 Predicting the reverse flow of spare parts in a complex supply chain: contribution of hybrid machine learning methods in an industrial contextHamza El Garrab, David Lemoine, Adnane Lazrak, Robert Heidsieck and Bruno Castanier International Journal of Logistics Systems and Management, 2023, vol. 45, issue 2, 131-158 Abstract: A key goal of after-sale services is to achieve customer satisfaction by providing a high-quality of service post-sale. In this context, repairable spare parts play a considerable role in balancing between service level and inventory value considering their relative cost compared to new buy parts. This article deals with forecasting the load of a repair centre in a closed-loop service parts supply chain. Since this supply chain is subject to variabilities and experts are modifying replenishment decisions, it is difficult to forecast this load and especially its peaks, this lack of visibility is creating difficulties to organise the repair centre. Thus, a methodology based on a hybrid machine learning method is proposed and then tested on real data from General Electric Healthcare. These tests showed a real improvement of the accuracy compared to simple machine learning methods or traditional forecasting methods. Keywords: forecast; hybrid machine learning; methodology; real data; spare parts; closed-loop supply chain. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijlsma:v:45:y:2023:i:2:p:131-158 Access Statistics for this article More articles in International Journal of Logistics Systems and Management from Inderscience Enterprises Ltd |
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