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Forecasting returns in reverse logistics using GERT metwork theory

Forecasting returns in reverse logistics using GERT metwork theory

Zhou, Li ORCID: 0000-0001-7132-5935, Xie, JiaPing and Lin, Yong (2010) Forecasting returns in reverse logistics using GERT metwork theory. In: 5th International Conference on Responsive Manufacturing - Green Manufacturing, ICRM 2010. Institution of Engineering and Technology, Stevenage, UK, pp. 349-356. ISBN 9781849191999 (doi:10.1049/cp.2010.0456)

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Abstract

The objective of this study is to explore a new approach to the forecasting of returns. Here, ‘returns’ refers to used products which can be sorted into resalable products, remanufacturing-able parts, renewable materials, and otherwise disposable waste. This research establishes a model by adopting the Graphical Evaluation and Review Technique (GERT) network theory combined with Bill of Material (BOM) to forecast returns. By using this model, the probability, the quantity and the expected timing of the returns can be predicted. Additionally, in line with the product BOM, the corresponding scale of reuse, i.e. remanufacturing-able parts and renewable materials, can also be forecasted. A numeric example is provided at the end of the study

Item Type: Conference Proceedings
Title of Proceedings: 5th International Conference on Responsive Manufacturing - Green Manufacturing, ICRM 2010
Additional Information: [1] This paper forms part of the published proceedings of the 5th International Conference on Responsive Manufacturing - Green Manufacturing, ICRM 2010 held at Ningbo, China January 11-13 2010
Uncontrolled Keywords: forecasting, returns, reverse logistics, GERT network, BOM
Subjects: H Social Sciences > HD Industries. Land use. Labor
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Faculty / Department / Research Group: Faculty of Business > Department of Systems Management & Strategy
Faculty of Business > Supply Chain Management Research Group
Related URLs:
Last Modified: 14 Oct 2016 09:13
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/5277

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