Novel algorithm cuts the risks of choosing ineffectual team members

Franck Marle and Julie Le Cardinal of the Laboratoire Genie Industriel at the Ecole Centrale Paris in Châtenay-Malabry, France, explain that the team members, or actors, assigned to a project are crucial to the future performance of the project. Previous researchers in human resources and the management field have focused on risk analysis of the processes involved in the execution of a project rather than at the critical stage of actor choice. They argue that this is a flawed approach and suggest that such a classical analysis can be improved significantly by identifying the risks associated with choosing team members based on expertise, skills, knowledge, experience, and other personal factors as well as extrapolating to see how a particular choice will affect the end product.

In the context of human resources, risks might be personal failure to complete particular tasks, running over budget because of poor choices made by an individual team member, or using inappropriate or excessive resources for the same reason. All such risks might lead to the overall failure of a given project.

By mapping the components of the decision-making process, the team has devised an algorithm that allows them to correctly estimate the risk associated with any given choice by considering each decision as a node in a complex project network. They have now successfully demonstrated proof of principle in two industrial settings: Vallourec Group and Peugeot-Citroën.

Decision makers already care about their decision-making process and the reliability of their choices and information on a project outcome. But, the current research could make the process much more reliable by taking into account risks associated with team member choice at an earlier stage in the management of a project and identifying the dozens of potential problems that might arise with each choice. The team found with their case studies that the dramatic differences between beliefs and reality can impact drastically on outcomes.