L'Apprentissage Automatique dans la planification et le contr{\^o}le de la production : un {\'e}tat de l'art

TL;DR

This paper systematically reviews how Machine Learning techniques are applied to Production Planning and Control, highlighting recent research, tools, and Industry 4.0 characteristics, and identifying gaps for future exploration.

Contribution

It provides a comprehensive review of ML applications in PPC, including analysis of Industry 4.0 features and identification of research gaps.

Findings

Seven Industry 4.0 characteristics analyzed

Identification of ML techniques used in PPC

Highlighting research gaps and future directions

Abstract

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.