Working Paper Article Version 1 This version is not peer-reviewed

Project Management Monitoring Based on Expected Duration Entropy

Version 1 : Received: 5 August 2020 / Approved: 7 August 2020 / Online: 7 August 2020 (11:43:14 CEST)

A peer-reviewed article of this Preprint also exists.

Cohen Kashi, S.; Rozenes, S.; Ben-Gal, I. Project Management Monitoring Based on Expected Duration Entropy. Entropy 2020, 22, 905. Cohen Kashi, S.; Rozenes, S.; Ben-Gal, I. Project Management Monitoring Based on Expected Duration Entropy. Entropy 2020, 22, 905.

Journal reference: Entropy 2020, 22, 905
DOI: 10.3390/e22080905

Abstract

Projects are rarely executed exactly as planned. Often, the actual durations of a project’s activities differ from the planned ones, resulting in costs stemming from the inaccurate estimation of the activities’ completion dates. While monitoring the project at various inspection points is pricy, it can lead to better estimation of the project completion time, hence saving on costs. Nonetheless, identifying the optimal inspection points is a difficult task, as it requires evaluating a large number of the project’s path options, even for small-scale projects. This paper proposes an analytical method for identifying the optimal project inspection points by using Information Theory measures. We search for monitoring (inspection) points that can maximize the information about the estimated project’s duration or completion time. The proposed methodology is based on a simulation-optimization scheme using a Monte Carlo engine that simulates potential activities’ durations. An exhaustive search is performed of all possible control points to find those with the highest expected information gain on the project duration. The proposed algorithm’s complexity is not affected the number of activities, and can address large projects with hundreds or thousands of activities. Numerical experimentation and analysis of various parameters are presented.

Subject Areas

Project Management; Information Theory; Uncertainty

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