Applying a change-point control chart based on likelihood ratio to supply chain network monitoring

Abstract

A supply chain network system was regarded as a serial-parallel multistage process and the application of a change-point control chart based on likelihood ratio was explored to monitor this system. First, state-space modeling was used to characterize complexities of the supply chain network system. Second, a change-point control chart based on likelihood ratio was used to trigger potential tardy orders in the system. Third, a case study was carried out to prove that the change-point control chart could effectively signal mean shift in completion time of one order in one stage and could accurately estimate the change point and the out-of-control stage in terms of performance indices. In detail, when the mean shift was relatively small, the change-point control chart could effectively identify it and more accurately detect the change point and the out-of-control stage than the traditional Shewhart control chart did. We also investigated the effect of misspecified parameters of state space equations on performance of the change-point control chart. The results showed that the performance of the change-point control chart could still remain relatively stable. In general, the change-point control could effectively monitor the supply chain network system and the monitoring effect was relatively stable.