Machine learning models for short-term demand forecasting in food catering services: A solution to reduce food waste

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Rodrigues, M (Author) Other; Vera L. Miguéis (Author) FEUP; Freitas, S (Author) Other; Machado, T (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: JOURNAL OF CLEANER PRODUCTION

Vol. 435

ISSN: 0959-6526

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Z-RMA

DOI: 10.1016/j.jclepro.2023.140265

Abstract (EN): Food waste is responsible for severe environmental, social, and economic issues and therefore it is imperative to prevent or at least minimize its generation. The main cause of food waste is poor demand forecasting and so it is essential to improve the accuracy of the tools tasked with these forecasts. The present work proposes four models meant to help food catering services predict food demand accurately and thus avoid overproducing or underproducing. Each model is based on a different machine learning technique. Two baseline models are also proposed to mimic how food catering services estimate future demand and to infer the added value of employing machine learning in this context. To verify the impact of the proposed models, they were tested on data from the three different canteens chosen as case studies. The results show that the models based on the random forest algorithm and the long short-term memory neural network produced the best forecasts, which would lead to a 14% to 52% reduction in the number of wasted meals. Furthermore, by basing their decisions on these forecasts, the food catering services would be able to reduce unmet demand by 3% to 16% when compared with the forecasts of the baseline models. Thus, employing machine learning to forecast future demand can be very beneficial to food catering services. These forecasts can increase the service level of food services and reduce food waste, mitigating its environmental, social, and economic consequences.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16