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Reducing food waste is essential if we want to create a more sustainable food system, which is why halving per capita food waste by 2030 has been included in the UN Sustainable Development Goals. The main aim of this paper is to demonstrate that by using digital tools to monitor food provisioning and management within canteens, it is possible to achieve a more sustainable food service management within industrial companies and to reduce the overall quantity and environmental impacts of food preparation and consumption thus achieving economic benefits. Longitudinal 2018 and 2019 data collected from the canteen of a major Italian food production company were analyzed. The results showed that the amount of food lost and wasted has decreased over time, reaping important environmental benefits and illustrating specific differences between the food lost in meal preparation and the food left on employees' plates. It is therefore important to implement education initiatives and to use digital tools to share food‐related data collected within companies among both employees and kitchen and catering staff in order to raise awareness of their behavioral strengths and weaknesses. From this perspective, new interventions at both kitchen and customer/worker levels are introduced and discussed. [ABSTRACT FROM AUTHOR]

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. • A machine learning approach can help kitchens to predict guest attendance. • Forecast of demand during extreme events is hard. • There is a savings potential compared to a business as usual scenario. [ABSTRACT FROM AUTHOR]

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. • A machine learning approach can help kitchens to predict guest attendance. • Forecast of demand during extreme events is hard. • There is a savings potential compared to a business as usual scenario. [ABSTRACT FROM AUTHOR]