The concept of logistics efficiency, especially reverse logistics efficiency measuring has become one of the key factors in our modern society as business and transportation become increasingly complex and networked. However, reverse logistics involves a high degree of uncertainty, which affects and makes evaluation more difficult. Our motivation and purpose is to present the efforts of one of the world’s leading retail companies to improve overall efficiency with a new supplementary measurement and analysis tool. Our initial hypothesis was that unladen logistics returns are inefficient and improvements in this area are more sustainable, so in our design and methodology approach we try to analyze logged data. According to our goals, this study is meant to demonstrate the significance of the reasons and the way to customize data analysis to formulate more adequate suggestions. Through a live practical example, a presentation is given how we can identify and highlight the hotspots to improve reverse logistics. The main results and originality of the paper are to develop a practical scalable model framework which can be customized by companies having a similar problem. Contrary with the well-known DEA models the presented model a system thinking method that provides (up-to-date) information which enables better flexibility and highlights areas of interdependency for development projects.