Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Forecasting for remanufacturing: The effects of serialization

Gkoltsos, Thanos E., Syntetos, Aris A. and van der Laan, Erwin 2019. Forecasting for remanufacturing: The effects of serialization. Journal of Operations Management 65 (5) , pp. 447-467. 10.1002/joom.1031
Item availability restricted.

[img] PDF - Accepted Post-Print Version
Restricted to Repository staff only until 3 June 2021 due to copyright restrictions.

Download (711kB)

Abstract

Remanufacturing operations rely upon accurate forecasts of demand and returned items. Return timing and quantity forecasts help estimate net demand (demand minus returns) requirements. Based on a unique data set of serialized transactional issues and returns from the Excelitas Group and one of their defense contractors, Qioptiq, we assess the empirical performance of some key methods in the area of returns forecasting. We extend their application (for net demand forecasting), by considering that demand is also subject to uncertainty and thus needs to be forecast. Information on remanufacturing costs allows for an evaluation of the inventory implications of such forecasts under various settings. A foray into the literature on information technologies enables a discussion on the interface between information availability and forecast accuracy and utility. We find that serialization accounts for considerable forecast accuracy benefits, and that the accuracy of demand forecasts is as important as that of returns. Further, we show how the combined returns and demand forecast uncertainty affects the inventory performance. Finally, we identify opportunities for further improvements for the operations of Qioptiq, and for remanufacturing operations in general.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Publisher: Elsevier
ISSN: 0272-6963
Funders: Innovate UK
Date of First Compliant Deposit: 4 June 2019
Date of Acceptance: 19 April 2019
Last Modified: 01 Aug 2019 11:41
URI: http://orca.cf.ac.uk/id/eprint/123085

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics