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Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty

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  • Garre, Alberto
  • Ruiz, Mari Carmen
  • Hontoria, Eloy

Abstract

Food production is a complex process where uncertainty is very relevant (e.g. stochastic yield and demand, variability in raw materials and ingredients…), resulting in differences between planned production and actual output. These discrepancies have an economic cost for the company (e.g. waste disposal), as well as an environmental impact (food waste and increased carbon footprint). This research aims to develop tools based on data analytics to predict the magnitude of these discrepancies, improving enterprise profitability while, at the same time, reducing environmental impact aiding food waste management.

Suggested Citation

  • Garre, Alberto & Ruiz, Mari Carmen & Hontoria, Eloy, 2020. "Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty," Operations Research Perspectives, Elsevier, vol. 7(C).
    • Handle: RePEc:eee:oprepe:v:7:y:2020:i:c:s2214716019301988
    DOI: 10.1016/j.orp.2020.100147
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    2. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    3. Florian Rösler & Judith Kreyenschmidt & Guido Ritter, 2021. "Recommendation of Good Practice in the Food-Processing Industry for Preventing and Handling Food Loss and Waste," Sustainability, MDPI, vol. 13(17), pages 1-30, August.
    4. Helen Onyeaka & Phemelo Tamasiga & Uju Mary Nwauzoma & Taghi Miri & Uche Chioma Juliet & Ogueri Nwaiwu & Adenike A. Akinsemolu, 2023. "Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    5. Sahar Ahmadzadeh & Tahmina Ajmal & Ramakrishnan Ramanathan & Yanqing Duan, 2023. "A Comprehensive Review on Food Waste Reduction Based on IoT and Big Data Technologies," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    6. Jelena Lonska & Anda Zvaigzne & Inta Kotane & Inese Silicka & Lienite Litavniece & Sergejs Kodors & Juta Deksne & Aija Vonoga, 2022. "Plate Waste in School Catering in Rezekne, Latvia," Sustainability, MDPI, vol. 14(7), pages 1-26, March.
    7. Sebatjane, Makoena, 2022. "The impact of preservation technology investments on lot-sizing and shipment strategies in a three-echelon food supply chain involving growing and deteriorating items," Operations Research Perspectives, Elsevier, vol. 9(C).
    8. Mercedes Luque‐Vílchez & José A. Gómez‐Limón & M. Dolores Guerrero‐Baena & Pablo Rodríguez‐Gutiérrez, 2023. "Deconstructing corporate environmental, social, and governance performance: Heterogeneous stakeholder preferences in the food industry," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(3), pages 1845-1860, June.

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