Using a Delphi process and the Analytic Hierarchy Process (AHP) to evaluate the complexity of projects
Research highlights
► Describing, defining, and understanding better project complexity and its measures. ► Building up a generic project complexity framework. ► Performing a Delphi study about project complexity factors. ► Building up an AHP-based multi-criteria evaluation of project complexity. ► Exploring the sensitivity of such a measure.
Introduction
A project is a temporary and unique endeavour undertaken to deliver a result. This result is always a change in the organization, whatever it is in its processes, performance, products or services. This transformation consists then in a gap between a start and a final state. Time and resources are consumed to produce results, which may be deliverables and/or performance improvement and/or resource improvement (skills, knowledge). Each project is unique because there is always at least one of the following parameters that changes: targets, resources and environment. As projects became more and more present into organizations, and as they had bigger and bigger amounts at stake, it became impossible to let them live without specific and rigorous methodology. As a consequence, project management was created as a formalized and structured methodology. It is usually admitted than modern project management appeared during World War II and was initially dedicated to big military and construction projects.
For all practical purposes, lots of studies have been done, based on statistical calculations or surveys. Limits and lacks have been detected in research as well as in industry about the project predictability, since usual parameters (time, cost and quality) are clearly not sufficient to describe properly the complete situation at a given time. As a whole, the conclusion of these studies is that current methods have shown their limits, since they cannot face anymore the stakes of ever growing project complexity. For instance, as noted during discussions with consulting practitioners, in the case of oil industry, it is clear that engineering projects today are larger, involve more sophisticated technology and are organised with a higher number of contractors and partners compared to 40 years ago. As a whole, project complexity results in damages or failures for the projects. In other words, project ever growing complexity is an ever growing source of project risks.
Identifying existing project complexity sources and levels of project complexity has thus become a crucial issue in order to assist modern project management. After a review of the literature on project complexity in Section 2, this paper aims at proposing an innovative measure of project complexity which incorporates the multiple aspects of this project characteristic. In order to do so, a methodology is exposed in Section 3 and followed in the next sections. Conclusions and research perspectives about this issue are finally given in Section 9.
Section snippets
Existing project complexity measures
Several authors tried to define complexity measures in order to explain project failures, to identify intricate situations, to understand better project complex phenomena and to help decision-making. Indeed, such a measure is notably to assist decision-makers before engaging their projects/portfolios into too complex situations since too early decisions when facing complex and uncertain situations often fail to deliver the targeted performance.
But before choosing a suitable project complexity
Research methodology
In order to do so, we propose to carry out the following methodology:
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Performing a broad state of the art to understand the multiple aspects of project complexity through the construction of a project complexity framework (Section 4).
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Identifying the most suitable multi-criteria decision methodology for project complexity evaluation and understanding its principles (Section 5)
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Refining the project complexity framework thanks to an international Delphi study in order to permit the construction of a
Systems thinking
In order to understand and handle project complexity, this paper claims for the use of systems thinking to facilitate the management of projects. Basically, our systems thinking-based approach is notably based on or at least consistent with the works of Bocquet et al., 2007, Boulding, 1956, Heylighen et al., 2006, Le Moigne, 1990, Penalva, 1997, Simon, 1981, Vidal et al., 2007, or Von Bertalanffy (1972). This paper considers the following definition of a system. Definition A system can be defined as anAdapted from Vidal et al. (2007)
Multi-criteria decision methodologies
In general, decision-making is the study of identifying and choosing alternatives based on the values and preferences of the decision-maker. Making a decision implies that some alternatives are to be considered, and that one chooses the alternative(s) that possibly best fits with the goals, objectives, desires and values of the problem.
A deeper look at the literature reveals that the problem of selecting the appropriate method appears itself to be a multi-criteria problem: this multiple
The need to refine the framework
As seen before, complexity appears as a multiple aspects or multiple criteria characteristic of a project. Measuring it thanks to the AHP should thus require the consideration of these multiple aspects. However, according to Baker et al. (2001), criteria used in multi-criteria decision making methods should be:
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Able to discriminate among the alternatives and to support the comparison of the performance of the alternatives,
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Complete to include all goals,
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Operational and meaningful,
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Non-redundant,
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Few
Proposing a project complexity index
As a whole, in the AHP hierarchy proposed in the formulation of our problem, the decision-maker formulates several pairwise comparison matrices:
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A matrix C comparing the four criteria to one another (size, variety, interdependency and context-dependence), the eigenvector of which permits to identify the relative weights of these categories W1, W2, W3, W4.
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For each j from 1 to 4, let Kj be the number of sub-criteria corresponding to criterion Cj. Each matrix SCj, of size Kj∗ Kj, permit to compute K
Conclusions and perspectives
As a whole, this paper elaborates an AHP-based methodology and measure to evaluate relative project complexity. The works proposed here answer the problem which was set after the literature review on existing (project) complexity measures. Indeed, as shown theoretically and validated with a first case study, the project complexity index proposed here permits to overcome to a great extent the limits of existing ones as it is:
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Reliable, since the final users are confident with the results,
Acknowledgements
The authors thank the employees of the start-up firm for their support to this work.
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