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}
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.
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.