人工智能技术及其对2019冠状病毒病期间供应链弹性的影响

IF 5.9 3区 管理学 Q1 MANAGEMENT
S. Modgil, Shivam Gupta, Rébecca Stekelorum, Issam Laguir
{"title":"人工智能技术及其对2019冠状病毒病期间供应链弹性的影响","authors":"S. Modgil, Shivam Gupta, Rébecca Stekelorum, Issam Laguir","doi":"10.1108/ijpdlm-12-2020-0434","DOIUrl":null,"url":null,"abstract":"PurposeCOVID-19 has pushed many supply chains to re-think and strengthen their resilience and how it can help organisations survive in difficult times. Considering the availability of data and the huge number of supply chains that had their weak links exposed during COVID-19, the objective of the study is to employ artificial intelligence to develop supply chain resilience to withstand extreme disruptions such as COVID-19.Design/methodology/approachWe adopted a qualitative approach for interviewing respondents using a semi-structured interview schedule through the lens of organisational information processing theory. A total of 31 respondents from the supply chain and information systems field shared their views on employing artificial intelligence (AI) for supply chain resilience during COVID-19. We used a process of open, axial and selective coding to extract interrelated themes and proposals that resulted in the establishment of our framework.FindingsAn AI-facilitated supply chain helps systematically develop resilience in its structure and network. Resilient supply chains in dynamic settings and during extreme disruption scenarios are capable of recognising (sensing risks, degree of localisation, failure modes and data trends), analysing (what-if scenarios, realistic customer demand, stress test simulation and constraints), reconfiguring (automation, re-alignment of a network, tracking effort, physical security threats and control) and activating (establishing operating rules, contingency management, managing demand volatility and mitigating supply chain shock) operations quickly.Research limitations/implicationsAs the present research was conducted through semi-structured qualitative interviews to understand the role of AI in supply chain resilience during COVID-19, the respondents may have an inclination towards a specific role of AI due to their limited exposure.Practical implicationsSupply chain managers can utilise data to embed the required degree of resilience in their supply chains by considering the proposed framework elements and phases.Originality/valueThe present research contributes a framework that presents a four-phased, structured and systematic platform considering the required information processing capabilities to recognise, analyse, reconfigure and activate phases to ensure supply chain resilience.","PeriodicalId":14251,"journal":{"name":"International Journal of Physical Distribution & Logistics Management","volume":" ","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"61","resultStr":"{\"title\":\"AI technologies and their impact on supply chain resilience during COVID-19\",\"authors\":\"S. Modgil, Shivam Gupta, Rébecca Stekelorum, Issam Laguir\",\"doi\":\"10.1108/ijpdlm-12-2020-0434\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PurposeCOVID-19 has pushed many supply chains to re-think and strengthen their resilience and how it can help organisations survive in difficult times. Considering the availability of data and the huge number of supply chains that had their weak links exposed during COVID-19, the objective of the study is to employ artificial intelligence to develop supply chain resilience to withstand extreme disruptions such as COVID-19.Design/methodology/approachWe adopted a qualitative approach for interviewing respondents using a semi-structured interview schedule through the lens of organisational information processing theory. A total of 31 respondents from the supply chain and information systems field shared their views on employing artificial intelligence (AI) for supply chain resilience during COVID-19. We used a process of open, axial and selective coding to extract interrelated themes and proposals that resulted in the establishment of our framework.FindingsAn AI-facilitated supply chain helps systematically develop resilience in its structure and network. Resilient supply chains in dynamic settings and during extreme disruption scenarios are capable of recognising (sensing risks, degree of localisation, failure modes and data trends), analysing (what-if scenarios, realistic customer demand, stress test simulation and constraints), reconfiguring (automation, re-alignment of a network, tracking effort, physical security threats and control) and activating (establishing operating rules, contingency management, managing demand volatility and mitigating supply chain shock) operations quickly.Research limitations/implicationsAs the present research was conducted through semi-structured qualitative interviews to understand the role of AI in supply chain resilience during COVID-19, the respondents may have an inclination towards a specific role of AI due to their limited exposure.Practical implicationsSupply chain managers can utilise data to embed the required degree of resilience in their supply chains by considering the proposed framework elements and phases.Originality/valueThe present research contributes a framework that presents a four-phased, structured and systematic platform considering the required information processing capabilities to recognise, analyse, reconfigure and activate phases to ensure supply chain resilience.\",\"PeriodicalId\":14251,\"journal\":{\"name\":\"International Journal of Physical Distribution & Logistics Management\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2021-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"61\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Physical Distribution & Logistics Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1108/ijpdlm-12-2020-0434\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Physical Distribution & Logistics Management","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1108/ijpdlm-12-2020-0434","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 61

摘要

2019冠状病毒病促使许多供应链重新思考并加强其抵御能力,以及如何帮助组织在困难时期生存下来。考虑到数据的可用性以及大量供应链在COVID-19期间暴露出薄弱环节,该研究的目的是利用人工智能来提高供应链的弹性,以抵御COVID-19等极端中断。设计/方法/方法我们采用定性的方法,通过组织信息处理理论的视角,使用半结构化的访谈时间表来采访受访者。来自供应链和信息系统领域的31名受访者分享了他们对在2019冠状病毒病期间使用人工智能(AI)增强供应链弹性的看法。我们使用开放、轴向和选择性编码的过程来提取相互关联的主题和建议,从而建立我们的框架。发现人工智能促进的供应链有助于系统地发展其结构和网络的弹性。动态环境和极端中断情景下的弹性供应链能够识别(感知风险、本地化程度、故障模式和数据趋势)、分析(假设情景、现实客户需求、压力测试模拟和约束)、重新配置(自动化、网络重新对齐、跟踪工作、物理安全威胁和控制)和激活(建立操作规则、应急管理、快速管理需求波动和减轻供应链冲击)操作。研究局限性/意义由于本研究是通过半结构化定性访谈进行的,以了解人工智能在COVID-19期间供应链弹性中的作用,由于受访者的接触有限,他们可能倾向于人工智能的特定作用。实际意义供应链管理者可以利用数据,通过考虑所建议的框架元素和阶段,在他们的供应链中嵌入所需的弹性程度。原创性/价值本研究提供了一个框架,该框架提出了一个四阶段,结构化和系统化的平台,考虑到识别,分析,重新配置和激活阶段所需的信息处理能力,以确保供应链的弹性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI technologies and their impact on supply chain resilience during COVID-19
PurposeCOVID-19 has pushed many supply chains to re-think and strengthen their resilience and how it can help organisations survive in difficult times. Considering the availability of data and the huge number of supply chains that had their weak links exposed during COVID-19, the objective of the study is to employ artificial intelligence to develop supply chain resilience to withstand extreme disruptions such as COVID-19.Design/methodology/approachWe adopted a qualitative approach for interviewing respondents using a semi-structured interview schedule through the lens of organisational information processing theory. A total of 31 respondents from the supply chain and information systems field shared their views on employing artificial intelligence (AI) for supply chain resilience during COVID-19. We used a process of open, axial and selective coding to extract interrelated themes and proposals that resulted in the establishment of our framework.FindingsAn AI-facilitated supply chain helps systematically develop resilience in its structure and network. Resilient supply chains in dynamic settings and during extreme disruption scenarios are capable of recognising (sensing risks, degree of localisation, failure modes and data trends), analysing (what-if scenarios, realistic customer demand, stress test simulation and constraints), reconfiguring (automation, re-alignment of a network, tracking effort, physical security threats and control) and activating (establishing operating rules, contingency management, managing demand volatility and mitigating supply chain shock) operations quickly.Research limitations/implicationsAs the present research was conducted through semi-structured qualitative interviews to understand the role of AI in supply chain resilience during COVID-19, the respondents may have an inclination towards a specific role of AI due to their limited exposure.Practical implicationsSupply chain managers can utilise data to embed the required degree of resilience in their supply chains by considering the proposed framework elements and phases.Originality/valueThe present research contributes a framework that presents a four-phased, structured and systematic platform considering the required information processing capabilities to recognise, analyse, reconfigure and activate phases to ensure supply chain resilience.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
11.20
自引率
10.40%
发文量
34
期刊介绍: IJPDLM seeks strategically focused, theoretically grounded, empirical and conceptual, quantitative and qualitative, rigorous and relevant, original research studies in logistics, physical distribution and supply chain management operations and associated strategic issues. Quantitatively oriented mathematical and modelling research papers are not suitable for IJPDLM. Desired topics include, but are not limited to: Customer service strategy Omni-channel and multi-channel distribution innovations Order processing and inventory management Implementation of supply chain processes Information and communication technology Sourcing and procurement Risk management and security Personnel recruitment and training Sustainability and environmental Collaboration and integration Global supply chain management and network complexity Information and knowledge management Legal, financial and public policy Retailing, channels and business-to-business management Organizational and human resource development Logistics and SCM education.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信