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

Document Type : Article


1 College of Management & College of Tourism, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.

2 School of Management, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

3 Department of Management Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.

4 Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, T2N 1N4, Canada.


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.


Main Subjects

1. Xu, X., Thuong, T. X., Kim, H. S. and You, S. S. “Optimising supply chain management using robust control synthesis”, International Journal of Logistics Economics & Globalisation, 7(3), pp.277-291 (2018).
2. Gitinavard, H., Shirazi, M. and Ghodsypour, S. “A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty”, Scientia Iranica, DOI: 10.24200/SCI.2018.5047.1060 (2018).
3. Zhong, J., Ma, Y. and Tu, P. “Supply chain quality management: An empirical study”, International Journal of Contemporary Hospitality Management, 28(11), pp.2446-2472(2016).
4. Heydari, J.“Lead time variation control using reliable shipment equipment: An incentive scheme for supply chain coordination.”Transportation Research Part E: Logistics and Transportation Review, 63(3), pp.44-58(2014).
5. Montgomery, D. C.“A discussion on statistically-based process monitoring and control”. Journal of Quality Technology , 29(2), pp.157-162(1997).
6. Woodall, W. H., Tsui, K. L. and Tucker, G. R. A review of statistical and fuzzy quality control charts based on categorical data, Frontiers in Statistical Quality Control, Physica-Verlag HD, 5, pp.83-89(2012).
7. Wang, F. S. Study on supply chain decision and monitoring based on response time, Doctoral dissertation of Huazhong University of Science and Technology (2006).
8. Chen, K. Shaw, Y. and Yi C. “Applying back propagation network to cold chain temperature monitoring”, Advanced Engineering Informatics, 25(1), pp.11-22(2012).
9. Wang, S. S., Teresa, W., Shaojen, W. and John, F. “A control chart based approach to monitoring supply network dynamics using Kalman filtering”, International Journal of Production Research,50(11), pp.3137-3151(2012). 
10. Faraz, A., Heuchenne, C., Saniga, E. and Foster E. “Monitoring delivery chains using multivariate control charts” , European Journal of Operational Research, 228(1), pp. 282-289 (2013). 
11. Lu, S. L. and Tsai, C. F.“A nonparametric GA-GWMA sign chart for green SCM optimization”,  IEEE International Conference on e-Business Engineering, pp.504-508(2013).
12. Zhong, J., Ma, Y. and Tu, P. “Integration of SPC and performance maintenance for supply chain system”, International Journal of Production Research, 54(19), pp.5932-5945(2016).
13. Tsung, F., Li, Y. T. and Jin, M.“Statistical process control for multistage manufacturing and service operations: a review and some extensions”, International Journal of Services Operations and Informatics, 3(2), pp.191-204(2008).
14. Zolfaghari, S. and Amiri, A. Monitoring multivariate-attribute quality characteristics in two stage processes using discriminant analysis based control charts, Scientia Iranica, Transactions E: Industrial Engineering, 23(2), pp.757-767 (2016).
15. Amiri, A. and Zolfaghari, S. “Estimation of change point for multistage processes subject to step change and linear trend”, International Journal of Reliability, Quality and Safety Engineering, 23(2), pp.1650007(1-18) (2016).
16. Assareh, H., Noorossana, R., Mohammadi, M. and Mengersen K. “Bayesian multiple change-point estimation of Poisson rates in control charts”, Scientia Iranica, 23(1), pp.316-329(2016).
17. Shang, Y. F., Tsung, F. and Zou, C. L.“Statistical process control for multistage process with binary outputs”, IIE Transactions ,45(9), pp.1008-1023(2013).
18. Pirhooshyaran, M. and Niaki, S. T. A. A double-max MEWMA scheme for simultaneous monitoring and fault isolation of multivariate multistage auto-correlated processes based on novel reduced-dimension statistics. Journal of Process Control, 29, pp.11-22 (2015).
19. Bazdar, A., Reza. B. K. and Niaki, S. T. A. Fault diagnosis within multistage machining processes using linear discriminant analysis: a case study in automotive industry. Quality Technology & Quantitative Management, 14(2), pp.129-141 (2017).
20. Zou, N. and Li, J.“Modeling and change detection of dynamic network data by a network state space model”,IISE Transactions,49(1), pp.45-57(2017).
21. Jin, M., Li, Y. and Tsung, F.“Chart allocation strategy for serial-parallel multistage manufacturing process”,  IIE Transactions, 42(8), pp.577-588(2010).
22. Sullivan, J. H. and Woodall, W. H.“A control chart for preliminary analysis of individual observations”, Journal of Quality Technology , 28(3), pp.265-278(1996).
23. Zou, C. L., Tsung, F. and Liu, Y.“A change point approach for phase I analysis in multistage process”, Technometrics, 50(3), pp.344–356(2008).