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A Framework to Evaluate Project Complexity Using the Fuzzy TOPSIS Method

Author

Listed:
  • Hadi Jaber

    (College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait)

  • Franck Marle

    (Laboratoire Genie Industriel, CentraleSupélec, Université Paris-Saclay, 3 Rue Joliot-Curie, 91190 Gif-sur-Yvette, France)

  • Ludovic-Alexandre Vidal

    (Laboratoire Genie Industriel, CentraleSupélec, Université Paris-Saclay, 3 Rue Joliot-Curie, 91190 Gif-sur-Yvette, France)

  • Ilkan Sarigol

    (College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait)

  • Lionel Didiez

    (Groupe Renault S.A., Strategy of Quality Management Department, 1 Avenue du Golf, 78280 Guyancourt, France)

Abstract

This work aims to help managers anticipate, detect, and keep under control complex situations before facing negative consequences. This article explores complexity modeling theory and develops a framework and associated score sheet to measure project complexity. A framework comprising ninety factors is presented and divided into seven categories: stakeholders, project team, project governance, product, project characteristics, resources, and environment. For the project complexity assessment grid, the project manager prioritizes and weighs its factors using linguistic variables. The score sheet is customizable in its handling of the factors and their weights. A critical state of the art on multi-criteria methodologies is presented, as well as reasons for using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. This method provides early-warning signs with the possibility of comparing multiple projects. It also enables one to measure and prioritize areas and domains where complexity may have the highest impact. Practical applications on three projects within an automotive manufacturer highlight the benefits of such an approach for managers. Project managers could use both a project complexity rating system and a measure of risk criticality to decide on the level of proactive actions needed. This research work differs from traditional approaches that have linked proactive actions to risk criticality but not project complexity.

Suggested Citation

  • Hadi Jaber & Franck Marle & Ludovic-Alexandre Vidal & Ilkan Sarigol & Lionel Didiez, 2021. "A Framework to Evaluate Project Complexity Using the Fuzzy TOPSIS Method," Sustainability, MDPI, vol. 13(6), pages 1-35, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3020-:d:514148
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    References listed on IDEAS

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    Cited by:

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    3. Yanhua Chang & Yi Liang, 2023. "Intelligent Risk Assessment of Ecological Agriculture Projects from a Vision of Low Carbon," Sustainability, MDPI, vol. 15(7), pages 1-21, March.

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