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.

Applications of ML/AI for Decision-Intensive Tasks in Production Planning and Control

Elbasheer M.;Longo F.
;
Padovano A.;Solina V.;
2022-01-01

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.
2022
Artificial Intelligence
Control
Dynamic Scheduling
Machine Learning
Monitoring
Performance Evaluation
Process Automation
Production Planning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/332048
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