Supply Chain Monitoring Using Principal Component Analysis
Jing Wang, Christopher Swartz, Brandon Corbett, Kai Huang
July 16, 2020
Various types of risks exist in a supply chain, and disruptions could lead to economic loss or even breakdown of a supply chain without an effective mitigation strategy. The ability to detect disruptions early can help improve the resilience of the supply chain. In this paper, the application of principal component analysis (PCA) and dynamic PCA (DPCA) in fault detection and diagnosis of a supply chain system is investigated. In order to monitor the supply chain, data such as inventory levels, market demands and amount of products in transit are collected. PCA and DPCA are used to model the normal operating conditions (NOC). Two monitoring statistics, the Hotelling's T-squared and the squared prediction error (SPE), are used to detect abnormal operation of the supply chain. The confidence limits of these two statistics are estimated from the training data based on the $\chi^2$- distributions. The contribution plots are used to identify the variables with abnormal behavior when at least one statistic exceeds its limit. Two case studies are presented - a multi-echelon supply chain for single product that includes a manufacturing process, and a gas bottling supply chain with multiple products. In order to validate the proposed method, supply chain simulation models are developed using the programming language Python 3.7, and simulated data is collected for analysis. PCA and DPCA are applied to the data using the scikit-learn machine learning library for Python. The results show that abnormal operation due to transportation delay, supply shortage, and poor manufacturing yield can be detected. The contribution plots are useful for interpreting and identifying the abnormality.
Suggested Citation
Wang J, Swartz C, Corbett B, Huang K. Supply Chain Monitoring Using Principal Component Analysis. (2020). LAPSE:2020.0822
Author Affiliations
Wang J: McMaster University
Swartz C: McMaster University
Corbett B: ProSensus
Huang K: McMaster University
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Industrial and Engineering Chemistry Research
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