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.

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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.