optimization using at-Risk commercial program was also performed with genetic algorithm.
5. Application and Validation
5.1. Validation of DECRIS Cut-Off Score
5.2. Statistical Comparison of ANN and Regression Result
6. Conclusions
6.1. Summaries and Contributions
- At the bidding session, proposal teams of EPC contractors can review the FEED engineering maturities using the DECRIS model and can estimate the project procurement and construction risks as cost units. Then, EPC contractors can decide whether the cost risks are to be taken or to be incorporated into their bid price as contingency or allowance.
- On the project starting session, project management teams of EPC contractors can calculate the optimum range of engineering resources to minimize the cost impact risks during project execution.
- Before major equipment procurement, purchasing teams of EPC contractors can predict the VCOR and decide when purchase orders of the major equipment could be placed. If the detailed engineering is pre-matured and the expected delivery of the equipment has some free float by comparing a “required on site” date, and then they can adjust the date of the purchase order, when feasible.
- At the construction starting stage that is the most important decision of EPC execution, the EPC contractor’s decision maker can make a macroscopic review of the detailed engineering maturities and judge “go” or “no-go” decision for steel cutting. If construction cost risks caused by poor engineering maturities is not within the acceptable range, they can adjust the steel cutting and continue the engineering progress up to the DECRIS cut-off score.
- During the early stage of EPC execution, clients can monitor the project risks using the DECRIS model. Engineering progress or material procurement status shows just the details of EPC activities; however, it is not easy to project the project cost or the schedule risks with only limited information. With the involvement of a DECRIS assessment team, they can properly monitor the engineering, procurement, and construction risks and request mitigation plans to EPC contractors for reducing DECRIS score over the threshold at each engineering key gate.
6.2. Discussions and Future Research
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviation
ANN | Artificial Neural Network |
CLIR | Construction Labor-hour Increase rate |
DECRIS | Detailed Engineering Completion Rating Index System |
EPC | Engineering, Procurement and Construction |
ERER | Engineering Resource Enhancement Rate |
FEED | Front-End Engineering and Design |
SSE | Sum Squared Error |
VCOR | Vendor Change Order Rate |
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Authors | Project Location | Inaccurate Cost Estimate | Design Changes * |
---|---|---|---|
Thomas and Napolitan [10] | - | O | |
Kaming et al. [5] | Indonesia | O | O |
Kumaraswamy et al. [11] | Hong Kong | O | |
Ssegawa et al. [4] | South Africa | O | |
Frimpong et al. [6] | Ghana | O | O |
Hsieh et al. [3] | Taipei | O | |
Long at al [12] | Vietnam | O | |
Elhag et al. [13] | UK | O | |
Acharya et al. [14] | Korea | O | O |
Arain and Pheng [15] | Singapore | O | |
Moura et al. [16] | Portugal | O | |
Harisaweni [17] | Indonesia | O | O |
Oladapo [18] | Nigeria | O | |
Azhar et al. [19] | Pakistan | O | O |
Le-Hoai et al. [20] | Vietnam | O | O |
El-Razek et al. [21] | Egypt | O | |
Enshassi et al. [22] | Gaza Strip | O | O |
Ameh et al. [23] | Nigeria | O | |
Memon et al. [24] | Malaysia | O | |
Mohammad et al. [25] | Malaysia | O | |
Rahman et al. [26] | Malaysia | O | O |
Halwatura and Ranasinghe [27] | Sri Lanka | O | O |
Shibani and Arumugam [2] | India | O | O |
Gunduz and Maki [28] | - | O | O |
Yadeta [29] | Ethiopia | O | O |
Ogunsanmi [30] | Nigeria | O | |
Creedy et al. [7] | Australia | O | O |
Baloi and Price [31] | Developing Countries | O | O |
Iyer and Jha [32] | India | O | O |
Project | DECRIS Score at Gate1 | DECRIS Score at Gate2 | VCOR | Sampling |
---|---|---|---|---|
A | 817 | 661 | 4.36% | training |
B | 872 | 725 | 22.22% | training |
C | 785 | 630 | 7.14% | training |
D | 822 | 665 | 12.17% | training |
E | 790 | 644 | 1.59% | test |
F | 814 | 687 | 13.48% | training |
G | 806 | 661 | 4.46% | test |
H | 824 | 668 | 3.94% | training |
I | 790 | 628 | 2.93% | training |
J | 799 | 641 | 5.18% | training |
K | 808 | 638 | 2.51% | training |
L | 796 | 630 | 8.55% | training |
M | 813 | 658 | 7.21% | training |
N | 848 | 696 | 7.15% | training |
O | 841 | 701 | 8.11% | test |
Risks | Gate | SSE | ||||
---|---|---|---|---|---|---|
n-4 | n-3 | n-2 | n-2 | n * | ||
CLIR | #1 | 0.0861 | 0.0861 | 0.0861 | 0.0862 | 0.0862 |
#2 | 0.0967 | 0.0966 | 0.0965 | 0.0964 | 0.0963 | |
#3 | 0.0965 | 0.0965 | 0.0964 | 0.0963 | 0.0963 | |
#4 | 0.1025 | 0.102 | 0.1015 | 0.1011 | 0.1006 | |
#5 | 0.0923 | 0.0921 | 0.0919 | 0.0916 | 0.0914 | |
VCOR | #1 | 0.1626 | 0.1626 | 0.1626 | 0.1626 | 0.1626 |
#2 | 0.1633 | 0.1631 | 0.1630 | 0.1628 | 0.1626 |
Project | Project E | Project G | Project O |
---|---|---|---|
Area | South-East Asia | West Africa | South-East Asia |
Project type | Fixed Platform | Floater | Floater |
Project contract type | EPC | EPCIC | EPCI |
Project period | 35 months | 32 months | 39 months |
DECRIS score (Gate 1) | 790 | 806 | 841 |
Actual VCOR | 1.59% | 4.46% | 8.11% |
CLIR | VCOR | |||||||
---|---|---|---|---|---|---|---|---|
Gate | Gate 1 | Gate 2 | Gate 3 | Gate 4 | Gate 5 | Gate 1 | Gate 2 | |
Project G | ANN Forecasting | 4.10% | 5.01% | 6.32% | 2.76% | 6.51% | 5.93% | 6.53% |
Actual | 6.02% | 6.02% | 6.02% | 6.02% | 6.02% | 4.46% | 4.46% | |
Difference | 1.91% | 1.01% | −0.30% | 3.26% | −0.49% | −1.47% | −2.07% | |
Deviation | 0.42 σ | 0.22 σ | −0.07 σ | 0.7 σ | −0.11 σ | −0.26 σ | −0.37 σ | |
Project E | ANN Forecasting | 2.87% | 3.27% | 3.09% | Outlier * | 4.57% | 5.55% | 5.34% |
Actual | 1.61% | 1.61% | 1.61% | Outlier | 1.61% | 1.59% | 1.59% | |
Difference | −1.26% | −1.66% | −1.48% | Outlier | −2.96% | −3.96% | −3.75% | |
Deviation | −0.27 σ | −0.36 σ | −0.32 σ | Outlier | −0.65 σ | −0.71 σ | −0.67 σ |
CLIR | VCOR | ||||||
---|---|---|---|---|---|---|---|
Gate | Gate 1 | Gate 2 | Gate 3 | Gate 4 | Gate 5 | Gate 1 | Gate 2 |
R2 (Linear Regression) | 0.739 | 0.731 | 0.594 | 0.660 | 0.686 | 0.425 | 0.545 |
R2 (ANN) | 0.771 | 0.764 | 0.760 | 0.792 | 0.796 | 0.611 | 0.681 |
R2 Increase | 4.33% | 4.51% | 28.0% | 20.0% | 16.0% | 44.0% | 24.9% |
Mean Error (Linear Regression) | 0.242 | 0.239 | 0.286 | 0.228 | 0.238 | 0.366 | 0.314 |
Mean Error (ANN) | 0.203 | 0.213 | 0.202 | 0.181 | 0.184 | 0.280 | 0.268 |
Mean Error Increase | −16.1% | −10.9% | −29.4% | −20.6% | −22.7% | −23.5% | −14.6% |
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Kim, M.-H.; Lee, E.-B. A Forecast Model for the Level of Engineering Maturity Impact on Contractor’s Procurement and Construction Costs for Offshore EPC Megaprojects. Energies 2019, 12, 2295. https://doi.org/10.3390/en12122295
Kim M-H, Lee E-B. A Forecast Model for the Level of Engineering Maturity Impact on Contractor’s Procurement and Construction Costs for Offshore EPC Megaprojects. Energies. 2019; 12(12):2295. https://doi.org/10.3390/en12122295
Chicago/Turabian StyleKim, Myung-Hun, and Eul-Bum Lee. 2019. "A Forecast Model for the Level of Engineering Maturity Impact on Contractor’s Procurement and Construction Costs for Offshore EPC Megaprojects" Energies 12, no. 12: 2295. https://doi.org/10.3390/en12122295
APA StyleKim, M.-H., & Lee, E.-B. (2019). A Forecast Model for the Level of Engineering Maturity Impact on Contractor’s Procurement and Construction Costs for Offshore EPC Megaprojects. Energies, 12(12), 2295. https://doi.org/10.3390/en12122295