Franck Marle

Understanding project complexity: implications on project management

Purpose – The purpose of this paper is to better identify, define and model complexity within the field of project management in order to manage better under conditions of complexity (and manage better complexity‐induced risks).Design/methodology/approach – An extensive literature review enlightens the lack of consensus on project complexity and thus provides a broad view and a critical analysis of the underlying concepts. A project complexity framework and definition are then proposed. After underlining the stakes of project complexity in accordance with these proposals, a project complexity model is then built notably due to systems analysis.Findings – Proposal of standard project complexity framework and definition. Proposal of a synthesis of the relationships between the concepts of project uncertainty and project complexity. Proposal of a project complexity model (and validation due to industrial application).Research limitations/implications – The literature review and project complexity framework t...

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. Project complexity is ever growing and needs to be understood, analysed and measured better to assist modern project management. The overall ambition of this paper is therefore to define a measure of project complexity in order to assist decision-making, notably when analysing several projects in a portfolio, or when studying different areas of a project. A synthesised literature review on existing complexity measures is firstly proposed in order to highlight their limitations. Then, we identify the multiple aspects of project complexity thanks to the construction and refinement of a project complexity framework thanks to an international Delphi study. We then propose a multi-criteria approach to project complexity evaluation, underlining the benefits of such an approach. In order to solve properly this multi-criteria problem, we first conduct a critical state of the art on multi-criteria methodologies. We then argue for the use of the Analytic Hierarchy Process. In the end, this tool permits to define a relative project complexity measure, which can notably assist decision-making. Complexity scales and subscales are defined in order to highlight the most complex alternatives and their principal sources of complexity within the set of criteria and sub-criteria which exist in the hierarchical structure. Finally, a case study within a start-up firm in the entertainment industry (musicals production) is performed. Conclusions, limitations and perspectives of research are given in the end.

A simulation-based risk network model for decision support in project risk management

This paper presents a decision support system (DSS) for the modeling and management of project risks and risk interactions. This is a crucial activity in project management, as projects are facing a growing complexity with higher uncertainties and tighter constraints. Existing classical methods have limitations for modeling the complexity of project risks. For example, some phenomena like chain reactions and loops are not properly taken into account. This will influence the effectiveness of decisions for risk response planning and will lead to unexpected and undesired behavior in the project. Based on the concepts of DSS and the classical steps of project risk management, we develop an integrated DSS framework including the identification, assessment and analysis of the risk network. In the network, the nodes are the risks and the edges represent the cause and effect potential interactions between risks. The proposed simulation-based model makes it possible to re-evaluate risks and their priorities, to suggest and test mitigation actions, and then to support project manager in making decisions regarding risk response actions. An example of application is provided to illustrate the utility of the model.

Network theory-based analysis of risk interactions in large engineering projects

This paper presents an approach based on network theory to deal with risk interactions in large engineering projects. Indeed, such projects are exposed to numerous and interdependent risks of various nature, which makes their management more difficult. In this paper, a topological analysis based on network theory is presented, which aims at identifying key elements in the structure of interrelated risks potentially affecting a large engineering project. This analysis serves as a powerful complement to classical project risk analysis. Its originality lies in the application of some network theory indicators to the project risk management field. The construction of the risk network requires the involvement of the project manager and other team members assigned to the risk management process. Its interpretation improves their understanding of risks and their potential interactions. The outcomes of the analysis provide a support for decision-making regarding project risk management. An example of application to a real large engineering project is presented. The conclusion is that some new insights can be found about risks, about their interactions and about the global potential behavior of the project.

Dealing with project complexity by matrix-based propagation modelling for project risk analysis

Engineering projects are facing a growing complexity and are thus exposed to numerous and interdependent risks. In this paper, we present a quantitative method for modelling propagation behaviour in the project risk network. The construction of the network requires the involvement of the project manager and related experts using the design structure matrix method. A matrix-based risk propagation model is introduced to calculate risk propagation and thus to re-evaluate risk characteristics such as probability and criticality. An eigenstructure analysis is also used based on the risk network, with the goal of measuring and prioritising risks with respect to their importance in terms of influence in the network. These supplemental project risk analyses provide project managers with improved insights into risks considering complexity and help them to design more effective response actions. An example of an application to a real urban transportation system implementation project is presented to illustrate the utility of the proposed approach.

An Integrated Framework for Risk Response Planning Under Resource Constraints in Large Engineering Projects

Engineering project managers often face a challenge to allocate tight resources for managing interdependent risks. In this paper, a quantitative framework of analysis for supporting decision making in project risk response planning is developed and studied. The design structure matrix representation is used to capture risk interactions and build a risk propagation model for predicting the global mitigation effects of risk response actions. For exemplification, a genetic algorithm is used as a tool for choosing response actions and allocating budget reserves. An application to a real transportation construction project is also presented. Comparison with a sequential forward selection greedy algorithm shows the superiority of the genetic algorithm search for optimal solutions, and its flexibility for balancing mitigation effects and required budget.

A systems thinking approach for project vulnerability management

Purpose – The purpose of this paper is to develop the concept of project vulnerability in order to focus on the weaknesses of a project system, instead of focusing on risk evaluation only. The paper concentrates on a systems thinking‐based view to highlight the potentially endangered elements of a project, including its outcomes.Design/methodology/approach – The paper gives a broad state of the art in many scientific domains; a definition of project vulnerability; a description of a project vulnerability management process, including identification, analysis and response plan; and a test on an industrial case study.Findings – The authors project vulnerability management process permits one to concentrate directly on the existing weaknesses of a project system, which may create potential damages regarding the project values creation. By focusing on this system, response plans may be more adapted to the existing short comings of the project.Research limitations/implications – Some aspects of the vulnerabil...

Forming Risk Clusters in Projects to Improve Coordination between Risk Owners

AbstractDue to the growing complexity of projects, their risks have increased in number and criticality. Risk lists thus need to be broken down into smaller, more manageable clusters. Classical clustering techniques are generally based on a single parameter, like risk nature, criticality, or ownership. Risk interactions are therefore not properly considered when building up clusters. That is why this paper aims at grouping risks so that the communication and coordination between the actors who are committed in the management of the project and its risks are facilitated. This paper is based on an optimization algorithm that maximizes interaction rate within the risk clusters. This paper focuses on two additional points. First, the optimization problem formulation is enriched by some constraints related to the risk owners, not only to the risks. Second, a frequency approach is introduced to test different configurations in order to improve the robustness of the clustering decision. It enables meaningful and...

Improving Collaborative Decision Making in New Product Development Projects Using Clustering Algorithms

Numerous decisions have to be made in early design processes. Often times they involve many actors, with the difficulty that they are shared across numerous parallel collaborative groups, for coordination and meeting scheduling reasons. This paper aims at facilitating collaborative decision-making process by grouping actors according to the relationships they have due to their assignment to decisions. Clusters of actors are proposed in order to provide decision makers with a temporary and complementary organization designed for making efficiently simultaneous collaborative decisions. This approach has been illustrated through actual data in a new product development project in the automotive industry.

A Framework for the Modeling and Management of Project Risks and Risk Interactions

Nowadays, projects are facing a growing complexity and are thus exposed to numerous and interdependent risks. However, existing methods have limitations for modeling the real complexity of project risks. In this chapter, a four-phase framework for project risk management is proposed. It not only deals with project risks in terms of their probability and impact, but also brings in the modeling of risk interactions. Through identifying and assessing risks and risk interactions, a project risk network is constructed to represent the complexity of project risks. A quantitative model is then developed to describe the propagation behavior in the risk network for refining the risk analysis results. A numerical example is given to illustrate how to apply the framework in practice. The proposed approach provides more insights on project risks and risk interactions for their management, and can be used as a powerful complement to the classical methods for subsequent decision support.