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Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0

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Abstract

Because of their cross-functional nature in the company, enhancing Production Planning and Control (PPC) functions can lead to a global improvement of manufacturing systems. With the advent of the Industry 4.0 (I4.0), copious availability of data, high-computing power and large storage capacity have made of Machine Learning (ML) approaches an appealing solution to tackle manufacturing challenges. As such, this paper presents a state-of-the-art of ML-aided PPC (ML-PPC) done through a systematic literature review analyzing 93 recent research application articles. This study has two main objectives: contribute to the definition of a methodology to implement ML-PPC and propose a mapping to classify the scientific literature to identify further research perspectives. To achieve the first objective, ML techniques, tools, activities, and data sources which are required to implement a ML-PPC are reviewed. The second objective is developed through the analysis of the use cases and the addressed characteristics of the I4.0. Results suggest that 75% of the possible research domains in ML-PPC are barely explored or not addressed at all. This lack of research originates from two possible causes: firstly, scientific literature rarely considers customer, environmental, and human-in-the-loop aspects when linking ML to PPC. Secondly, recent applications seldom couple PPC to logistics as well as to design of products and processes. Finally, two key pitfalls are identified in the implementation of ML-PPC models: the complexity of using Internet of Things technologies to collect data and the difficulty of updating the ML model to adapt it to the manufacturing system changes.

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Acknowledgements

This work was financially supported by a partnership between the company iFAKT France SAS and the ANRT (Association Nationale de la Recherche et de la Technologie) under the Grant 2018/1266. Furthermore, the authors thank the Editor-in-chief and three anonymous referees who helped improve the quality of this paper through their comments and suggestions.

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Correspondence to Juan Pablo Usuga Cadavid.

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Appendix

Appendix

Appendix I: Detail on the search strategy for the bibliometric analysis

See Figs. 14, 15 and 16.

Fig. 14
figure14

Search strategy detail for “Deep Learning” OR “Machine Learning”

Fig. 15
figure15

Search strategy detail for “Data Mining”

Fig. 16
figure16

Search strategy detail for “Statistical Learning”

Appendix II: Usage evolution of the top 6 most used techniques

See Figs. 17, 18, 19, 20, 21 and 22.

Fig. 17
figure17

Usage evolution for Neural Networks

Fig. 18
figure18

Usage evolution for Q-Learning

Fig. 19
figure19

Usage evolution for decision trees

Fig. 20
figure20

Usage evolution for clustering

Fig. 21
figure21

Usage evolution for regression

Fig. 22
figure22

Usage evolution for ensemble learning

Appendix III: Detail on NN and Ensemble learning techniques

See Fig. 23.

Fig. 23
figure23

Detail on the techniques of the NN and ensemble learning families

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Usuga Cadavid, J.P., Lamouri, S., Grabot, B. et al. Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0. J Intell Manuf (2020). https://doi.org/10.1007/s10845-019-01531-7

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Keywords

  • Machine learning
  • Industry 4.0
  • Smart manufacturing
  • Production planning and control
  • State-of-the-art
  • Systematic literature review