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dc.contributor.authorVanhoucke, Mario
dc.contributor.authorBatselier, Jordy
dc.date.accessioned2019-10-04T08:17:40Z
dc.date.available2019-10-04T08:17:40Z
dc.date.issued2019en_US
dc.identifier.issn1099-4300
dc.identifier.doi10.3390/e21100952
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6390
dc.description.abstractJust like any physical system, projects have entropy that must be managed by spending energy. The entropy is the project’s tendency to move to a state of disorder (schedule delays, cost overruns), and the energy process is an inherent part of any project management methodology. In order to manage the inherent uncertainty of these projects, accurate estimates (for durations, costs, resources, …) are crucial to make informed decisions. Without these estimates, managers have to fall back to their own intuition and experience, which are undoubtedly crucial for making decisions, but are are often subject to biases and hard to quantify. This paper builds further on two published calibration methods that aim to extract data from real projects and calibrate them to better estimate the parameters for the probability distributions of activity durations. Both methods rely on the lognormal distribution model to estimate uncertainty in activity durations and perform a sequence of statistical hypothesis tests that take the possible presence of two human biases into account. Based on these two existing methods, a new so-called statistical partitioning heuristic is presented that integrates the best elements of the two methods to further improve the accuracy of estimating the distribution of activity duration uncertainty. A computational experiment has been carried out on an empirical database of 83 empirical projects. The experiment shows that the new statistical partitioning method performs at least as good as, and often better than, the two existing calibration methods. The improvement will allow a better quantification of the activity duration uncertainty, which will eventually lead to a better prediction of the project schedule and more realistic expectations about the project outcomes. Consequently, the project manager will be able to better cope with the inherent uncertainty (entropy) of projects with a minimum managerial effort (energy).en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectProject Managementen_US
dc.subjectEntropyen_US
dc.subjectManagerial Efforten_US
dc.subjectDistribution Fittingen_US
dc.subjectLognormal Distributionen_US
dc.titleA statistical method for estimating activity uncertainty parameters to improve project forecastingen_US
dc.identifier.journalEntropyen_US
dc.source.volume21en_US
dc.source.issue10en_US
dc.source.beginpage1en_US
dc.source.endpage28en_US
dc.contributor.departmentFaculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, 9000 Gent, Belgiumen_US
vlerick.knowledgedomainOperations & Supply Chain Managementen_US
vlerick.typearticleJournal article with impact factoren_US
vlerick.vlerickdepartmentTOMen_US
dc.identifier.vperid58614en_US


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