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Project Makespan Prediction and Risk Analysis Using Simulation: Application in a Seawater Desalination Plant Construction Project

In: Operational Research in the Era of Digital Transformation and Business Analytics

Author

Listed:
  • Georgios K. Koulinas

    (School of Engineering, Democritus University of Thrace)

  • Konstantinos A. Sidas

    (Hellenic Open University)

  • Dimitrios E. Koulouriotis

    (School of Engineering, Democritus University of Thrace)

Abstract

This paper uses a Monte Carlo simulation-based technique to analyze project delay concerns and forecast the probability of timely activity completion. The assignment of statistical distributions by an experienced risk manager illustrates the uncertainty of each activity duration. The main contribution of this paper is the development of an approach for risks quantification and duration prediction applied to a seawater desalination plant construction project. Furthermore, Monte Carlo Simulation is used to assess the degree of risk that each job and the entire project are exposed to and aid the project risk management in precisely estimating the actual project completion time. Also, it is possible to calculate the likelihood of finishing the project by a specific deadline. On the Greek island of Alonissos, the suggested approach was used to estimate the overall project completion time of a seawater desalination plant construction project. Compared to the traditional PERT method, the current methodology gives project risk management many alternatives for dealing with uncertainty about project task durations and critical deadline overruns.

Suggested Citation

  • Georgios K. Koulinas & Konstantinos A. Sidas & Dimitrios E. Koulouriotis, 2023. "Project Makespan Prediction and Risk Analysis Using Simulation: Application in a Seawater Desalination Plant Construction Project," Springer Proceedings in Business and Economics, in: Nikolaos F. Matsatsinis & Fotis C. Kitsios & Michael A. Madas & Maria I. Kamariotou (ed.), Operational Research in the Era of Digital Transformation and Business Analytics, pages 149-157, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-24294-6_16
    DOI: 10.1007/978-3-031-24294-6_16
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