A decision support model to investigate the pandemic recovery challenges and strategies in the leather supply chain

Abstract

The COVID-19 has caused unprecedented disruptions to supply chains (SC) worldwide, posing numerous challenges for industries, particularly in the emerging economies (EE). These economies are undergoing a phase of recovery from the pandemic devastations now, requiring investigation into the recovery challenges (RCs) and propositions for effective recovery strategies (RSs) to address RCs. Given this backdrop, this study aims to explore the COVID-19-related RCs in the Bangladeshi leather industry and build an integrated decision-making model to formulate RSs to counteract the RCs while the industry seeks to recover. This study used Pareto analysis to deduce lists of the nine most critical RCs and nine vital RSs for the Bangladeshi leather industry. This study also applied the best worst method (BWM) to identify a long-term liquidity crisis and an increasing bankruptcy of business stakeholders as the most urgent RCs, highlighting financial sustainability as a significant matter of concern for the sector. With regard to the RSs, the application of the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) indicated a need to solve the existing problems of central effluent treatment plant (CETP) and provisioning of solid waste management facilities for long run business as priorities to make the leather industry SC more financially and operationally sustainable. The RSs formulated in this study have managerial implications for decision-makers in reducing the adversities caused by the pandemic and hence improving the SC performance of the leather industry. Although not totally, these valuable insights into the RCs and RSs for the leather industry during and following COVID-19 periods can be generalized across other industries in Bangladesh and EE regions affected by the pandemic.