Stochastic batch dispersion model to optimize traceability and enhance transparency using Blockchain

Abstract

The absence of food traceability has led to severe problems like a product recall, consumer dissatisfaction, and contamination insecurities in the past. We consider a five-level supply chain for sausage where at each level, the output product is manufactured by combining/mixing correct proportions of the raw materials from the previous stages. The demand for the final product is uncertain. Using stochastic models, we improve the supply chain's traceability and optimize dispersion among the sausage material batches. We also derive theoretical results in terms of proving a relatively complete recourse structure in the proposed model. Furthermore, to provide insights regarding the supply chain network's transparency, we integrate the Blockchain framework in our model data storage. Using a simple case study, where an attacker tries to alter or delete data inside the Blockchain, we quantitatively measure this immutability of this decentralized data. The case study reveals that decentralized databases could make data accessible to peers (retailers, suppliers, manufacturers) while mitigating data tampering by Blockchain technology. This ensures transparency among the peers and the privacy and security of the data present in the decentralized database of different supply chain networks.