Telukdarie A., Munsamy M., Katsumbe T., Maphisa X. (2024). Smart value chain tool advancing sustainability in the FoodBev manufacturing industry. Journal of Cleaner Production, 15/02/2024, vol. 441, p. 140871.
https://doi.org/10.1016/j.jclepro.2024.140871
https://doi.org/10.1016/j.jclepro.2024.140871
Titre : | Smart value chain tool advancing sustainability in the FoodBev manufacturing industry (2024) |
Auteurs : | A. Telukdarie ; M. Munsamy ; T. Katsumbe ; X. Maphisa |
Type de document : | Article |
Dans : | Journal of Cleaner Production (vol. 441, February 2024) |
Article en page(s) : | p. 140871 |
Langues : | Anglais |
Langues du résumé : | Anglais |
Catégories : |
Catégories principales 10 - INDUSTRIES ; 10.2 - IAA (en général)Thésaurus IAMM INDUSTRIE ALIMENTAIRE ; DURABILITE ; CHAINE DE VALEUR ; ECONOMIE CIRCULAIRE ; ALIMENTATION HUMAINE ; BOISSON ; TECHNOLOGIE DE L'INFORMATION ET DE LA COMMUNICATION |
Résumé : | The world is evolving towards sustainability and optimum resource management, with a rapid evolution towards a circular economy (CE). The focus on sustainability in the food and beverage (FoodBev) sector is a priority due to the close association and dependency on natural resources. The evolution of digitalization has accelerated sustainable development of the FoodBev sector and helped preserve sectorial natural resources dependencies. Digital skills are a key component of sustainable development, as skills are used to identify and implement sustainable solutions. Digitalization and the CE in the FoodBev sector are considered critical solutions for food waste and food loss, and potential drivers of sustainability. This study seeks to deploy a smart value chain tool for food waste and food loss management, and a digital mechanism for the sustainability of the FoodBev sector. The research team designs, develops, and demonstrates a python-enabled digital toolset, configured to construct a specific global search for sustainable alternates for waste and other streams of the FoodBev industry. The toolset adopts a combination of Artificial Intelligence techniques including data analytics and Natural Language Processing. The results provide for rapid technological alternatives identification for the sector. The paper provides technological alternatives for 18 FoodBev value streams. |
Cote : | En ligne |
URL / DOI : | https://doi.org/10.1016/j.jclepro.2024.140871 |