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Industry 4.0 aims to revolutionize the manufacturing sector to achieve sustainable and efficient production. The novel coronavirus pandemic has brought many challenges in different industries globally. Shortage in supply of raw material, changes in product demand, and factories closures due to general lockdown are all examples of such challenges. The adaption of Industry 4.0 technologies can address these challenges and prevent their recurrence in case of another pandemic outbreak in future. A prominent advantage of Industry 4.0 technologies is their capability of building resilient and flexible systems that are responsive to exceptional circumstances such as unpredictable market demand, supply chain interruptions, and manpower shortage which can be crucial at times of pandemics. This work focuses on discussing how different Industry 4.0 technologies such as Cyber Physical Systems, Additive Manufacturing, and Internet of Things can help the manufacturing sector overcome pandemics challenges. The role of Industry 4.0 technologies in raw material provenance identification and counterfeit prevention, collaboration and business continuity, agility and decentralization of manufacturing, crisis simulation, elimination of single point of failure risk, and other factors is discussed. Moreover, a self-assessment readiness model has been developed to help manufacturing firms determine their readiness level for implementing different Industry 4.0 technologies.

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Characterized by its resilience, connectivity, and real-time data processing capabilities, the fourth industrial revolution, referred to as Industry 4.0, is the main driver of today’s digital transformation. It is crucially important for manufacturing facilities to correctly identify the most suitable Industry 4.0 technologies that meet their operational schemes and production targets. Different technology selection frameworks were proposed to tackle this problem, several of which are complex, or require historic data from manufacturing facilities that might not always be available. The aim of this paper is to develop a novel Industry 4.0 selection framework that utilizes Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) to rank different Industry 4.0 technologies based on their economic, social, and environmental impact. The framework is also implemented on a real-life case study of a manufacturing firm to rank the different Industry 4.0 technologies required for its digital transformation based on their significance to the facility’s key performance indicators. The framework is utilized to select the top three Industry 4.0 technologies from a pool of eight technologies that are deemed important to the manufacturing firm. Results of the case study showed that Cyber-Physical Systems, Big Data analytics, and autonomous/industrial robots are the top three ranked technologies, having closeness coefficient scores of 0.964, 0.928, and 0.601, respectively. Moreover, the framework showed sensitivity towards weight changes. This is an advantage in the developed framework, since its main aim is to provide policymakers with a customized list of technologies based on their importance to the firm.