AI Automation and Retailer Regret in Supply Chains

• 成果类型: Article
• 署名作者: Li, Meng; Li, Tao
• 署名单位: University of Houston System; University of Houston; Santa Clara University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13498
• 发表日期: 2022
• 页码: 83-97
• 关键词: managerial bias; human-machine reconcile; emotion; Industry 4.0; Artificial intelligence

摘要

Artificial intelligence (AI) has significantly changed the supply chain process. In this study, we study the effects associated with AI automation of the retailer's order decision in a decentralized supply chain comprising one supplier and one regretful retailer. In the absence of AI automation, the retailer has a regret bias in that it behaves as though considering the deviation between the realized demand and order quantity, when making an ex ante inventory decision. We find that if profit margins of the supply chain are high, regret bias drives the retailer to decline the supplier's contract, whereas, if profit margins are low, regret drives retailers to order more from the supplier. As a result, although the automation of retailer decision leads to a higher expected profit for a retailer that operates in a centralized vacuum, it nevertheless can be a negative force for a decentralized supply chain with either high or low profit margins. Perhaps more interestingly, as a retailer's decision becomes automatic, it is not destined to earn a higher expected profit. In the extreme, a lose-lose outcome can prevail in which automation potentially leaves both the retailer and supplier worse off.