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dc.contributor.authorVanhoucke, Mario
dc.date.accessioned2019-01-18T09:57:43Z
dc.date.available2019-01-18T09:57:43Z
dc.date.issued2019
dc.identifier.issn0360-8352
dc.identifier.doi10.1016/j.cie.2018.10.035
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6123
dc.description.abstractMonitoring 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.
dc.language.isoen
dc.publisherElsevier
dc.subjectProject Management
dc.subjectProject Control
dc.subjectEarned Value Management
dc.subjectTolerance Limits
dc.subjectStatistical Project Control
dc.titleTolerance limits for project control: An overview of different approaches
dc.identifier.journalComputers and Industrial Engineering
dc.source.volume127
dc.source.issueJanuary
dc.source.beginpage467
dc.source.endpage479
vlerick.knowledgedomainOperations & Supply Chain Management
vlerick.typearticleJournal article with impact factor
vlerick.vlerickdepartmentTOM
dc.identifier.vperid58614


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