International Journal of Industrial Engineering and Management

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Vol. 13 No. 2 (2022)
Review Article

Time Series Based Forecasting Methods in Production Systems: A Systematic Literature Review

Raphael Hartner
University of Applied Sciences FH JOANNEUM
Bio
Vitaliy Mezhuyev
University of Applied Sciences FH JOANNEUM
Bio

Published 2022-06-30

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Keywords

  • Industrial forecasting,
  • Machine learning,
  • Neural network,
  • Production system,
  • Systematic literature review

How to Cite

Hartner, R., & Mezhuyev, V. (2022). Time Series Based Forecasting Methods in Production Systems: A Systematic Literature Review. International Journal of Industrial Engineering and Management, 13(2), 119–134. https://doi.org/10.24867/IJIEM-2022-2-306

Abstract

Forecasting in production systems is used to anticipate their quality, efficiency, and yield. However, to the best of our knowledge, there exists no systematic review for industrial fore- casting approaches. Thus, this work aimed to address this gap through a systematic literature review. The quantitative results revealed that industrial forecasting models are mainly ap- plied in three economic sectors, with recurrent neural network models being the dominant approach. Moreover, this work proposes a classification of forecasting applications based on common characteristics found in reviewed sources. Several additional insights were pro- duced, and future research directions were elaborated. Hence, this systematic review fosters an understanding of the current state-of-the-art of industrial forecasting approaches and facili- tates future research initiatives.

 

Article history: Received (October 25, 2021); Revised (April 04, 2022); Accepted (May 5, 2022); Published online (May 12, 2022)

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