A blockchain-based approach for a multi-echelon sustainable supply chain. Issue 7 (2nd April 2020)

• Record Type: Journal Article
• Authors: Manupati, V. K.; Schoenherr, Tobias; Ramkumar, M.; Wagner, Stephan M.; Pabba, Sai Krishna; Inder Raj Singh, R.
• Is Part Of: International journal of production research. Volume 58:Issue 7(2020)
• Page Start: 2222
• Page End: 2241
• Publication Date: 2020-04-02
• Subjects: Blockchain -- production allocation problem -- multi-echelon supply chain -- smart contracts -- carbon taxation policy -- Mixed integer programming; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2019.1683248
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : Blockchain technology is destined to revolutionise supply chain processes. At the same time, governmental and regulatory policies are forcing firms to adjust their supply chains in response to environmental concerns. The objective of this study is therefore to develop a distributed ledger-based blockchain approach for monitoring supply chain performance and optimising both emission levels and operational costs in a synchronised fashion, producing a better outcome for the supply chain. We propose the blockchain approach for different production allocation problems within a multi-echelon supply chain (MESC) under a carbon taxation policy. As such, we couple recent advances in digitalisation of operations with increasingly stringent regulatory environmental policies. Specifically, with lead time considerations under emission rate constraints (imposed by a carbon taxation policy), we simultaneously consider the production, distribution and inventory control decisions in a production allocation-based MESC problem. The problem is then formulated as a Mixed Integer Non-Linear Programming (MINLP) model. We show that the distributed ledger-based blockchain approach minimises both total cost and carbon emissions. We then validate the feasibility of the proposed approach by comparing the results with a non-dominated sorting genetic algorithm (NSGA-II). The findings provide support for policymakers and supply chain executives alike.