Tonya Boone , Benham Fahimnia , Ram Ganeshan , David M. Herold , Nada R. Sanders
{"title":"Generative AI: Opportunities, challenges, and research directions for supply chain resilience","authors":"Tonya Boone , Benham Fahimnia , Ram Ganeshan , David M. Herold , Nada R. Sanders","doi":"10.1016/j.tre.2025.104135","DOIUrl":null,"url":null,"abstract":"<div><div>Generative Artificial Intelligence (GenAI) is emerging as a transformative force in supply chain resilience, offering new ways to enhance decision-making, automate operations, and improve adaptability to disruptions. Unlike traditional AI, which relies on historical data for prediction and optimization, GenAI can generate novel solutions and simulate alternative scenarios in real time. Despite its potential, research on GenAI’s role in supply chain resilience remains limited. This paper explores GenAI applications and possible research questions across key supply chain areas while also addressing challenges such as misinformation, security risks, and governance. As GenAI integrates with existing technologies, its adoption raises critical questions about accountability and systemic dependencies. To ensure responsible implementation, further research is needed to refine oversight mechanisms, establish benchmarks, and develop hybrid decision-making models where AI enhances, rather than replaces, human expertise. These insights provide guidance to managers and policymakers to help make informed decisions about the strategic deployment of GenAI in resilience-oriented supply chains.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"199 ","pages":"Article 104135"},"PeriodicalIF":8.3000,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part E-Logistics and Transportation Review","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1366554525001760","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Generative Artificial Intelligence (GenAI) is emerging as a transformative force in supply chain resilience, offering new ways to enhance decision-making, automate operations, and improve adaptability to disruptions. Unlike traditional AI, which relies on historical data for prediction and optimization, GenAI can generate novel solutions and simulate alternative scenarios in real time. Despite its potential, research on GenAI’s role in supply chain resilience remains limited. This paper explores GenAI applications and possible research questions across key supply chain areas while also addressing challenges such as misinformation, security risks, and governance. As GenAI integrates with existing technologies, its adoption raises critical questions about accountability and systemic dependencies. To ensure responsible implementation, further research is needed to refine oversight mechanisms, establish benchmarks, and develop hybrid decision-making models where AI enhances, rather than replaces, human expertise. These insights provide guidance to managers and policymakers to help make informed decisions about the strategic deployment of GenAI in resilience-oriented supply chains.
期刊介绍:
Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management.
Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.