A new age of innovation in the hospitality sector is being ushered in by the confluence of deep learning (DL) and the internet of things (IoT), which offers a comprehensive solution to sustainability issues. Due in large part to its complex supply networks and high resource consumption, the hospitality sector contributes significantly to food waste and environmental damage. In order to provide a more sustainable and socially responsible approach to waste management, this chapter explores how the combination of DL and IoT systems might play a crucial role in it. DL algorithms are used to optimize a number of areas of hospitality operations because to their capacity to evaluate massive datasets. These models are used in the food service industry for customized menu improvement, inventory control and demand forecasting. These technologies can help to achieve SDGs 1 and 2 by creating ecologically and socially responsible hospitality industries through the promotion of sustainable consumption patterns and effective waste management.
Top1. Introduction
Despite advances in technology, there is a troubling rise in food waste as a result of intricate cuisines and culinary practices (Barrett et al., 2023). Even while cutting down on food waste is an important sustainable development aim, the numbers that are available today show that there is a lot of food wasted (Labadi et al., 2021). Food waste has been the subject of much research, especially in the hotel industry where it accounts for 12% of all waste (Araujo, 2023). Many causes of food waste have been investigated; however, the studies have a tendency to be disjointed and lack a thorough methodology (Shams et al., 2023). The previous research has mostly concentrated on industrialized nations, ignoring the more significant problem in developing economies (NaThalang, 2019). Research about food waste in Taiwanese hotels is scarce (Fitchuk, 2021). Adopting efficient waste management techniques may lower expenses, enhance brand recognition, and show a dedication to environmental sustainability for both hoteliers and culinary managers (Sartor, 2021).
When DL and IoT are combined, smart decision-making is made possible and it helps hospitality industries to adapt their operations in real-time to meet demand and avoid waste (Voorhoeve, 2021). In line with SDGs 1 and 2, this chapter explores the societal effect of these technologies (Singh, 2023). The cost reductions in the hospitality sector are facilitated by DL and IoT by improving operational efficiency and cutting waste (Hunter et al., 2020). In line with SDG 1's objective of ending poverty, these savings might be put toward community development, social projects and programs that reduce poverty (Bisaria, 2022). Within the framework of Sustainable Development Goal 2, attaining zero hunger is directly impacted by the decrease in food waste using DL and Internet of Things applications (Singh, 2023). In order to alleviate food insecurity and encourage sustainable consumption habits, this chapter highlights how smart technologies make it possible for resources to be distributed more fairly (Atser et al., 2019).
1.1 Background of Study
The advent of complex cuisines and technical advancements in culinary arts has made food acquisition easier in recent years (Sharma & Singh, 2022). There has been a worrying increase in waste to go along with this greater convenience in gratifying gastronomic cravings (Tice, 2024). This waste encompasses less obvious forms in addition to obvious kitchen waste (Toivonen, 2020). The European Union has included the reduction of food waste as a critical sustainable development target in its 2030 agenda, acknowledging the significance of tackling this issue (Singh, 2022). Collaboration between institutions, the public and governments is necessary to accomplish this goal (Miconi, 2022). As part of proactive measures, the Pacific Asia Travel Association in Asia has started regional campaigns to decrease food waste with the purpose of increasing public awareness of the larger objectives of environmental sustainability in the travel and tourist industry (Vind, 2023).