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Strategic Demand Forecasting with Machine Learning Algorithms in a Dyeing Company

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Towards Industry 5.0 (ISPR 2022)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

In global competitive conditions, correctly designed strategic demand forecasting studies aid to gain an advantage over competitors by accurately predicting production systems based on previous sales data in order to plan future demands in a consistent manner. Demand forecasting has a significant impact on the determining the production volumes of companies and preventing serious losses such as prestige and high costs. In this study, it is aimed to create demand models of a dyeing company for the next five years using recently developed machine learning tools such as random forests (RF), support vector regression (SVR) and artificial neural networks (ANN). The success of the models has been shown according to the error rates (MAPE, MSE, RMSE etc.) obtained as a result of the utilized methods. The proposed forecasting models will contribute to the strategic decision-making mechanisms of the company adequately for future projections. The effectiveness and applicability of the developed models are discussed in detail through a real case study.

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Authors and Affiliations

  1. Department of Industrial Engineering, Konya Food and Agriculture University, Konya, Turkey

    Vuslat Alp

  2. Department of Aviation Management, Faculty of Aviation and Space Sciences, Necmettin Erbakan University, Konya, Turkey

    Beyzanur Cayir Ervural

Authors
  1. Vuslat Alp
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  2. Beyzanur Cayir Ervural
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Correspondence to Beyzanur Cayir Ervural .

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Editors and Affiliations

  1. Institute of Production Engineering and Photonic Technologies, TU Wien, Vienna, Wien, Austria

    Numan M. Durakbasa

  2. Faculty of Engineering and Natural Sciences, Industrial Engineering Department, Istanbul Zaim University, Istanbul, Türkiye

    M. Güneş Gençyılmaz

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Alp, V., Ervural, B.C. (2023). Strategic Demand Forecasting with Machine Learning Algorithms in a Dyeing Company. In: Durakbasa, N.M., Gençyılmaz, M.G. (eds) Towards Industry 5.0. ISPR 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-24457-5_16

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  • DOI: https://doi.org/10.1007/978-3-031-24457-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24456-8

  • Online ISBN: 978-3-031-24457-5

  • eBook Packages: EngineeringEngineering (R0)

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