Technical Brief

Predictive Model Selection for Forecasting Product Returns

[+] Author and Article Information
Jungmok Ma

Department of National Defense Science,
Korea National Defense University,
Seoul 10544, South Korea
e-mail: jungmokma@kndu.ac.kr

Harrison M. Kim

Associate Professor
Industrial and Enterprise Systems Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: hmkim@illinois.edu

1Corresponding author.

Contributed by the Design for Manufacturing Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 22, 2015; final manuscript received February 4, 2016; published online April 6, 2016. Assoc. Editor: Carolyn Seepersad.

J. Mech. Des 138(5), 054501 (Apr 06, 2016) (5 pages) Paper No: MD-15-1664; doi: 10.1115/1.4033086 History: Received September 22, 2015; Revised February 04, 2016

As awareness of environmental issues increases, the pressures from the public and policy makers have forced original equipment manufacturers (OEMs) to consider remanufacturing as the key product design option. In order to make the remanufacturing operations more profitable, forecasting product returns is critical due to the uncertainty in quantity and timing. This paper proposes a predictive model selection algorithm to deal with the uncertainty by identifying a better predictive model. Unlike other major approaches in literature such as distributed lag models or DLMs, the predictive model selection algorithm focuses on the predictive power over new or future returns and extends the set of candidate models. The case study of reusable bottles shows that the proposed algorithm can find a better predictive model than the DLM.

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Grahic Jump Location
Fig. 2

Difference between DLM and proposed approach

Grahic Jump Location
Fig. 1

A closed-loop supply chain

Grahic Jump Location
Fig. 3

Sales and returns of reusable bottles (redrawn from Ref.[6])



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