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

• Published in: Computers & industrial engineering 2023-03, Vol.177, p.109055, Article 109055
• Main Authors: Ahmed, Tazim, Karmaker, Chitra Lekha, Nasir, Sumaiya Benta, Moktadir, Md. Abdul, Paul, Sanjoy Kumar
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Bayesian Best-Worst Method; Industry 5.0; Post-COVID-19 pandemic; Supply chain resilience
• ISSN: 0360-8352; 1879-0550; 1879-0550
• DOI: 10.1016/j.cie.2023.109055

•We analyze AI-based imperatives of industry 5.0 toward resilient supply chains.•We develop an integrated approach to analyze the imperatives.•We integrate Pareto analysis, the Bayesian approach, and the Best-Worst Method.•We discuss the managerial implications for practitioners. 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 potential to build an inclusive digital future by ensuring supply chain (SC) resiliency and sustainability. Accordingly, 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 potential 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.