Deep Transfer Learning for Installed Base Life-Cycle Evolution Forecast

Emna Turki, Emna Turki, Oualid Jouini, Ziad Jemai, Robert Heidsieck

2024

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.

Paper Citation

in Harvard Style

Turki E., Jouini O., Jemai Z. and Heidsieck R. (2024). Deep Transfer Learning for Installed Base Life-Cycle Evolution Forecast. In Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES; ISBN 978-989-758-681-1, SciTePress, pages 398-402. DOI: 10.5220/0012467500003639

in Bibtex Style

@conference{icores24,
author={Emna Turki and Oualid Jouini and Ziad Jemai and Robert Heidsieck},
title={Deep Transfer Learning for Installed Base Life-Cycle Evolution Forecast},
booktitle={Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES},
year={2024},
pages={398-402},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012467500003639},
isbn={978-989-758-681-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES
TI - Deep Transfer Learning for Installed Base Life-Cycle Evolution Forecast
SN - 978-989-758-681-1
AU - Turki E.
AU - Jouini O.
AU - Jemai Z.
AU - Heidsieck R.
PY - 2024
SP - 398
EP - 402
DO - 10.5220/0012467500003639
PB - SciTePress