Reason‐code based model to forecast product returns

This paper aims to propose a method for forecasting product returns based on reason codes. The methodology uses two approaches, namely central tendency approach and extreme point approach, and is developed for the consumer electronics industry.

The methodology presented here is based on the return reason codes (RC). The incoming returns are split into different categories using 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 on 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 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 the authors use a moving average of percent returns to estimate the central tendency.

Reason codes and consumer behavior in combination with statistical methods can be used to forecast product returns.

Consumer electronics retailers and manufacturers can effectively use this methodology to forecast product returns. This methodology effectively addresses and covers different product return scenarios.

This research paper shows the new way of forecasting product returns i.e. reason codes based forecasting by combining two approaches, namely extreme point approach and central tendency approach. Also, it shows a new way of translating the consumer behavior into meaningful data; that data can be fed to a model to forecast product returns.