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Demand Forecasting for Textile Products Using Statistical Analysis and Machine Learning Algorithms

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Intelligent Information and Database Systems (ACIIDS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12672))

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Abstract

The generation of an accurate forecast model to estimate the future demand for textile products that favor decision-making around an organization's key processes is very important. The minimization of the model's uncertainty allows the generation of reliable results, which prevent the textile industry's economic commitment and improve the strategies adopted around production planning and decision making. That is why this work is focused on the demand forecasting for textile products through the application of artificial neural networks, from a statistical analysis of the time series and disaggregation in different time horizons through temporal hierarchies, to develop a more accurate forecast. With the results achieved, a comparison is made with statistical methods and machine learning algorithms, providing an environment where there is an adequate development of demand forecasting, improving accuracy and performance. Where all the variables that affect the productive environment of this sector under study are considered. Finally, as a result of the analysis, multilayer perceptron achieved better performance compared to conventional and machine learning algorithms. Featuring the best behavior and accuracy in demand forecasting of the analyzed textile products.

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Acknowledgment

The authors are greatly grateful by the support given by the SDAS Research Group (www.sdas-group.com).

Author information

Authors and Affiliations

  1. Universitat Politècnica de València, Camino de Vera S/N 46022, València, Spain

    Leandro L. Lorente-Leyva & M. M. E. Alemany

  2. SDAS Researh Group, Ibarra, Ecuador

    Leandro L. Lorente-Leyva & Diego H. Peluffo-Ordóñez

  3. Modeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Ben Guerir, Morocco

    Diego H. Peluffo-Ordóñez

  4. North Technical University, Ibarra, Ecuador

    Roberth A. Araujo

Authors
  1. Leandro L. Lorente-Leyva
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  2. M. M. E. Alemany
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  3. Diego H. Peluffo-Ordóñez
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  4. Roberth A. Araujo
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Corresponding author

Correspondence to Leandro L. Lorente-Leyva .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

  3. Nanyang Technological University, Singapore, Singapore

    Dusit Niyato

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Lorente-Leyva, L.L., Alemany, M.M.E., Peluffo-Ordóñez, D.H., Araujo, R.A. (2021). Demand Forecasting for Textile Products Using Statistical Analysis and Machine Learning Algorithms. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_15

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  • DOI: https://doi.org/10.1007/978-3-030-73280-6_15

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-73280-6

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