The use of the multigrade fuzzy approach was demonstrated to determine the human factor index for an automotive component manufacturing industry. Physiological and psychological factors were also highly correlated. Physiological factors were found to be highly correlated with ergonomic practices. SEM was used to validate the ergonomic measurement model statistically. ISM was employed to demonstrate the applicability of the model to depict various ergonomic enablers considered in the ergonomic measurement. The proposed model integrates the applications of structural equation modeling (SEM), interpretive structural modeling (ISM), and the multigrade fuzzy approach. These were identified through a literature review.
Several criteria have been classified under four enablers: physiological factors, psychological factors, environmental factors, and safety factors. In this study, a model is proposed that can be used as a measurement tool to design ergonomic workplaces in the automotive 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. However, the human–machine interface plays a role in establishing a smart manufacturing system in any industry. 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.
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