: Reverse logistics management has evolved as one of the new research fields of management, in order to help companies to recognize the potential resources and challenges associated with operation and strategy. Reverse logistics defines as “The process of planning, implementing, and controlling the efficient, cost effective flow of raw materials, in-process inventory, finished goods, and related information from the point of consumption to the point of origin for the purpose of recapturing or creating value or proper disposal”. Reverse logistics management has evolved as one of the new research fields of management, in order to help companies to recognize the potential resources and challenges associated with operation and strategy. In reverse logistics return processes are considered, as well as all logistics activities. Proper application of this processes not only enables management to manage effectively the flow of returned products, but also identifying opportunities to reduce unwanted returned and control reusable capital. Effective management of returned products is an important part of supply chain management that enables organizations to achieve sustainable competitive advantage. According to increasing importance of reverse logistics in organization, performance measurement of return processes will be needed. In this research, after explaining the concept of reverse logistics, we develop a model for assessing organization’s performance in reverse logistics field. This model integrates Balanced Scorecard and Data Envelopment Analysis, moreover uses Fuzzy Analysis Hierarchical Process for ranking weights. This model considers five perspectives in balanced scorecard including innovation and growth, internal and external process, costumer, environmental, and financial. Also we develop two-stage data envelopment analysis for four stages. In order to verify the validation of model, it is implemented in Pak Dairy Company. The result show that, Hormozlaban branch has better performance than other branches of company in all perspectives and is recognized as the most effective DMU and can serve as a benchmarking for other DMUs. Reverse logistics time cycle, products returned collection time, disposal capability, wastes reduce, reverse logistics expenses, and products returned acceptance costs indicators have direct relation with reverse logistics results show that except reverse logistics time cycle and disposal capability indicators which gain lower bound, other indicators have direct influence in final performance.
