On the use of multivariate regression methods for longest path calculations from earned value management observations |
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| Institution: | 1. Faculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, 9000 Ghent, Belgium;2. Technology and Operations Management Area, Vlerick Business School, Reep 1, 9000 Ghent, Belgium;3. Department of Management Science and Innovation, University College London, Gower Street, London WC1E 6BT, United Kingdom;1. Faculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, 9000 Gent Belgium;2. School of management, Northwestern Polytechnical University, Xi’an, Shaanxi 710129, PR China;3. Technology and Operations Management, Vlerick Business School, Reep 1, 9000 Gent Belgium;4. UCL School of Management, University College London, 1 Canada Square, London E14 5AA United Kingdom;1. Faculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, 9000 Ghent, Belgium;2. Technology and Operations Management Area, Vlerick Business School, Reep 1, 9000 Ghent, Belgium;3. Department of Management Science and Innovation, University College London, Gower Street, London WC1E 6BT, United Kingdom;1. Department of Business and Management, National University of Tainan, No. 33, Sec. 2, Shu-Lin St., Tainan 700, Taiwan;2. Department of Construction Engineering, National Yunlin University of Science & Technology, No. 123, Section 3, University Rd., Douliu, Yunlin 640 Taiwan;3. Department of Industrial and Information Management, National Cheng Kung University, Tainan City 701, Taiwan |
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| Abstract: | This paper explores the use of multivariate regression methods for project schedule control within a statistical project control framework. These multivariate regression methods monitor the activity level performance of an ongoing project from the earned value management/earned schedule (EVM/ES) observations that are made at a high level of the work breakdown structure (WBS). These estimates can be used to calculate the longest path in the project and to produce warning signals for project schedule control. The effort that is spent by the project manager is thereby reduced, since a drill-down of the WBS is no longer required for every review period. An extensive computational experiment was set up to test and compare four distinct multivariate regression methods on a database of project networks. The kernel principal component regression method, when used with a radial base function kernel, was found to outperform the other presented regression methods. |
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| Keywords: | Project management Scheduling Risk Simulation |
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