{"title":"供应链管理中的人工智能应用：系统性文献综述","authors":"Gulnaz Shahzadi, Fu Jia, Lujie Chen, Albert John","doi":"10.1108/jmtm-09-2023-0431","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>This systematic literature review (SLR) aims to critically analyze the current academic research on the adoption of artificial intelligence (AI) in supply chain management (SCM) and develop a theoretical framework and future research agenda.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>Through a comprehensive review of 68 relevant papers, this study synthesizes the findings to identify key themes based on extended technology-organization-environment (TOE) theory.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>This study analyzes AI integration in SCM based on the TOE framework, identifying drivers (technological, organizational, environmental and human), barriers (technical, organizational, economic and human) and outcomes (operational, environmental, social and economic) of AI adoption. It emphasizes AI's potential in improving SCM practices like resilience, process improvement and sustainable operations, contributing to better decision-making, efficiency and sustainable practices. The study also provided a novel framework that offers insights for strategic AI integration in SCM, aiding policymakers and managers in understanding and leveraging AI's multifaceted impact.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>The originality of the study lies in the development of a theoretical framework that not only elucidates the drivers and barriers of AI in SCM but also maps the operational, financial, environmental and social outcomes of AI-enabled practices. This framework serves as a novel tool for policymakers and managers, offering specific, actionable insights for the strategic integration of AI in supply chains (SCs). Furthermore, the study's value is underscored by its potential to guide policy formulation and managerial decision-making, with a focus on optimizing SC efficiency, sustainability and resilience through AI adoption.</p><!--/ Abstract__block -->","PeriodicalId":16301,"journal":{"name":"Journal of Manufacturing Technology Management","volume":null,"pages":null},"PeriodicalIF":7.3000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI adoption in supply chain management: a systematic literature review\",\"authors\":\"Gulnaz Shahzadi, Fu Jia, Lujie Chen, Albert John\",\"doi\":\"10.1108/jmtm-09-2023-0431\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Purpose</h3>\\n<p>This systematic literature review (SLR) aims to critically analyze the current academic research on the adoption of artificial intelligence (AI) in supply chain management (SCM) and develop a theoretical framework and future research agenda.</p><!--/ Abstract__block -->\\n<h3>Design/methodology/approach</h3>\\n<p>Through a comprehensive review of 68 relevant papers, this study synthesizes the findings to identify key themes based on extended technology-organization-environment (TOE) theory.</p><!--/ Abstract__block -->\\n<h3>Findings</h3>\\n<p>This study analyzes AI integration in SCM based on the TOE framework, identifying drivers (technological, organizational, environmental and human), barriers (technical, organizational, economic and human) and outcomes (operational, environmental, social and economic) of AI adoption. It emphasizes AI's potential in improving SCM practices like resilience, process improvement and sustainable operations, contributing to better decision-making, efficiency and sustainable practices. The study also provided a novel framework that offers insights for strategic AI integration in SCM, aiding policymakers and managers in understanding and leveraging AI's multifaceted impact.</p><!--/ Abstract__block -->\\n<h3>Originality/value</h3>\\n<p>The originality of the study lies in the development of a theoretical framework that not only elucidates the drivers and barriers of AI in SCM but also maps the operational, financial, environmental and social outcomes of AI-enabled practices. This framework serves as a novel tool for policymakers and managers, offering specific, actionable insights for the strategic integration of AI in supply chains (SCs). Furthermore, the study's value is underscored by its potential to guide policy formulation and managerial decision-making, with a focus on optimizing SC efficiency, sustainability and resilience through AI adoption.</p><!--/ Abstract__block -->\",\"PeriodicalId\":16301,\"journal\":{\"name\":\"Journal of Manufacturing Technology Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2024-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Technology Management\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1108/jmtm-09-2023-0431\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Technology Management","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1108/jmtm-09-2023-0431","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
摘要
目的本系统性文献综述（SLR）旨在批判性地分析当前关于在供应链管理（SCM）中采用人工智能（AI）的学术研究，并制定理论框架和未来研究议程。研究结果本研究基于 TOE 框架分析了供应链管理中的人工智能整合，确定了采用人工智能的驱动因素（技术、组织、环境和人力）、障碍（技术、组织、经济和人力）和结果（运营、环境、社会和经济）。它强调了人工智能在改进供应链管理实践（如复原力、流程改进和可持续运营）方面的潜力，有助于改善决策、提高效率和可持续实践。该研究还提供了一个新颖的框架，为将人工智能战略性地融入供应链管理提供了见解，有助于政策制定者和管理者了解和利用人工智能的多方面影响。 原创性/价值 该研究的原创性在于开发了一个理论框架，不仅阐明了人工智能在供应链管理中的驱动因素和障碍，还描绘了人工智能赋能实践的运营、财务、环境和社会成果。这一框架为决策者和管理者提供了一个新颖的工具，为供应链（SC）中人工智能的战略整合提供了具体可行的见解。此外，这项研究的价值还体现在它具有指导政策制定和管理决策的潜力，重点是通过采用人工智能优化供应链的效率、可持续性和复原力。
AI adoption in supply chain management: a systematic literature review
Purpose
This systematic literature review (SLR) aims to critically analyze the current academic research on the adoption of artificial intelligence (AI) in supply chain management (SCM) and develop a theoretical framework and future research agenda.
Design/methodology/approach
Through a comprehensive review of 68 relevant papers, this study synthesizes the findings to identify key themes based on extended technology-organization-environment (TOE) theory.
Findings
This study analyzes AI integration in SCM based on the TOE framework, identifying drivers (technological, organizational, environmental and human), barriers (technical, organizational, economic and human) and outcomes (operational, environmental, social and economic) of AI adoption. It emphasizes AI's potential in improving SCM practices like resilience, process improvement and sustainable operations, contributing to better decision-making, efficiency and sustainable practices. The study also provided a novel framework that offers insights for strategic AI integration in SCM, aiding policymakers and managers in understanding and leveraging AI's multifaceted impact.
Originality/value
The originality of the study lies in the development of a theoretical framework that not only elucidates the drivers and barriers of AI in SCM but also maps the operational, financial, environmental and social outcomes of AI-enabled practices. This framework serves as a novel tool for policymakers and managers, offering specific, actionable insights for the strategic integration of AI in supply chains (SCs). Furthermore, the study's value is underscored by its potential to guide policy formulation and managerial decision-making, with a focus on optimizing SC efficiency, sustainability and resilience through AI adoption.
期刊介绍：
The Journal of Manufacturing Technology Management (JMTM) aspires to be the premier destination for impactful manufacturing-related research. JMTM provides comprehensive international coverage of topics pertaining to the management of manufacturing technology, focusing on bridging theoretical advancements with practical applications to enhance manufacturing practices.
JMTM seeks articles grounded in empirical evidence, such as surveys, case studies, and action research, to ensure relevance and applicability. All submissions should include a thorough literature review to contextualize the study within the field and clearly demonstrate how the research contributes significantly and originally by comparing and contrasting its findings with existing knowledge. Articles should directly address management of manufacturing technology and offer insights with broad applicability.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
