Predicting Vendor Performance in Data-Scarce Environments: A Hybrid Deep Learning Approach

DOI

10.2991/978-94-6239-711-8_35How to use a DOI?

Keywords

Vendor Performance Forecasting; Data Scarcity; Deep Learning; LSTM; Variational Auto-Encoder (VAE); MENA Region

Abstract

In the era of Industry 4.0, the imperative for proactive vendor risk management is predicated upon the precise forecasting of future performance. Nevertheless, the deployment of sophisticated Deep Learning (DL) architectures such as Long Short-Term Memory (LSTM) networks remains markedly constrained in developing economies owing to a profound paucity of historical time-series data. This study interrogates the cold start phenomenon by advancing an original Generative-Predictive Framework that fuses Variational Auto-Encoders (VAEs) with LSTM networks. Applied to a leading entity within the Algerian glass manufacturing sector (Mediterranean Float Glass: MFG), the framework utilises VAEs to reconstruct latent historical performance trajectories (2015–2019) from a limited dataset (2020–2023), thereby affording robust LSTM training for long-horizon forecasting (2026). To substantiate the methodology, a visual trajectory analysis is conducted for three representative vendor archetypes: Stable, Stagnant, and Deteriorating. The findings demonstrate that this hybrid approach efficaciously alleviates data scarcity, producing risk-aware forecasts that empower the buyer (MFG) to transcend reactive evaluation in favour of proactive, strategically informed sourcing.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Nacereddine Bouriche
AU  - Messaoud Djeddou
PY  - 2026
DA  - 2026/06/24
TI  - Predicting Vendor Performance in Data-Scarce Environments: A Hybrid Deep Learning Approach
BT  - Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)
PB  - Atlantis Press
SP  - 376
EP  - 385
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-711-8_35
DO  - 10.2991/978-94-6239-711-8_35
ID  - Bouriche2026
ER  -