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Detail Engineering Completion Rating Index System (DECRIS) for Optimal Initiation of Construction Works to Improve Contractors’ Schedule-Cost Performance for Offshore Oil and Gas EPC Projects

Author

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  • Myung-Hun Kim

    (POSTECH University & Hyundai Heavy Industries, Engineering Management Team, 400 Bangeojinsunhwan-doro, Dong-gu, Ulsan 44114, Korea
    Graduate Institute of Ferrous Technology & Graduate School of Engineering Mastership, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Nam-Ku, Pohang 37673, Korea)

  • Eul-Bum Lee

    (Graduate Institute of Ferrous Technology & Graduate School of Engineering Mastership, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Nam-Ku, Pohang 37673, Korea)

  • Han-Suk Choi

    (Graduate Institute of Ferrous Technology & Graduate School of Engineering Mastership, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Nam-Ku, Pohang 37673, Korea)

Abstract

Engineering, Procurement, and Construction (EPC) contractors with lump-sum turnkey contracts have recently been suffering massive profit losses due to re-works and schedule delays in offshore oil and gas EPC megaprojects. The main objective of this research is to develop and implement a detail engineering completion rating index system (DECRIS) to assist EPC contractors to optimize fabrication and construction works schedules while minimizing potential re-work/re-order. This is achieved through adequate detail design development and results in minimizing schedule delays and potential liquidated damages (i.e., delay penalties). The developed DECRIS was based on findings from an extensive review of existing literature, industry-led studies, expert surveys, and expert workshops. The DECRIS model is an evolution, and improvement of existing tools such as the project definition raking index (PDRI) and front-end loading (FEL) developed specifically for the early stage of engineering maturity assessment (i.e., planning, basic design, and front-end engineering design (FEED)), prior to EPC projects. The DECRIS was evaluated and validated with thirteen sample as-built offshore megaprojects completed recently. When the DECRIS was applied to the completed projects post-hoc, a correlation (R-squared 0.71) was found between DECRIS scores and schedule/cost performances. This is much superior to the PDRI-Industrial model’s correlation (R-squared 0.04), which was primarily devised for owners’ basic engineering or FEED completion assessment. Finally, as a means of further validation, project schedule and cost performance of an ongoing project was predicted based on the correlations found on the thirteen completed projects. The resultant predicted schedule and cost performance was well matched with the current project performance status. Based on the accuracy of the DECRIS model found in the validation, said model is an effective prospective tool for EPC contractors to manage their engineering and procurement/construction risks during the initial detail design stages.

Suggested Citation

  • Myung-Hun Kim & Eul-Bum Lee & Han-Suk Choi, 2018. "Detail Engineering Completion Rating Index System (DECRIS) for Optimal Initiation of Construction Works to Improve Contractors’ Schedule-Cost Performance for Offshore Oil and Gas EPC Projects," Sustainability, MDPI, vol. 10(7), pages 1-31, July.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2469-:d:157986
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    Citations

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    Cited by:

    1. Daekyoung Yi & Eul-Bum Lee & Junyong Ahn, 2019. "Onshore Oil and Gas Design Schedule Management Process Through Time-Impact Simulations Analyses," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    2. Ahsan Waqar & Idris Othman & Roberto Alonso González-Lezcano, 2023. "Challenges to the Implementation of BIM for the Risk Management of Oil and Gas Construction Projects: Structural Equation Modeling Approach," Sustainability, MDPI, vol. 15(10), pages 1-28, May.
    3. Myung-Hun Kim & 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, MDPI, vol. 12(12), pages 1-18, June.
    4. Byung-Yun Son & Eul-Bum Lee, 2019. "Using Text Mining to Estimate Schedule Delay Risk of 13 Offshore Oil and Gas EPC Case Studies During the Bidding Process," Energies, MDPI, vol. 12(10), pages 1-25, May.
    5. Min-Ji Park & Eul-Bum Lee & Seung-Yeab Lee & Jong-Hyun Kim, 2021. "A Digitalized Design Risk Analysis Tool with Machine-Learning Algorithm for EPC Contractor’s Technical Specifications Assessment on Bidding," Energies, MDPI, vol. 14(18), pages 1-31, September.
    6. Jesus Javier Losada-Maseda & Laura Castro-Santos & Manuel Ángel Graña-López & Ana Isabel García-Diez & Almudena Filgueira-Vizoso, 2020. "Analysis of Contracts to Build Energy Infrastructures to Optimize the OPEX," Sustainability, MDPI, vol. 12(17), pages 1-17, September.
    7. Myung-Hun Kim & Eul-Bum Lee & Han-Suk Choi, 2019. "A Forecast and Mitigation Model of Construction Performance by Assessing Detailed Engineering Maturity at Key Milestones for Offshore EPC Mega-Projects," Sustainability, MDPI, vol. 11(5), pages 1-21, February.
    8. So-Won Choi & Eul-Bum Lee & Jong-Hyun Kim, 2021. "The Engineering Machine-Learning Automation Platform ( EMAP ): A Big-Data-Driven AI Tool for Contractors’ Sustainable Management Solutions for Plant Projects," Sustainability, MDPI, vol. 13(18), pages 1-33, September.
    9. Su Jin Choi & So Won Choi & Jong Hyun Kim & Eul-Bum Lee, 2021. "AI and Text-Mining Applications for Analyzing Contractor’s Risk in Invitation to Bid (ITB) and Contracts for Engineering Procurement and Construction (EPC) Projects," Energies, MDPI, vol. 14(15), pages 1-28, July.

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