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Machine Learning Tool for Yield Maximization in Cream Cheese Production

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Innovative Intelligent Industrial Production and Logistics (IN4PL 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2372))

  • 8 Accesses

Abstract

Artificial intelligence tools and data collection on the shop floor are enhancing flexibility and productivity in industry, addressing labor shortages and skills attrition by leveraging the tacit knowledge of workers. This study focuses on the cream cheese production sector, where operator expertise is essential for controlling the ultrafiltration concentration factor, a critical parameter affecting product moisture content. To ensure continuous and flexible production despite workforce challenges, a machine-learning tool was developed using the CRISP-DM approach to maximize cream cheese yield on a Canadian production line. A decision tree algorithm applied to real production and quality data yielded promising results, with an RMSE of 0.061 and an R\(^2\) of 0.91 when predicting the ultrafiltration concentration factor used by an experienced operator to maximize yield while complying with quality standards. The implementation saw positive operator acceptance due to comprehensive training and an inclusive approach. This research marks a pioneering effort to harness tacit knowledge in the dairy industry for machine parameter control, highlighting data acquisition and quality as key areas for further investigation to enhance tool performance and adaptability.

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Acknowledgments

We would like to thank our industrial partner for providing us with the data and the expert knowledge as well as MITACS Stage de stratégie d’entreprise (IT36475) for the support.

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Authors and Affiliations

  1. Laboratoire en Intelligence des Données, Montréal, (QC), Canada

    Loïc Parrenin & Bruno Agard

  2. Département de Mathématiques et de Génie Industriel, École Polytechnique de Montréal, Montréal, (QC), Canada

    Loïc Parrenin, Christophe Danjou & Bruno Agard

  3. Département de Génie Industriel, Université du Québec à Trois-Rivières, Trois-Rivières, (QC), Canada

    Ambre Dupuis

Authors
  1. Loïc Parrenin
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Corresponding author

Correspondence to Ambre Dupuis .

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Editors and Affiliations

  1. Polytechnical University of Bari, Bari, Italy

    Michele Dassisti

  2. University of Paris-EST Créteil, Créteil, France

    Kurosh Madani

  3. University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

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Parrenin, L., Dupuis, A., Danjou, C., Agard, B. (2025). Machine Learning Tool for Yield Maximization in Cream Cheese Production. In: Dassisti, M., Madani, K., Panetto, H. (eds) Innovative Intelligent Industrial Production and Logistics. IN4PL 2024. Communications in Computer and Information Science, vol 2372. Springer, Cham. https://doi.org/10.1007/978-3-031-80760-2_6

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  • DOI: https://doi.org/10.1007/978-3-031-80760-2_6

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