Data-driven supply chain monitoring using canonical variate analysis

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.