Statistical process control for monitoring scheduling performance—addressing the problem of correlated data

In both manufacturing and service operations effective scheduling plays an important role in achieving delivery performance and in utilizing resources economically. Classical scheduling theory takes a narrow, static view of performance. In reality the assessment of scheduling performance is a particularly difficult task. Typically scheduling is an activity that takes place repeatedly over time in the context of an overall planning and control architecture. Scheduling may be viewed as an activity within a process. Statistical Process Control (SPC) provides an attractive option for monitoring performance. In this paper we investigate the potential of applying SPC control charts in this context. The feasibility of monitoring flow time in a single processor model using control charts is studied using simulation. The application of control charts to monitor time-related measures in operational systems raises fundamental statistical problems. The need for approaches that are robust with respect to data correlation and lack of normality is shown to be an essential requirement. Residual-based approaches and the Exponentially Weighted Moving Average (EWMA) chart are shown to be reasonably effective in avoiding false alarms and in detecting process shifts. The applicability of the single processor model to more complex operational systems is discussed. The implications of the work for the design of performance monitoring and continuous improvement systems for time-related measures in manufacturing and service operations are considered. A number of areas are highlighted for further theoretical and practical studies.