An Industry 4.0 Technology Selection Framework for Manufacturing Systems and Firms Using Fuzzy AHP and Fuzzy TOPSIS Methods

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.