IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i10p1956-d233240.html
   My bibliography  Save this article

Using Text Mining to Estimate Schedule Delay Risk of 13 Offshore Oil and Gas EPC Case Studies During the Bidding Process

Author

Listed:
  • Byung-Yun Son

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

  • Eul-Bum Lee

    (Graduate School of Engineering Mastership (GEM), 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)

Abstract

Korean offshore oil and gas (O&G) mega project contractors have recently suffered massive deficits due to the challenges and risks inherent to the offshore engineering, procurement, and construction (EPC) of megaprojects. This has resulted in frequent prolonged projects, schedule delay, and consequently significant cost overruns. Existing literature has identified one of the major causes of project delays to be the lack of adequate tools or techniques to diagnose the appropriateness and sufficiency of the contract deadline proposed by project owners prior to signing the contract in the bid. As such, this paper seeks to propose appropriate or correct project durations using the research methodology of text mining for bid documents. With the emergence of ‘big data’ research, text mining has become an acceptable research strategy, having already been utilized in various industries including medicine, legal, and securities. In this study the scope of work (SOW), as a main part of EPC contracts is analyzed using text mining processes in a sequence of pre-processing, structuring, and normalizing. Lessons learned, collected from 13 executed off shore EPC projects, are then used to reinforce the findings from said process. For this study, critical terms (CT), representing the root of past problems, are selected from the reports of lessons learned. The occurrence of the CT in the SOW are then counted and converted to a schedule delay risk index (SDRI) for the sample projects. The measured SDRI of each sample project are then correlated to the project’s actual schedule delay via regression analysis. The resultant regression model is entitled the schedule delay estimate model (SDEM) for this paper based on the case studies. Finally, the developed SDEM’s accuracy is validated through its use to predict schedule delays on recently executed projects with the findings being compared with actual schedule performance. This study found the relationship between the SDRI, frequency of CTs in the SOW, and delays to be represented by the regression formula. Through assessing its performance with respect to the 13th project, said formula was found to have an accuracy of 81%. As can be seen, this study found that more CTs in the SOW leads to a higher tendency for a schedule delay. Therefore, a higher project SDRI implies that there are more issues on projects which required more time to resolve them. While the low number of projects used to develop the model reduces its generalizability, the text mining research methodology used to quantitatively estimate project schedule delay can be generalized and applied to other industries where contractual documents and information regarding lessons learned are available.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1956-:d:233240
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/10/1956/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/10/1956/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shailendra Singh & Abdulsalam Yassine, 2018. "Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting," Energies, MDPI, vol. 11(2), pages 1-26, February.
    2. Hei Chia Wang & Yung Chang Chi & Ping Lun Hsin, 2018. "Constructing Patent Maps Using Text Mining to Sustainably Detect Potential Technological Opportunities," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
    3. Yakubu Adisa Olawale & Ming Sun, 2010. "Cost and time control of construction projects: inhibiting factors and mitigating measures in practice," Construction Management and Economics, Taylor & Francis Journals, vol. 28(5), pages 509-526.
    4. 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.
    5. 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.
    6. Tiago Pinto & Zita Vale & Isabel Praça & E. J. Solteiro Pires & Fernando Lopes, 2015. "Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning," Energies, MDPI, vol. 8(9), pages 1-26, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Carlos Araújo-Rey & Miguel A. Sebastián, 2021. "An Approach to the Analysis of Causes of Delays in Industrial Construction Projects through Planning and Statistical Computing," Sustainability, MDPI, vol. 13(7), pages 1-21, April.
    2. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. Wu, Junqi & Niu, Zhibin & Li, Xiang & Huang, Lizhen & Nielsen, Per Sieverts & Liu, Xiufeng, 2023. "Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach," Energy, Elsevier, vol. 263(PD).
    5. Xinghua Wang & Fucheng Zhong & Yilin Xu & Xixian Liu & Zezhong Li & Jianan Liu & Zhuoli Zhao, 2023. "Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics," Energies, MDPI, vol. 16(18), pages 1-19, September.
    6. Mihai Daniel Roman & Diana Mihaela Stanculescu, 2021. "An Analysis of Countries’ Bargaining Power Derived from the Natural Gas Transportation System Using a Cooperative Game Theory Model," Energies, MDPI, vol. 14(12), pages 1-13, June.
    7. Xavier Serrano-Guerrero & Guillermo Escrivá-Escrivá & Santiago Luna-Romero & Jean-Michel Clairand, 2020. "A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles," Energies, MDPI, vol. 13(5), pages 1-23, February.
    8. Rongheng Lin & Budan Wu & Yun Su, 2018. "An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering," Energies, MDPI, vol. 11(9), pages 1-17, September.
    9. Zunaira Nadeem & Nadeem Javaid & Asad Waqar Malik & Sohail Iqbal, 2018. "Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes," Energies, MDPI, vol. 11(4), pages 1-30, April.
    10. Miguel López & Carlos Sans & Sergio Valero & Carolina Senabre, 2018. "Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting," Energies, MDPI, vol. 11(8), pages 1-19, August.
    11. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    12. Nyoni, Thabani, 2019. "Cost overrun factors in construction industry: a case of Zimbabwe," MPRA Paper 96788, University Library of Munich, Germany.
    13. Yazdanie, M. & Orehounig, K., 2021. "Advancing urban energy system planning and modeling approaches: Gaps and solutions in perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    14. Rafael Sánchez-Durán & Joaquín Luque & Julio Barbancho, 2019. "Long-Term Demand Forecasting in a Scenario of Energy Transition," Energies, MDPI, vol. 12(16), pages 1-23, August.
    15. Tiago Pinto & Mohammad Ali Fotouhi Ghazvini & Joao Soares & Ricardo Faia & Juan Manuel Corchado & Rui Castro & Zita Vale, 2018. "Decision Support for Negotiations among Microgrids Using a Multiagent Architecture," Energies, MDPI, vol. 11(10), pages 1-20, September.
    16. Petter Osmundsen & Kristin Helen Roll, 2016. "Rig Rates and Drilling Speed: Reinforcing Effects," CESifo Working Paper Series 5895, CESifo.
    17. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    18. Paweł Dymora & Mirosław Mazurek & Bartosz Sudek, 2021. "Comparative Analysis of Selected Open-Source Solutions for Traffic Balancing in Server Infrastructures Providing WWW Service," Energies, MDPI, vol. 14(22), pages 1-23, November.
    19. Patrick Uwineza & Jean de Dieu Dushimimana, 2022. "Project Management Practices And Effectiveness Of Private Radio Station’S Projects A Case Of Selected Projects Of Energy Radio, In Musanze District, Rwanda (2017-2020)," Working papers 2022-42-08, Voice of Research.
    20. Ali Ghofrani & Esmat Zaidan & Mohsen Jafari, 2021. "Reshaping energy policy based on social and human dimensions: an analysis of human-building interactions among societies in transition in GCC countries," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-26, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1956-:d:233240. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.