Sharif University of Technology Scientia Iranica 1026-3098 27 5 2020 10 01 Applying a change-point control chart based on likelihood ratio to supply chain network monitoring 2529 2538 21505 10.24200/sci.2019.5626.1380 EN J. Zhong College of Management & College of Tourism, Fujian Agriculture and Forestry University, Fuzhou, 350002, China. X. Hu School of Management, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China Y. Yang Department of Management Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China. Y. Tu Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, T2N 1N4, Canada. Journal Article 2017 11 21 In this paper, a supply chain network system is viewed 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 complexity of the supply chain network system. Then, a change point control chart based on likelihood ratio is built to trigger potential tardy orders in the system. A case study is illustrated to indicate that the change point control charts can effectively signal process mean shift, and accurately estimate the change point and the out-of-control stage in term of power of detection and the accuracy of estimation of change point. We also investigate the effect of misspecified parameters of state space equations on the performance of the change point control chart. The results show that the performance of the change point control chart is relatively stable. https://scientiairanica.sharif.edu/article_21505_befe94657dec0f42bcaf6dc88ff1b82d.pdf