Degree Type

Thesis

Date of Award

1-1-2003

Degree Name

Master of Science

Department

Industrial and Manufacturing Systems Engineering

Major

Industrial Engineering

Abstract

The effective management of returned products is enhanced by reliable forecasts of returns. We explore a method of forecasting the returns in an electronic product remanufacturing environment under the assumptions that each item will be returned and the movement of each individual item can be tracked. We investigate the benefit of information from early returns on the predictability of future return times under a variety of conditions described by the variability in the sale time and the mean time to return. We also explore the effects of prior knowledge of the return time distribution on the precision of estimates. We estimate the parameters of the distribution of the time until items are returned using maximum likelihood estimation and determine approximate confidence intervals for the estimates. The approximate confidence intervals are also derived for both the mean time until items are returned and the proportion of items that will be returned before that specified product is obsolete. The method is illustrated under two scenarios depending on the prior knowledge about the return time distribution. The results show that prior knowledge about the return time distribution contributes greatly to the precision in estimating and that the widths of the confidence intervals for the estimates are also affected by the variability in sales time distribution. However, even without prior knowledge about return distribution characteristics, both the mean of the return time distribution and the proportion of items that will be returned before obsolescence can by estimated satisfactorily.

DOI

https://doi.org/10.31274/rtd-20200803-376

Copyright Owner

Suphalat Chittamvanich

Language

en

OCLC Number

52498700

File Format

application/pdf

File Size

94 pages

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