Human Factor Index Measurement Using an ISM-SEM-Fuzzy Approach

Abstract

In recent years, there has been a trend toward automation and data exchange in manufacturing processes through industrial cognitive computing, the Internet of Things (IoT), and artificial intelligence. However, the human–machine interface plays a role in establishing a smart manufacturing system in any industry. It is necessary to develop a comprehensive model to identify the risk factors that contribute to the loss of human performance and productivity and evaluate the workplace for its compliance and agility toward safe human–machine systems. In this study, a model is proposed that can be used as a measurement tool to design ergonomic workplaces in the automotive industry. Several criteria have been classified under four enablers: physiological factors, psychological factors, environmental factors, and safety factors. These were identified through a literature review. The proposed model integrates the applications of structural equation modeling (SEM), interpretive structural modeling (ISM), and the multigrade fuzzy approach. ISM was employed to demonstrate the applicability of the model to depict various ergonomic enablers considered in the ergonomic measurement. SEM was used to validate the ergonomic measurement model statistically. Physiological factors were found to be highly correlated with ergonomic practices. Physiological and psychological factors were also highly correlated. The use of the multigrade fuzzy approach was demonstrated to determine the human factor index for an automotive component manufacturing industry. The proposed model can enable management to evaluate the various risk factors that hamper the ergonomic level of a company and thereby allow the company to harness the benefits of ergonomics to enhance safety and productivity.