Skip to Main Content
One important aspect of reverse logistics is to have a correct and timely estimation of return flow of material. Improved forecast accuracy can lead to a better decision making in strategic, tactical and operational areas of the organization. Very little research has been done about the forecasting aspect of reverse logistics. For higher forecast accuracy, more robust method is required. The methodology presented here is based on the return reason codes (RC). The incoming returns are split into different categories using return reason codes. These reason codes are further analyzed to forecast returns. The computation part of this model uses a combination of two approaches namely extreme point approach and central tendency approach. Both the approaches are used separately for separate types of reason codes and then results are added together. The extreme point approach is based upon data envelopment analysis (DEA) as a first step combined with a linear regression while central tendency approach uses a moving average. For certain type of returns, DEA evaluates relative ranks of the products using single input and multiple outputs. Once this is completed, linear regression defines a correlation between relative rank (predictor variable) and return quantity (response variable). For the remaining type of returns we use a moving average of percent returns to estimate the central tendency. Thus, by combining two approaches for different types of return reason codes, we have developed a model that can be used to forecast product returns for the consumer electronics industry.