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

Alberto Garre, Mari Carmen Ruiz and Eloy Hontoria

Operations Research Perspectives, 2020, vol. 7, issue C

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.

Keywords: Output uncertainty; Waste management; Empirical study; Production planning; Sustainability (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:oprepe:v:7:y:2020:i:c:s2214716019301988

DOI: 10.1016/j.orp.2020.100147

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