A Framework to Evaluate Project Complexity Using the Fuzzy TOPSIS Method

Hadi Jaber,Ilkan Sarigol,Ludovic-Alexandre Vidal,Franck Marle,Lionel Didiez

doi:10.3390/su13063020

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.

Highlights

This paper aims at answering the following research questions to develop the framework and the score sheet to evaluate project complexity: Which factors make a project more complex? Which classification of these factors is more valuable for industry applications? What could be the benefits of an evaluation of project complexity?
Several authors in the literature tried to define complexity measures to explain project failures, identify intricate situations, understand better project complex phenomena, and help decision-making. These measures can be classified into four categories: informationbased approaches, parametric approaches, project network-based approaches, and project complexity framework-based approaches
Based on project complexity measurement, we aim to provide a complete ranking of projects with quantitative measures, which could be used for project selection or for applying specific risk management actions

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Managing project complexity is known to be a success factor in modern project management [1]. Complex projects require unique project governance and management to adapt to interconnectedness and communication with, and control over, the different stakeholders [2]. The problem with the failure of business projects and entities is a very current topic in the economy [3,4]. Applying proper risk management actions based on complexity level helps in achieving better project success rates

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