Integrating Ergonomic Factors with Waste Identification Diagram to Enhance Operator Performance and Productivity in the Textile Industry

Industries have introduced lean manufacturing systems to outperform their competitors and sustain their growth. The implementation of lean tools results in satisfying the customer needs. Industries focus primarily on technical assistance when implementing lean strategies, but the success and sustainability of a lean strategy largely depend on the skill and cooperation of the workers. Research findings show that most industries have not attached importance to human factors while implementing lean logic. The negligence of human factors affects the quality of life of workers. Hence, this study intends to improve the quality of life and efficiency of workers by integrating ergonomic factors with the implementation of lean strategies in apparel industry. To accomplish this, the waste identification diagram was improved by adding a component to determine operators’ performance and analyse human factors. The ergonomic-waste identification diagram has been created to identify tasks related to ergonomic investigations and analyse human factors with lean metrics. The results point to the fact that an egalitarian approach increases the performance of operators and productivity of the organization

A Time-Performance Improvement Model with Optimal Ergonomic Risk Level Using Genetic Algorithm

The optimization of productivity has received significant attention in the manufacturing field. The majority of operations in the manufacturing business are still performed by workers. The analysis of work efficiency and the avoidance of ergonomic risk levels in the production line of clothing industry is critical. The correlation between a task in production and a reduction in ergonomic risks has been rarely considered in previous studies. This study proposes a time-performance improvement model with an optimal ergonomic risk level using a genetic algorithm; the model is intended to be used in the garment industry and reduce the gap for real-world applications. The results show that by distributing management training and limiting ergonomic risk factors, operator performance of selected operations can be improved, resulting in an optimum solution. The proposed model was implemented through case studies, and the operator performance improved from 73.68% to 92.76%. The significant element of this study is to use ergonomic improvement to increase operator performance through a time-performance improvement model