L'Apprentissage Automatique dans la planification et le contr{ô}le de la production : un {é}tat de l'art
This is an incremental review paper for researchers and practitioners in manufacturing and operations management.
This study conducted a systematic review of machine learning applications in production planning and control, identifying techniques, tools, and Industry 4.0 characteristics, while highlighting research gaps for future work.
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.