Authors:
Emna Turki
1
;
2
;
Oualid Jouini
2
;
Ziad Jemai
3
and
Robert Heidsieck
1
Affiliations:
1
General Electric Healthcare, 283 Rue de la Minière, 78530 Buc, France
;
2
Laboratoire Genie Industriel, Centrale Supélec, Université Paris-Saclay, 3 rue Joliot-Curie, 91190 Gif-sur-Yvette, France
;
3
Laboratoire OASIS, École Nationale d’Ingénieurs de Tunis, Université Tunis El Manar, BP37, 1002 Tunis, Tunisia
Keyword(s):
Healthcare Industry, Closed Loop Supply Chain, Transfer Learning, Deep Learning, Installed Base Forecast.
Abstract:
In Healthcare industry, companies are reducing their environmental impact by implementing a closed loop supply chain (CLSC) in which products can be de-installed and bought back for reconditioning or parts reuse. In this supply chain, it is necessary to implement the appropriate strategies to ensure a sustainable parts management system knowing that the installed base (IB) evolution and the products design changes are highly impacting factors. Since strategic CLSC decisions are taken early in the part and/or product life-cycles, usu-ally there is not enough data to predict the IB information. Therefore, We build a Deep Transfer learning framework to forecast the products IB evolution from the beginning to the end-of-life (EOL) using data of different generations from the same product family. We provide a use case from a Healthcare company showing the performance of different deep learning models on a long horizon.