Title: Predicting the reverse flow of spare parts in a complex supply chain: contribution of hybrid machine learning methods in an industrial context

Authors: Hamza El Garrab; David Lemoine; Adnane Lazrak; Robert Heidsieck; Bruno Castanier

Addresses: LARIS EA7315, Université d'Angers, Angers, France ' LS2N UMR CNRS 6004, IMT Atlantique, Nantes, France ' Global Service Supply Chain, GE Healthcare, Buc, France ' Global Service Supply Chain, GE Healthcare, Buc, France ' LARIS EA7315, Université d'Angers, Angers, France

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.

DOI: 10.1504/IJLSM.2023.131425

International Journal of Logistics Systems and Management, 2023 Vol.45 No.2, pp.131 - 158

Received: 26 Oct 2020
Accepted: 26 Feb 2021
Published online: 12 Jun 2023 *

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