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Artificial Intelligence and Information System Resilience to Cope With Supply Chain Disruption
IEEE Transactions on Engineering Management ; 2021.
Article in English | Scopus | ID: covidwho-1515171
ABSTRACT
Artificial Intelligence (AI) as a technology has the potential to interpret and evaluate alternatives where multidimensional data are involved in dynamic situations such as supply chain disruption. This article aims to explore the role of resilient information systems in minimizing the risk magnitude in disruption situations in supply chain operations. The article is conducted in the qualitative mode through a semistructured interview schedule for professionals of supply chains. A thematic analysis has been used to create emerging categories. The findings of this article present critical gaps in current information systems and demonstrate how AI-oriented systems can facilitate the ecosystem of disrupted supply chains to save costs and drive efficiency on multiple parameters. The article also proposes a conceptual framework where organizational values and architectural components can be viewed jointly for quick and adequate business decisions in complex and uncertain disruptions. The framework presents the relationships among AI, information systems, and supply chain disruption. Installing appropriate AI-based data acquisition, processing, and self-training capabilities along with information system infrastructure can help organizations lessen the impact of supply chain disruption while aligning the transportation network and ensuring geographically suitable supply chains and cybersecurity. Finally, the implications for theory and practice with the limitations and scope for future research are described. IEEE

Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study / Qualitative research Language: English Journal: IEEE Transactions on Engineering Management Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study / Qualitative research Language: English Journal: IEEE Transactions on Engineering Management Year: 2021 Document Type: Article