Applications of ML/AI for Decision-Intensive Tasks in Production Planning and Control
Mohaiad Elbasheer, Francesco Longo, Letizia Nicoletti, Antonio Padovano, Vittorio Solina, Marco Vetrano
Abstract
The fast-growing literature corpus of industry 4.0 in the last years also reflects the various applications of Artificial Intelligence (AI) and Machine Learning (ML) in the manufacturing domain. This paper conducts a literature survey to infer patterns and correlations within this scientific domain. This analysis aims to identify the knowledge gaps within the literature of ML/AI-enabled manufacturing systems. Therefore, the objective of this study is twofold; first, we aim to identify commonalities and general trends in AI/ML-enabled manufacturing systems. Secondly, we map the various application areas of AI/ML-based Production Planning and Control (PPC). To this end, an overall analysis of the keywords highlighted four major scientific scopes for ML/AI in manufacturing systems. Moreover, further investigation of this literature corpus was made to identify the different applications for AI/ML to enhance decision-making in PPC. Based on this analysis, three major pillars were identified as potential areas for AI/ML-enabled PPC for decision intensive tasks: dynamic scheduling, performance evaluation and monitoring, and automated process control.