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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 and Secondi, Luca and Marchetti, Stefano and Eriksson, Mattias (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. Socio-Economic Planning Sciences. 82 :Part A , 101041
[Research article]

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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.

Authors/Creators:Malefors, Christopher and Secondi, Luca and Marchetti, Stefano and Eriksson, Mattias
Title: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
Series Name/Journal:Socio-Economic Planning Sciences
Year of publishing :2022
Volume:82
Number:Part A
Article number:101041
Number of Pages:11
ISSN:0038-0121
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 102 Computer and Information Science > Information Science
(A) Swedish standard research categories 2011 > 5 Social Sciences > 502 Economics and Business > Business Administration
(A) Swedish standard research categories 2011 > 1 Natural sciences > 107 Other Natural Sciences > Other Natural Sciences not elsewhere specified
Keywords:Food waste school kitchens forecasting random-forest system optimization
URN:NBN:urn:nbn:se:slu:epsilon-p-110958
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-110958
Additional ID:
Type of IDID
DOI10.1016/j.seps.2021.101041
Web of Science (WoS)000833547800003
Scopus2-s2.0-85102141121
ID Code:28757
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Energy and Technology
Deposited By: SLUpub Connector
Deposited On:02 Sep 2022 13:02
Metadata Last Modified:02 Sep 2022 13:11

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