Constrained bayesian inference of project performance models

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dc.contributor.authorSunmola, Funlade
dc.date.accessioned2015-09-25T10:30:53Z
dc.date.available2015-09-25T10:30:53Z
dc.date.issued2013-09-19
dc.description.abstractProject performance models play an important role in the management of project success. When used for monitoring projects, they can offer predictive ability such as indications of possible delivery problems. Approaches for monitoring project performance relies on available project information including restrictions imposed on the project, particularly the constraints of cost, quality, scope and time. We study in this paper a Bayesian inference methodology for project performance modelling in environments where information about project constraints is available and can be exploited for improved project performance. We apply the methodology to probabilistic modelling of project S-curves, a graphical representation of a project’s cumulative progress. We show how the methodology could be used to improve confidence bounds on project performance predictions. We present results of a simulated process improvement project in agile setting to demonstrate our approach.en_UK
dc.identifier.citationSunmola F. (2013). Constrained bayesian inference of project performance models. Proceedings of the 11th International Conference on Manufacturing Research (ICMR2013), Cranfield University, UK, 19th – 20th September 2013, pp 575-580en_UK
dc.identifier.isbn9781907413230
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/9512
dc.language.isoenen_UK
dc.publisherCranfield University Pressen_UK
dc.relation.ispartofseriesQuality and Performance Measurementen_UK
dc.relation.ispartofseries4en_UK
dc.subjectBayesian Inferenceen_UK
dc.subjectProject Performance Modelen_UK
dc.subjectS-Curveen_UK
dc.subjectProject Constraintsen_UK
dc.titleConstrained bayesian inference of project performance modelsen_UK
dc.typeConference paperen_UK

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