Tolerance limits for project control: An overview of different approaches. (January 2019)

• Record Type: Journal Article
• Authors: Vanhoucke, Mario
• Is Part Of: Computers & industrial engineering. Volume 127(2019)
• Page Start: 467
• Page End: 479
• Publication Date: 2019-01
• Subjects: Project management -- Project control -- Earned Value Management -- Tolerance limits -- Statistical project control; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.10.035
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A summary of project control methods is made. Analytical tolerance limits for the project control are discussed. Statistical project control is critically reviewed. Abstract: Monitoring the performance of projects in progress and controlling their expected outcome by taking corrective actions is a crucial task for any project manager. Project control systems are in use to quantify the project performance at a certain moment in time, and allow the project manager to predict the expected outcome if no action is taken. Consequently, these systems serve as mechanism that provide warning signals that tell the project manager when it is time to take corrective actions to bring the expected project outcome back on track. In order to trust these generated warning signals, the project manager has to set limits on the provide performance metrics that serve as thresholds for these actions. This paper gives an overview of different approaches discussed in the literature to control projects using such actions thresholds. First and foremost, the paper discusses three classes of actions thresholds, ranging from very easy-to-use rules-of-thumb to more advanced statistical project control methodologies. Each of these tools have been the subject to research studies, each of which aim at showing their power to predict project problems during its progress. In addition, the paper will emphasize the fundamental different between statistical project control using tolerance limits and statistical process control for projects. Finally, three different quality metrics to evaluate the performance of such control methods are presented and discussed.