Application of blockchain and smart contracts in autonomous vehicle supply chains: An experimental design

• Published in: Transportation research. Part E, Logistics and transportation review Vol. 165; p. 102864
• Main Authors: Arunmozhi, Manimuthu, Venkatesh, V.G., Arisian, Sobhan, Shi, Yangyan, Raja Sreedharan, V.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-09-2022; Elsevier
• Subjects: Artificial intelligence; Autonomous vehicle; Blockchain; Business administration; Digital supply chain; Humanities and Social Sciences; Smart contract; Sustainable logistics; Sustainable logistics; Smart contract; Autonomous vehicle; Artificial intelligence; Digital supply chain; Blockchain; algorithm,artificial intelligence,Artificial intelligence,Autonomous vehicle,Blockchain,Digital supply chain,economic growth,experimental design,machine learning,Smart contract,supply chain management,sustainable development,Sustainable logistics,technology adoption
• ISSN: 1366-5545; 1878-5794
• DOI: 10.1016/j.tre.2022.102864

•An innovative AV design using Artificial Intelligence and Blockchain-based smart contract is proposed.•A novel design element, Margin Indicator, is incorporated into the proposed AV design.•The proposed approach supports a robust control of costs and energy by reducing energy wastage and hidden financial transactions.•The proposed framework offers an improved product traceability, transaction transparency, and sustainable economic growth for the AV supply chains.
With the rise of digital sustainable business models in the Autonomous Vehicles (AV) industry, the traditional automakers are undergoing a major restructuring in their key performance areas and associated supply chains processes. Focusing on an innovative AV design (AD) concept, this paper investigates how Artificial Intelligence (AI) and Blockchain-based Smart Contracts can enhance sustainable supply chain operations. A novel design element, Margin Indicator (MI), is developed to obtain reliable predictive analytics results from the mainstream machine learning algorithms. The proposed approach supports a robust control of costs and energy, while maintaining a high level of transparency in managing decentralized AV supply chain processes, monetary impacts, and environmental sustainability. Testing the developed concept through a preliminary case study, we observed a reduction in energy wastage and hidden financial transactions by 12.48% and 11.58%, respectively. Supported by the rapid advancement in the blockchain and AI technologies, the developed framework is expected to improve product traceability, transaction transparency, and sustainable economic growth for the AV supply chains.