Improving supply chain security using big data

Abstract

Previous attempts at supply chain risk management are often non-technical and rely heavily on policies/procedures to provide security assurances. This is particularity worrisome as there are vast volumes of data that must be analyzed and data continues to grow at unprecedented rates. In order to mitigate these issues and minimize the amount of manual inspection required, we propose the development of mathematically-based automated screening methods that can be incorporated into supply chain risk management. In particular, we look at methods for identifying deception and deceptive practices that may be present in the supply chain. We examine two classes of constraints faced by deceivers, cognitive/computational limitations and strategic tradeoffs, which can be used to developed graph-based metrics to represent entity behavior. By using these metrics with novel machine learning algorithms, we can robustly detect deceptive behavior and identify potential supply chain issues.

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