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

Abstract

A supply chain network system is regarded as a serial-parallel multistage process; and the application of a change-point control chart based on likelihood ratio is explored to monitor this system. Firstly, state-space modeling is used to characterize complexities of the supply chain network system. Secondly, a change-point control chart based on likelihood ratio is used to trigger potential tardy orders in the system. Thirdly, a case study is illustrated to indicate that the change-point control chart can effectively signal mean shift in completion time of one order in one stage, and can accurately estimate the change point and the out-of-control stage in term of the performance indexes. In detail, when the mean shift is relatively small, the change-point control chart can effectively identify it, and more accurately detect the change point and the out-of-control stage comparing with the traditional Shewhart control chart. We also investigate the effect of misspecified parameters of state space equations on performance of the change-point control chart. The results show that the performance of the change-point control chart can still maintain relatively stable. In general, the change-point control can effectively monitor the supply chain network system, and the monitoring effect is relatively stable.