A method to improve workers’ well-being toward human-centered connected factories

• Published in: Journal of computational design and engineering 2020, 7(5), , pp.630-643
• Main Authors: Papetti, Alessandra, Gregori, Fabio, Pandolfi, Monica, Peruzzini, Margherita, Germani, Michele
• Format: Article
• Language: English
• Subjects: 기계공학
• ISSN: 2288-5048; 2288-4300; 2288-5048
• DOI: 10.1093/jcde/qwaa047

Abstract One of the most actual and consistent drivers for the industry is sustainability, which includes three main pillars: environment, economics, and society. While numerous methods for environmental and economic sustainability assessment have been proposed, social sustainability assessment is still lacking in structured methods and tools, although human has always played a key role. Moreover, technological development is pushing the industrial world toward a new paradigm, the “Industry 4.0,” which embeds topics such as data digitalization, cyber-physical systems, and machine learning. It entails significant changes in human resources management, without reducing their importance. Humans were part of the manufacturing system from the first industrial revolution, and no automation or digitalization can be possible without humans. The industry can no longer underestimate the reasonable application of human factors and ergonomics principles to the workplace. For this purpose, the paper provides a novel transdisciplinary engineering method to measure and promote social sustainability on production sites. It exploits Internet of Things technology to support the (re)design of manufacturing processes and plants toward human-centered connected factories. To improve the workers’ well-being has positive effects on their health, satisfaction, and performance. The method has been implemented in a real industrial case study within the footwear industry. The sole finishing process has been analyzed from different perspectives to solve ergonomics-related problems and implement effective improvement strategies. Graphical Abstract Graphical Abstract