Productivity modeling of apparel industry using Hierarchical Evidential Reasoning

This paper aims to find a methodology to identify apparel productivity performance attributes. To achieve this aim, there is a need for a measurement program to manage both quantitative and qualitative measurements, considering uncertainties. In this research, the Hierarchical Evidential Reasoning (HER) procedure is utilized to handle uncertainties and complications in the productivity evaluation area. For evaluating apparel productivity, the HER method is used to develop a multi-criteria decision-making (MCDM) structure. The suggested structure is performed to assess the productivity of twelve garments industries in Bangladesh. After assessing productivity, a comparison and sensitivity analysis is conducted to identify the critical measures that contribute to enhance productivity. In this study, it can be observed that Yager's rule and Dempster-Shafer theory present administrators with a decision-making mechanism, which can help them to prioritize productivity characteristics, assessing the traits, and judgmental assessment to gain the total consequence of productivity. Therefore, examining the status allows the supervisors to elaborate plans for a greater state and enhance overall productivity. As one of the findings of this research based on Yager's rule and Dempster-Shafer theory, DBL Company, compared with 11 other organizations in Bangladesh, has the highest value of the index, which indicates the overall performance of DBL Company is much better compared to the other organizations. This research can be utilized as a critical achievement analysis mean by the organizations. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study utilizes the Hierarchical Evidential Reasoning (HER) method to evaluate apparel industry productivity, identifying key measures to enhance productivity through comparison and sensitivity analysis. The results show that DBL Company in Bangladesh performs the best overall, which holds significant implications for organizations.