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Setting tolerance limits for statistical project control using earned value management
Colin, Jeroen Vanhoucke, Mario
Colin, Jeroen
Vanhoucke, Mario
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2014
Journal
Omega - International Journal of Management Science
Book
Publication Volume
49
Publication Issue
December
Publication Begin page
107
Publication End page
122
Publication Number of pages
Collections
Abstract
Project control has been a research topic since decades that attracts both academics and practitioners. Project control systems indicate the direction of change in preliminary planning variables compared with actual performance. In case their current project performance deviates from the planned performance, a warning is indicated by the system in order to take corrective actions. Earned value management/earned schedule (EVM/ES) systems have played a central role in project control, and provide straightforward key performance metrics that measure the deviations between planned and actual performance in terms of time and cost. In this paper, a new statistical project control procedure sets tolerance limits to improve the discriminative power between progress situations that are either statistically likely or less likely to occur under the project baseline schedule. In this research, the tolerance limits are derived from subjective estimates for the activity durations of the project. Using the existing and commonly known EVM/ES metrics, the resulting project control charts will have an improved ability to trigger actions when variation in a project׳s progress exceeds certain predefined thresholds A computational experiment has been set up to test the ability of these statistical project control charts to discriminate between variations that are either acceptable or unacceptable in the duration of the individual activities. The computational experiments compare the use of statistical tolerance limits with traditional earned value management thresholds and validate their power to report warning signals when projects tend to deviate significantly from the baseline schedule.
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Keywords
Project Management, Scheduling, Risk, Simulation