Influence of creating an oligopoly through government intervention to improve partner collaboration intentions in the context of green supply chains

Sustainability in Pakistan's textile industry: analyzing barriers and strategies for green supply chain management implementation

The industries view green supply chain management (GSCM) as a viable means of achieving sustainable operations by reducing environmental impact and enhancing operational performance. Although conventional supply chains still dominate many industries, integrating eco-friendly practices through green supply chain management (GSCM) is crucial. Nonetheless, there are several barriers that hinder the successful adoption of GSCM practices. Therefore, this study proposes fuzzy-based multi-criteria decision-making approaches comprised of the Analytical Hierarchy Process (FAHP) and the Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS). The study evaluates and overcomes barriers to the adoption of GSCM practices in the textile manufacturing sector of Pakistan. After the comprehensive literature review, this study identifies 6 barriers, 24 sub-barriers, and 10 strategies. The FAHP method employs to analyze the barriers and sub-barriers. Then, the FTOPSIS method ranks the strategies to overcome various identified barriers. Based on the FAHP results, the most significant barriers to the adoption of GSCM practices are technological (MB4), financial (MB1), and information and knowledge (MB5). Further, the FTOPSIS indicates that increasing the research and development capacity (GS4) is the most vital strategy for implementing GSCM. The study's findings have important implications for policymakers, organizations, and other stakeholders interested in promoting sustainable development and implementing GSCM practices in Pakistan.

Robust design of a green-responsive closed-loop supply chain network for the ventilator device

This study aims to investigate the closed-loop supply chain network design problem considering the environmental and responsiveness features. For this purpose, a multi-objective mathematical model is suggested that minimizes the carbon emissions and the total costs and maximizes the responsiveness of the system. Due to the dynamic space of the business environment, uncertainty is an integral part of the supply chain problem. Therefore, this research applies the robust possibilistic programming method to cope with uncertainty. Afterwards, since the research problem has a high level of the complexity, a hybrid solution approach based on a heuristic method and the meta-goal programming method is developed to solve the research problem in a reasonable time. Then, due to the importance of the ventilator device during the recent pandemic (COVID-19), this study considers this product as a case study. The main contribution of the current study is to design a green-responsive closed-loop supply chain network under uncertainty using a multi-objective robust possibilistic programming model, for the first time in the literature, especially in the medical devices industry. On the other side, the other contribution of this study is to develop an efficient hybrid solution method. The achieved results demonstrate the efficiency of the offered model and the developed hybrid method. Eventually, by carrying out sensitivity analysis, the impact of some of the critical parameters on the model is investigated. Based on the obtained results, an increase in the demand sizes leads to increasing the environmental damages and the total costs while reducing the responsiveness level. On the other side, an increase in the rate of return leads to an increase in all of the objective functions. Also, the achieved results show that when the capacity parameter is increased, the total costs are decreased, but the responsiveness and environmental impacts are increased.