Data-driven supply chain monitoring using canonical variate analysis. (June 2023)

• Record Type: Journal Article
• Authors: Wang, Jing; Swartz, Christopher L.E.; Huang, Kai
• Is Part Of: Computers & chemical engineering. Volume 174(2023)
• Publication Date: 2023-06
• Subjects: Supply chain monitoring -- Data-driven method -- Canonical variate analysis -- Principal component analysis -- Fault detection; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2023.108228
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract: The monitoring of supply chain operations and the ability to detect abnormal operation in a timely manner is important to the functioning and economics of a supply chain system. This paper presents a data-driven supply chain monitoring method based on canonical variate analysis (CVA). A sparse CVA algorithm is used to address singular covariance matrices. In addition, a fault impact prediction method that utilizes the time-dependent relationships inherent in the CVA model is proposed. The proposed monitoring scheme is validated on two case studies — the classical beer distribution game, and a more complicated supply chain that has material flow in forward and reverse directions. The performance of CVA in fault detection is examined and compared against dynamic principal component analysis (DPCA) under cross-correlated and autocorrelated demand. Results show that CVA is effective in detecting abnormal supply chain operations, and achieves comparable performance to DPCA in a lower-dimensional latent space. Highlights: Application of canonical variate analysis (CVA) to supply chain monitoring (SCMo). A Q-statistic based on the right Moore–Penrose inverse for sparse CVA. An approach to hyperparameter tuning for sparse CVA. Comparative study of CVA and principal component analysis (PCA) in SCMo. Novel technique proposed for fault impact prediction based on CVA model relationships.