Application of machine learning to support production planning of a food industry in the context of waste generation under uncertainty
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Knowledge AreaTecnología de los Alimentos
SponsorsEloy Hontoria is grateful to Project RTI2018-099139-B-C21 financed by FEDER/Ministerio de Ciencia e Innovación-Agencia Estatal de Investigación. Alberto Garre (20900/PD/18) is grateful to the Seneca foundation for awarding him a post-doctoral grant.
Bibliographic CitationGarre, A., Ruiz, M. C., & Hontoria, E. (2020). Application of machine learning to support production planning of a food industry in the context of waste generation under uncertainty. Operations Research Perspectives, 7 doi:10.1016/j.orp.2020.100147
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. A food company that produces liquid products based on fruits and vegetables was analyzed. Data was gathered on 1,795 batches, including the characteristics of the product (recipe, components used…) and the difference between the input and the output weight. Machine Learning (ML) algorithms were used to predict deviations in production, reducing uncertainties related to the amount of ...
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