Developing a framework for statistical process control approaches in project management

Different statistical process control (SPC) approaches were proposed over the years for project management using earned value management/earned schedule. A detailed examination of these approaches has led us to express a need for a unified framework in which to test and compare them. The main drivers for this need were the lack of a formal definition for a state of control, the unavailability of a benchmark dataset, the absence of measures to quantify the SPC performance and the lack of consensus on how to overcome and test the normality assumption. In this paper, we present such a framework that combines a classification from empirical data, a known project dataset, a sound simulation model and two quantitative measures for project control efficiency. Four SPC approaches from prior literature have been implemented and an exhaustive experiment was set up to compare and to discuss their value for the project management practice.