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A Digitalized Design Risk Analysis Tool with Machine-Learning Algorithm for EPC Contractor’s Technical Specifications Assessment on Bidding

Author

Listed:
  • Min-Ji Park

    (Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Nam-Ku, Pohang 37673, Korea)

  • Eul-Bum Lee

    (Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Nam-Ku, Pohang 37673, Korea
    Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Nam-Ku, Pohang 37673, Korea)

  • Seung-Yeab Lee

    (Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Nam-Ku, Pohang 37673, Korea)

  • Jong-Hyun Kim

    (WISEiTECH Co., Pangyo Inovalley, 253 Pangyo-ro, Bundang-gu, Seongnam 13486, Korea)

Abstract

Engineering, Procurement, and Construction (EPC) projects span the entire cycle of industrial plants, from bidding to engineering, construction, and start-up operation and maintenance. Most EPC contractors do not have systematic decision-making tools when bidding for the project; therefore, they rely on manual analysis and experience in evaluating the bidding contract documents, including technical specifications. Oftentimes, they miss or underestimate the presence of technical risk clauses or risk severity, potentially create with a low bid price and tight construction schedule, and eventually experience severe cost overrun or/and completion delays. Through this study, two digital modules, Technical Risk Extraction and Design Parameter Extraction, were developed to extract and analyze risks in the project’s technical specifications based on machine learning and AI algorithms. In the Technical Risk Extraction module, technical risk keywords in the bidding technical specifications are collected, lexiconized, and then extracted through phrase matcher technology, a machine learning natural language processing technique. The Design Parameter Extraction module compares the collected engineering standards’ so-called standard design parameters and the plant owner’s technical requirements on the bid so that a contractor’s engineers can detect the difference between them and negotiate them. As described above, through the two modules, the risk clauses of the technical specifications of the project are extracted, and the risks are detected and reconsidered in the bidding or execution of the project, thereby minimizing project risk and providing a theoretical foundation and system for contractors. As a result of the pilot test performed to verify the performance and validity of the two modules, the design risk extraction accuracy of the system module has a relative advantage of 50 percent or more, compared to the risk extraction accuracy of manual evaluation by engineers. In addition, the speed of the automatic extraction and analysis of the system modules are 80 times faster than the engineer’s manual analysis time, thereby minimizing project loss due to errors or omissions due to design risk analysis during the project bidding period with a set deadline.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5901-:d:637723
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    References listed on IDEAS

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    1. 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.
    2. Murat Gunduz & Mohammed Almuajebh, 2020. "Critical Success Factors for Sustainable Construction Project Management," Sustainability, MDPI, vol. 12(5), pages 1-17, March.
    3. 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.
    4. 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.
    5. Sung-O Kang & Eul-Bum Lee & Hum-Kyung Baek, 2019. "A Digitization and Conversion Tool for Imaged Drawings to Intelligent Piping and Instrumentation Diagrams (P&ID)," Energies, MDPI, vol. 12(13), pages 1-26, July.
    6. Vasile Dumbravă & Vlăduț - Severian Iacob, 2013. "Using Probability – Impact Matrix in Analysis and Risk Assessment Projects," Journal of Knowledge Management, Economics and Information Technology, ScientificPapers.org, vol. 3(6), pages 1-7, December.
    7. 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.
    8. Stetter, Chris & Piel, Jan-Hendrik & Hamann, Julian F.H. & Breitner, Michael H., 2020. "Competitive and risk-adequate auction bids for onshore wind projects in Germany," Energy Economics, Elsevier, vol. 90(C).
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    1. So-Won Choi & Eul-Bum Lee, 2022. "Contractor’s Risk Analysis of Engineering Procurement and Construction (EPC) Contracts Using Ontological Semantic Model and Bi-Long Short-Term Memory (LSTM) Technology," Sustainability, MDPI, vol. 14(11), pages 1-32, June.

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