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dc.contributor.authorAndré de Andrade, Paulo
dc.contributor.authorMartens, Annelies
dc.contributor.authorVanhoucke, Mario
dc.date.accessioned2020-12-03T12:18:19Z
dc.date.available2020-12-03T12:18:19Z
dc.date.issued2019en_US
dc.identifier.issn0926-5805
dc.identifier.doi10.1016/j.autcon.2018.11.030
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6599
dc.description.abstractSince project control involves taking decisions that affect the future, the ability to accurately forecast the final duration and cost of projects is of major importance. In this paper, we focus on improving the accuracy of project duration forecasting by introducing a forecasting approach for Earned Value Management (EVM) and Earned Duration Management (EDM) that combines the schedule performance and schedule adherence of the project in progress. As the schedule adherence has not yet been defined formally for EDM, we extend the EVM-based measure of schedule adherence, the p-factor, to EDM and refer to this measure as the c-factor. Moreover, we aim to improve the ability to indicate the expected forecasting accuracy for a project by extending the EVM concept of project regularity to EDM. The introduced forecasting approach and the EDM project regularity indicator are applied to a large number of real-life projects, mainly situated in the construction sector. The conducted empirical experiment shows that the project duration forecasting accuracy can be increased by focusing on both the schedule performance and schedule adherence. Further, this study shows that the EDM project regularity indicator is indeed a more reliable indicator of forecasting accuracy.en_US
dc.language.isoenen_US
dc.subjectProject managementen_US
dc.subjectEarned Duration Managementen_US
dc.subjectEarned Scheduleen_US
dc.subjectTime Forecastingen_US
dc.subjectEmpirical Databaseen_US
dc.subjectProject Regularityen_US
dc.subjectProject Control Systemen_US
dc.titleUsing real project schedule data to compare earned schedule and earned duration management project time forecasting capabilitiesen_US
dc.identifier.journalAutomation in Constructionen_US
dc.source.volume99en_US
dc.source.issueMarchen_US
dc.source.beginpage68en_US
dc.source.endpage78en_US
dc.contributor.departmentTechisa do Brasil, Brazilen_US
dc.contributor.departmentFaculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, Gent 9000, Belgiumen_US
dc.contributor.departmentUCL School of Management, University College London, 1 Canada Square, London E14 5AA, United Kingdomen_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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