Entropy-Based Optimization of Decentralized Supply-Chain Networks

Abstract

Supply-chain operations are intricate and complex due to exogenous and endogenous uncertainties. The prime weakness in a decentralized supply chain arises due to lack of consideration of the uncertainty generation and transmission between supply-chain entities. Ineffective internal strategies carry the uncertainties beyond the boundaries of the supply-chain entity and consequently affect predictability of supply-chain behavior, customer satisfaction, and cost. Previous attempts made to enhance the supply-chain performance by optimizing the replenishment strategy do not pay attention to the issue of increasing uncertainty and consequent operational complexity in the system. The storage and flow of uncertainty must therefore be controlled and managed effectively. The minimization of complexity in supply chains using Shannon’s entropy-based measures by tweaking supply-chain decision parameters (replenishment parameters) is a unique aspect of this work. As a result, the predictability of the supply chain is improved. Four complexity management strategies (S-I to S-IV) are proposed and analyzed to understand their benefits under diverse business scenarios. Our investigations demonstrate that the strategy which aims to minimize an additive measure of information and material flow complexity outperforms other complexity management strategies under all business scenarios.