Supply Chain Monitoring Using Principal Component AnalysisClick to copy article linkArticle link copied!
- Jing WangJing WangSchool of Computational Science and Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1More by Jing Wang
- Christopher L. E. Swartz*Christopher L. E. Swartz*Email: [email protected]Department of Chemical Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4L7More by Christopher L. E. Swartz
- Brandon CorbettBrandon CorbettProSensus Inc., 4325 Harvester Road, Unit 12, Burlington, Ontario, Canada, L7L 5M4More by Brandon Corbett
- Kai HuangKai HuangDeGroote School of Business, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4M4More by Kai Huang
Abstract
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 T2 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 χ2-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 a 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.
Cited By
Smart citations by scite.ai include citation statements extracted from the full text of the citing article. The number of the statements may be higher than the number of citations provided by ACS Publications if one paper cites another multiple times or lower if scite has not yet processed some of the citing articles.
This article is cited by 13 publications.
- Aldina Correia, Diogo Ribeiro. Longitudinal Features Extraction in International Logistics Performance Index. WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 2025, 22 , 426-439. https://doi.org/10.37394/23207.2025.22.40
- Jing Wang, Christopher L.E. Swartz, Kai Huang. Risk-averse supply chain management via robust reinforcement learning. Computers & Chemical Engineering 2025, 192 , 108912. https://doi.org/10.1016/j.compchemeng.2024.108912
- Ruxanda Godina Silva, Carina Pimentel, Reinaldo Gomes, Ana Luísa Ramos, João C. O. Matias. Sustainable Harvest/Collection Optimization of Residual Agro-Forestry Biomass including Wildfire Risk. Procedia Computer Science 2025, 253 , 3037-3048. https://doi.org/10.1016/j.procs.2025.02.028
- Ehsan Badakhshan, Navonil Mustafee, Ramin Bahadori. Application of simulation and machine learning in supply chain management: A synthesis of the literature using the Sim-ML literature classification framework. Computers & Industrial Engineering 2024, 198 , 110649. https://doi.org/10.1016/j.cie.2024.110649
- Lambodar Saha, Sphurti Birajdar, Dipesh Uike, M. Dhiliphan Kumar, G. Sivakumar, Ullal Akshatha Nayak. LSSVM Analysis of Competency Effects on Supply Chain Management from Individual and Collaborative Perspectives. 2024, 1-6. https://doi.org/10.1109/ICDSNS62112.2024.10691117
- Xingyu Li, Vasiliy Krivtsov, Chaoye Pan, Aydin Nassehi, Robert X. Gao, Dmitry Ivanov. End-to-end supply chain resilience management using deep learning, survival analysis, and explainable artificial intelligence. International Journal of Production Research 2024, 333 , 1-29. https://doi.org/10.1080/00207543.2024.2367685
- Pranesh Saisridhar, Matthias Thürer, Balram Avittathur. Assessing supply chain responsiveness, resilience and robustness (Triple-R) by computer simulation: a systematic review of the literature. International Journal of Production Research 2024, 62
(4)
, 1458-1488. https://doi.org/10.1080/00207543.2023.2180302
- Snežana Tadić, Mladen Krstić, Miloš Veljović, Milovan Kovač. Human and artificial intelligence in the function of logistics. 2024, 55-68. https://doi.org/10.5937/VI24055T
- Jing Wang, Christopher L.E. Swartz, Kai Huang. Data-driven supply chain monitoring using canonical variate analysis. Computers & Chemical Engineering 2023, 174 , 108228. https://doi.org/10.1016/j.compchemeng.2023.108228
- Mei Yang, Ming K. Lim, Yingchi Qu, Du Ni, Zhi Xiao. Supply chain risk management with machine learning technology: A literature review and future research directions. Computers & Industrial Engineering 2023, 175 , 108859. https://doi.org/10.1016/j.cie.2022.108859
- Marco S. Reis, Ricardo Rendall, Tiago J. Rato, Cristina Martins, Pedro Delgado. Improving the sensitivity of statistical process monitoring of manifolds embedded in high-dimensional spaces: The truncated-Q statistic. Chemometrics and Intelligent Laboratory Systems 2021, 215 , 104369. https://doi.org/10.1016/j.chemolab.2021.104369
- Ming-Chi Tsai, . Developing a sustainability strategy for Taiwan’s tourism industry after the COVID-19 pandemic. PLOS ONE 2021, 16
(3)
, e0248319. https://doi.org/10.1371/journal.pone.0248319
- Zijun Dang, Shunshun Liu, Tong Li, Liang Gao, . [Retracted] Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm. Computational Intelligence and Neuroscience 2021, 2021
(1)
https://doi.org/10.1155/2021/3715116
Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.
Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.
The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.