Forecasting of Used Product Returns for Remanufacturing

M. W. Geda, C. K. Kwong

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

To meet the demand for remanufactured products, accurate forecasting of used product returns is needed. The quantity and timing of the returns of used products for remanufacturing depend on the quantity of new products sold in previous periods. Conventional time series forecasting techniques are not able to capture the relationship between past sales and future returns and hence cannot be used to predict used product returns for remanufacturing. Distributed lag models (DLMs) which can model the dependence of future returns on past sales has been proposed in previous studies for forecasting used product returns. However, the choice of an appropriate lag function for the DLM and estimation of the parameters of the lag function were the main challenges in previous studies. In this research, a DLM with a negative binomial lag function is proposed for forecasting used product returns, and Bayesian Markov Chain Monte Carlo simulation is used to estimate of the parameters of the lag function. To validate the forecasting model, the mean absolute error (MAE) and the mean absolute percent error (MAPE) are computed. Numerical experiments were conducted to illustrate the proposed forecasting model and the parameter estimation approach. The results showed the proposed forecasting model predicts used product returns with good accuracy.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
PublisherIEEE Computer Society
Pages889-893
Number of pages5
ISBN (Electronic)9781538667866
DOIs
Publication statusPublished - 9 Jan 2019
Event2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018 - Bangkok, Thailand
Duration: 16 Dec 201819 Dec 2018

Publication series

NameIEEE International Conference on Industrial Engineering and Engineering Management
Volume2019-December
ISSN (Print)2157-3611
ISSN (Electronic)2157-362X

Conference

Conference2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
Country/TerritoryThailand
CityBangkok
Period16/12/1819/12/18

Keywords

  • Bayesian Markov-chain Monte-Carlo
  • Forecasting
  • Product returns
  • Remanufacturing
  • Uncertainty

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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