Nowadays, due to increased competition caused by globalization and high rates of innovation supply chains continue to have shorter life-cycle products. More liberal return policies, increasing enforcement of take-back laws, heightened environmental regulations, increase in financial returns, good corporate image, increasing customer demands, have made enterprises face the challenges of strategically managing the returned and discarded products. Therefore, handling of product returns has led the reverse logistics activities to be effective and efficient. Efficiency of reverse logistics is achieved by reducing the waste, recapturing recovered value, reducing inventory investments, and optimizing the collection networks. Reverse logistics effectiveness allows enterprises an opportunity to improve competitiveness by building consumer confidence through handling of returned products, liberalized returns policies, operations of take-back networks, and green aspects of performance. But due to the intrinsic complexities of reverse logistics operations, such as uncertainty in quality, quantity, and timing of returns, makes the product returns process more complicated. The present literature on reverse logistics focuses on the factors that support the enterprises to manage and optimize their operations to remain competitive, but does not reflect upon the comprehensive performance measurement on how enterprises have to measure their reverse logistics activities. To contribute to the field, this research is carried out to study the performance measurement in reverse logistics enterprise to fill the gap in the literature. This dissertation presents a CRLEPMS - Comprehensive Reverse Logistics Enterprise Performance Measurement methodology to facilitate performance measurement and decision making for the enterprise involved in reverse logistics. It examines different performance measurement attributes and criteria for measuring reverse logistics performance. It investigates the inner and inter relationships among different criteria of attributes and also among clusters of attributes applying DEMATEL, FANP and AHP MCDM methods. Further, the enterprise computes the comprehensive overall performance index in order to benchmark its performance with best in class practices. The application of the CRLEPMS methodology provides the enterprises with a real world viewpoint of what and how performance attributes and measures impact the comprehensive overall performance index, so that they get feedback and continuously improve their product returns practices