IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v6y2024i4p54-1097d1525237.html
   My bibliography  Save this article

Forecasting Raw Material Yield in the Tanning Industry: A Machine Learning Approach

Author

Listed:
  • Ismael Cristofer Baierle

    (Agroindustrial Systems and Processes Graduate Program, Universidade Federal do Rio Grande, FURG, Cel. Francisco Borges de Lima, 3005, Santo Antônio da Patrulha 95500-000, Brazil)

  • Leandro Haupt

    (Industrial Processes and Systems Engineering Graduate Program, Universidade de Santa Cruz do Sul, UNISC, Av. Independência, 2293—Universitário, Santa Cruz do Sul 96815-900, Brazil)

  • João Carlos Furtado

    (Industrial Processes and Systems Engineering Graduate Program, Universidade de Santa Cruz do Sul, UNISC, Av. Independência, 2293—Universitário, Santa Cruz do Sul 96815-900, Brazil)

  • Eluza Toledo Pinheiro

    (Production and Systems Engineering Graduate Program, Universidade do Vale do Rio dos Sinos, UNISINOS, Av. Unisinos, 950—Cristo Rei, São Leopoldo 93022-000, Brazil)

  • Miguel Afonso Sellitto

    (Production and Systems Engineering Graduate Program, Universidade do Vale do Rio dos Sinos, UNISINOS, Av. Unisinos, 950—Cristo Rei, São Leopoldo 93022-000, Brazil)

Abstract

This study presents an innovative machine learning (ML) approach to predicting raw material yield in the leather tanning industry, addressing a critical challenge in production efficiency. Conducted at a tannery in southern Brazil, the research leverages historical production data to develop a predictive model. The methodology encompasses four key stages: data collection, processing, prediction, and evaluation. After rigorous analysis and refinement, the dataset was reduced from 16,046 to 555 high-quality records. Eight ML models were implemented and evaluated using Orange Data Mining software, version 3.38.0, including advanced algorithms such as Random Forest, Gradient Boosting, and neural networks. Model performance was assessed through cross-validation and comprehensive metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Coefficient of Determination (R 2 ). The AdaBoost algorithm emerged as the most accurate predictor, achieving impressive results with an MAE of 0.042, MSE of 0.003, RMSE of 0.057, and R 2 of 0.331. This research demonstrates the significant potential of ML techniques in enhancing raw material yield forecasting within the tanning industry. The findings contribute to more efficient forecasting processes, aligning with Industry 4.0 principles and paving the way for data-driven decision-making in manufacturing.

Suggested Citation

  • Ismael Cristofer Baierle & Leandro Haupt & João Carlos Furtado & Eluza Toledo Pinheiro & Miguel Afonso Sellitto, 2024. "Forecasting Raw Material Yield in the Tanning Industry: A Machine Learning Approach," Forecasting, MDPI, vol. 6(4), pages 1-20, November.
  • Handle: RePEc:gam:jforec:v:6:y:2024:i:4:p:54-1097:d:1525237
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/6/4/54/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2571-9394/6/4/54/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jforec:v:6:y:2024:i:4:p:54-1097:d:1525237. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.