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Research article - Peer-reviewed, 2022

Food waste reduction and economic savings in times of crisis: The potential of machine learning methods to plan guest attendance in Swedish public catering during the Covid-19 pandemic

Malefors, Christopher; Secondi, Luca; Marchetti, Stefano; Eriksson, Mattias

Abstract

Food waste is a significant problem within public catering establishments in any normal situation. During spring 2020 the Covid-19 pandemic placed the public catering system under greater pressure, revealing weaknesses within the system and generation of food waste due to rapidly changing consumption patterns. In times of crisis, it is especially important to conserve resources and allocate existing resources to areas where they can be of most use, but this poses significant challenges. This study evaluated the potential of a forecasting model to predict guest attendance during the start and throughout the pandemic. This was done by collecting data on guest attendance in Swedish school and preschool catering establishments before and during the pandemic, and using a machine learning approach to predict future guest attendance based on historical data. Comparison of various learning methods revealed that random forest produced more accurate forecasts than a simple artificial neural network, with conditional mean absolute prediction error of <0.15 for the trained dataset. Economic savings were obtained by forecasting compared with a no-plan scenario, supporting selection of the random forest approach for effective forecasting of meal planning. Overall, the results obtained using forecasting models for meal planning in times of crisis confirmed their usefulness. Continuous use can improve estimates for the test period, due to the agile and flexible nature of these models. This is particularly important when guest attendance is unpredictable, so that production planning can be optimized to reduce food waste and contribute to a more sustainable and resilient food system.

Keywords

Food waste school kitchens forecasting random-forest system optimization

Published in

Socio-Economic Planning Sciences
2022, volume: 82, number: Part A, article number: 101041

Authors' information

Swedish University of Agricultural Sciences, Department of Energy and Technology
Secondi, Luca
University of Tuscia
Marchetti, Stefano
University of Pisa
Swedish University of Agricultural Sciences, Department of Energy and Technology

Associated SLU-program

Food Waste

Sustainable Development Goals

SDG12 Ensure sustainable consumption and production patterns

UKÄ Subject classification

Other Natural Sciences not elsewhere specified
Business Administration
Information Science

Publication Identifiers

DOI: https://doi.org/10.1016/j.seps.2021.101041

URI (permanent link to this page)

https://res.slu.se/id/publ/110958