By Topic

Methodology to forecast product returns for the consumer electronics industry

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Amit Potdar ; Department of Industrial and Manufacturing Systems Engineering, The University of Texas at Arlington, Arlington, TX - USA ; Jamie Rogers

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.

Published in:


Date of Conference:

18-22 July 2010