Managing Operational Uncertainty in Manufacturing with Industry 4.0 and 5.0 Technologies: A Modified Neo-Configurational Perspective

Abstract

Purpose - The manufacturing sector drives industrialisation and contributes substantially to economic growth and employment creation. Despite this, it faces challenges of diminishing size and lack of competitiveness, mainly due to operational uncertainty. This study aims to develop an approach to managing operational uncertainty with Industry 4.0 and 5.0 technologies.Design /methodology/approach - The study employed a multimethod quantitative design based on the post-positivist paradigm, with data collected from 22 experts and 262 responses from a manufacturing firms' survey. It uses an integrated fuzzy decision-making trial and evaluation laboratory (DEMATEL) with structural equation modelling partial least squares (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). Findings - The results reveal that growing geopolitical tension, cost of living-driven consumer behavioural change, pandemic turbulence, lack of energy stability and security, and entrenchment power of large firms are causal dimensions of operational uncertainty. Industry 4.0 and 5.0 technologies with capabilities for scenario planning and supply chain integration, flexible production and mass customisation, real-time system and process monitoring and response, root cause analysis, and sustainable solutions can manage operational uncertainty. These technologies are artificial intelligence, the Internet of Things, big data analytics and, to a lesser extent, advanced robotics, blockchain, and augmented and virtual reality.Originality - The study advanced the modified neo-configuration theory and a new integrated methodology (fuzzy-DEMATEL-PLS-SEM-fsQCA) to develop solutions for sustained performance during operational uncertainty in manufacturing. This research offers valuable information to advance the subject and make meaningful changes in day-to-day manufacturing operations and promote practical real-world issue solving.