[en] Supply chain operations have tended to become more complex, thus placing significant pressure on one of the most critical processes: supplier selection and order allocation (SSOA). This process involves a focal company selecting suppliers and allocating orders to obtain required materials. Achieving effective SSOA processes is challenged by (1) reliance on centralized governance and (2) ensuring effective contract management. While so called “smart contracts” could address these challenges, design knowledge about such technology — particularly in the SSOA context — is underexplored in the literature. In this paper we design a smart contract for SSOA in supply chains. We conducted a design science research study and developed three core artifacts: (1) a mathematical description of SSOA; (2) a system model of actor interactions; and (3) SSOA-relevant algorithms. Utilizing the Ethereum blockchain, we demonstrated and tested our smart contracts through scenario analysis. We found that our design is feasible and highly likely to address centralization and effectiveness challenges in SSOA. This paper contributes to the literature by demonstrating how smart contract design focusing on SSOA can further enhance blockchain-driven business models. In addition, we offer prescriptive knowledge on developing smart contracts for SSOA in supply chains.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations NCER-FT - FinTech National Centre of Excellence in Research
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
This research was funded in part by the Luxembourg National Research Fund (FNR), grant reference NCER22/IS/16570468/NCER-FT and the Ministry of Finance of Luxembourg through the FutureFinTech National Centre of Excellence in Research & Innovation . For the purpose of open access, and in fulfillment of the obligations arising from the grant agreement, the author has applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission.