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Machine Learning in Production Planning and Control: A Review of Empirical Literature

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 paper provides an initial systematic review of publications on ML applied in PPC. The research objective of this study is to identify standard activities as well as techniques to apply ML in PPC. Additionally, the commonly used data sources in literature to implement a ML-aided PPC are identified. Finally, results are analyzed and gaps leading to further research are highlighted.
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https://hal-uphf.archives-ouvertes.fr/hal-03467620
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Submitted on : Monday, December 6, 2021 - 4:00:59 PM
Last modification on : Tuesday, January 4, 2022 - 5:53:30 AM

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Juan Pablo Usuga Cadavid, Samir Lamouri, Bernard Grabot, Arnaud Fortin. Machine Learning in Production Planning and Control: A Review of Empirical Literature. IFAC-PapersOnLine, Elsevier, 2019, 52 (13), pp.385-390. ⟨10.1016/j.ifacol.2019.11.155⟩. ⟨hal-03467620⟩

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