Green Supply Chain Management (GSCM) has received more attention in the last few years in academia and industries. As customers are becoming more environmental conscious and governments are making stricter environmental regulations, the industries need to reduce the environmental impact of their supply chain and the requirement of GSC increased. The main aim of this paper is to determine the relationship among the barriers and to identify the most influential barriers from the recommended barrier list with the help of interpretive structural modelling. Classification of barriers has been carried out based upon dependence and driving power with the help of MICMAC analysis. A structural model of barriers to implement GSCM in Indian industry has also been put forward using Interpretive Structural Modelling (ISM) technique. The study has been conducted in three different phases: identification of barriers from the literature, interviews with various department managers. Twenty numbers of relevant barriers have been identified. Out of which, nineteen numbers of barriers have been identified as linkage variables; one number of barriers have been identified as the driver variables and no barriers have been identified as the dependence variables. No barrier has been identified as autonomous variable. Eight barriers have been identified as top level barriers and one bottom level barrier. Clear understanding of these barriers will help organizations to prioritize better and manage their resources in an efficient and effective way. The contribution by this work is to identify the barriers to implement GSCM in Indian industry and to prioritize them. The proposed structured model developed will help to understand interdependence of the barriers

Reverse supply chain logistics, means mobility of goods from end consumers towards core manufacturer in the channel of product distribution. In the turbulent business environment, the companies must promote alternative uses of resources that may be cost-effective and ecology friendly by extending products’ routine life cycles. Reveres logistics activities i.e. storing, transporting and handling of used products poses a great challenge to reverse logistics managers as there is always chances of uncertainty in terms of quantity, quality and timing of return of EOL products in case of reverse supply chains. Business organizations including those of white/electronics goods manufacturing industries would like to focus on their core competency areas and there is need of making outsourcing decisions of their reverse logistics process to Third-Party reverse Logistics Providers (3PRLPs). Thus, most important strategic issue for top management is the evaluation and selection of third party logistics service provider who can effectively provide reverse logistics operation services to the firms. The objective of this work is to develop decision support system to assist the top management of the company in selection and evaluation of different 3PRL service providers by hybrid approach using Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. A real life case study of a mobile phone manufacturing company is presented to demonstrate the steps of the decision support system. Present study also enables the logistics managers to better understand the complex relationships of the key attributes in the decision making environment and subsequently improve the reliability of the decision making process

Reverse logistics has evolved to assist companies in recognizing potential benefits and overcoming challenges associated with its operations and strategies. Reverse logistics has a considerable influence both on production planning and management and on the determination of optimal production and storage capacities. Product recovery, which encompasses reuse, remanufacturing and materials recycling, requires a structured reverse logistic network in order to collect products efficiently at the end of their life cycle. Present work describes simulation modelling of reverse logistics networks for collection of EOL products for XYZ Limited Company of North India. This company is involved in production of acid batteries for commercial use. Simulation model presented in this work, allows the user to analyze the future performance of the network and to understand the complex relationship between the parties involved. The findings from the simulation suggest that the model calculates cycle time, transfer time, transfer cost, and resource utilization in a predictable manner. Simulation model was developed using Arena 11.0 simulation package

The aim of present research work is to hybrid the “MOORA and WASPAS” MCDM methods in the sustainable vendor’s selection & evaluation decision. It also considers the feedback impact of the decision on strategic variables that determine the future viability of the case company in the competitive business environment. MCDM strategic decision framework is applied that considers simultaneously the influence of criteria and sub-criteria on the sustainable vendor evaluation decision. The developed framework is evaluated, and criteria weight is determined using the step-wise weight assessment ratio (SWARA) method. The MOORA & WASPAS consider the influence of criteria, sub-criteria and their interdependencies simultaneously in the system. The results of the model is the relative priorities ad ranking developed for the suppliers. The present research has been conducted in a battery manufacturing company of Punjab state of India. The influence of dimension of sustainability dimensions would be widespread due to the big market in which the case company operates. The outcome the present work can provide support to the managers who are decision makers in the industries. The framework present in the research arriving at an optimal sound decision considering all sustainability applicable dimensions. The novelty of the approach lies in the use of different MCDM model integrating the dimension of sustainability with a feedback effect for vendor selection. The industry would be benefited by showing its commitment toward social responsibility, environment management, and economic aspects leading to improved market image and profitable business with sustainability.