Enhancing Trust of Supply Chain Using Blockchain Platform with Robust Data Model and Verification Mechanisms

Abstract

As supply chain has become extremely complicated, traditional supply chain management schemes reveal limitations in keeping track of resources for efficient risk management. Recently, blockchain-based solutions for supply chain have shown possibility to overcome those limits. However, numerous practical problems in this research domain are not fully explored up to date. Thus, we delve into one of the most significant problems among them: verification of business logic in transactions. In this paper, we propose a novel distributed ledger system for supply chain, enabled by anomaly detection framework that verifies semantic correctness of transactions based on business context data. Our blockchain data model is tailored to accurately represent events occurred in supply chain based on real-world business standards. In order to facilitate more efficient tracking of provenance, we leverage graph data model to represent supply chain network. On top of the data model, we present smart contract-based anomaly detection framework that verifies whether a transaction is anomalous. Generic rule-based and graph-based detection methods are devised. The feasibility of our proposed model is shown by the system implemented using Hyperledger Sawtooth. We show how our anomaly detection layer can be plugged into the system: how it interacts with other system components, and how overall system flow works with this new function. We evaluate our system with scenario-based simulations. For a number of use cases we synthesized, the correctness and effectiveness of our system are demonstrated.