# L'Apprentissage Automatique dans la planification et le controle de la production : un 'etat de l'art

arXiv: https://arxiv.org/abs/2106.12916 · HTML: https://mlsanity.com/paper/2106.12916/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 53 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Abstract (arXiv)

Proper Production Planning and Control (PPC) is capital to have an edge over competitors, reduce costs and respect delivery dates. With regard to PPC, Machine Learning (ML) provides new opportunities to make intelligent decisions based on data. Therefore, this communication provides an initial systematic review of publications on ML applied in PPC. The research objective of this study is twofold: firstly, it aims to identify techniques and tools allowing to apply ML in PPC, and secondly, it reviews the characteristics of Industry 4.0 (I4.0) in recent research papers. Concerning the second objective, seven characteristics of I4.0 are used in the analysis framework, from which two of them are proposed by the authors. Additionally, the addressed domains of ML-aided PPC in scientific literature are identified. Finally, results are analyzed and gaps that may motivate further research are highlighted.