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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S2214716019301988
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:oprepe:v:7:y:2020:i:c:s2214716019301988

DOI: 10.1016/j.orp.2020.100147

Access Statistics for this article

Operations Research Perspectives is currently edited by Rubén Ruiz Garcia

More articles in Operations Research Perspectives from Elsevier
Bibliographic data for series maintained by Catherine Liu ().