Applying MVA to Supply Chain Data

By |2020-07-15T16:02:18-04:00July 15, 2020||

Supply chain

ProSensus founder Dr. John MacGregor has long anticipated significant advantages in monitoring and analyzing several aspects of supply chain data through the application of Multivariate Analysis (MVA).

To this end and in discussion with ProSensus, the McMaster [University] Advanced Control Consortium (MACC) recently investigated the potential of applying proven MVA methodology to the monitoring and diagnosis of supply chain data.

As a result of this promising effort, a paper titled “Supply Chain Monitoring Using Principal Component Analysis” was published on June 24, 2020 in Industrial & Engineering Chemistry Research (I&EC).

Please join Dr. John MacGregor and Dr. Chris Swartz (MACC) for a webinar

The 1-hour webinar will be held on Tuesday August 11th, 2020 at 1pm and will include the following discussions:

  • The characteristics of supply chain datasets that are ideally suited to such analysis
  • The approach and results of applying MVA to monitoring and diagnosing supply chain data (from the I&EC paper)
  • Additional application ideas (such as the use of MVA models for the optimal rebalancing of supply chains)
  • Input and comments from attendees
  • How to get involved with ProSensus and McMaster and your industrial supply chain data

Registration has now closed. If you are interested in participating in this research, please contact us.

Read the Paper

“Supply Chain Monitoring Using Principal Component Analysis”, Jing Wang, Christopher L. E. Swartz*, Brandon Corbett, and Kai Huang, Ind. Eng. Chem. Res. 2020, 59, 27, 12487–12503.  Publication Date: June 24, 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 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 1 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.

Full Paper (pre-publication)

Read the pre-publicaton version of the full paper here.

About the Author: Kristin Wallace

Kristin Wallace, MASc
Project / Sales Engineer
Kristin has a Bachelor’s Degree in Chemical Engineering and a Master’s Degree in Applied Science (optimization focus) from McMaster University. She has worked on projects in rapid product development and troubleshooting and plays a key role in writing technical proposals. Prior to working at ProSensus, she spent 5 years working at Hatch designing and troubleshooting non-ferrous electric arc furnaces.