4.7 Article

Modeling the artificial intelligence-based imperatives of industry 5.0 towards resilient supply chains: A post-COVID-19 pandemic perspective

Journal

COMPUTERS & INDUSTRIAL ENGINEERING
Volume 177, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2023.109055

Keywords

Post-COVID-19 pandemic; Supply chain resilience; Artificial intelligence; Industry 5; 0; Bayesian Best-Worst Method

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The COVID-19 pandemic has disrupted manufacturing activities in emerging economies, impacting their global supply chains. To survive and thrive in the post-COVID era, adopting artificial intelligence (AI) technologies to revamp traditional manufacturing activities is crucial. Industry 5.0 and AI offer the potential to build a resilient and sustainable digital future. This research aims to identify and evaluate the AI-based imperatives of Industry 5.0 to improve supply chain resilience using an integrated and intelligent approach. Real-time tracking of supply chain activities using IoT is found to be the most crucial AI-based imperative for manufacturing supply chain survivability. The research findings can assist industry leaders and practitioners in dealing with the impacts of large-scale supply chain disruptions in the post-COVID era.
The recent COVID-19 pandemic has significantly affected emerging economies' global supply chains (SCs) by disrupting their manufacturing activities. To ensure business survivability during the current and post-COVID-19 era, it is crucial to adopt artificial intelligence (AI) technologies to renovate traditional manufacturing activities. The fifth industrial revolution, Industry 5.0 (I5.0), and artificial intelligence (AI) offer the overwhelming po-tential to build an inclusive digital future by ensuring supply chain (SC) resiliency and sustainability. Accord-ingly, this research aims to identify, assess, and prioritize the AI-based imperatives of I5.0 to improve SC resiliency. An integrated and intelligent approach consisting of Pareto analysis, the Bayesian approach, and the Best-Worst Method (BWM) was developed to fulfill the objectives. Based on the literature review and expert opinions, nine AI-based imperatives were identified and analyzed using Bayesian-BWM to evaluate their po-tential applicability. The findings reveal that real-time tracking of SC activities using the Internet of Things (IoT) is the most crucial AI-based imperative to improving a manufacturing SC's survivability. The research insights can assist industry leaders, practitioners, and relevant stakeholders in dealing with the impacts of large-scale SC disruptions in the post-COVID-19 era.

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