In recent years, machine learning has become increasingly important in the catering industry, and the utilization of this technology is witnessed in a variety of aspects. The article discusses machine learning in the catering industry in detail, especially the innovations, issues, opportunities, and challenges. This chapter contributes insights into the impact of machine learning on the industry and provides suggestions on how the industry may face the challenges of using machine learning. Future research directions are also mentioned.
TopIntroduction
Restaurants provide diverse choices, delicious dishes, and enjoyable environments compared to home cooking. Enjoying foods from restaurants has become a lifestyle, and the catering industry has developed rapidly. In the past, people played the most indispensable roles in the catering industry because of its service nature. Giving customers a satisfying experience is essential in services. However, machine learning, which simulates the human behavior of acquiring knowledge to allow machines to think and act like humans, is now providing a new way to fill these service roles (Jordan & Mitchell, 2015; Wang et al., 2017).
Robots empowered by machine learning can take orders, make recommendations, process foods, collect payments, and even deliver takeaways (Jang & Lee, 2020). Figure 1 shows how machine learning can play a role in various tasks in the catering industry. Customers arriving at a restaurant can place orders with a robot server. After receiving the order, robot cooks can make dishes catering to customers’ requests, and when the food is ready, the robot server takes it to the customers’ table. When the customers finish, the robot server can process the bill with cash, credit card, or mobile payment. After the customers leave, the customer information and transaction data are recorded to a database and analyzed for recommendations that meet customers’ personal preferences the next time.
Figure 1. Machine learning can play a role in various tasks in the catering industry
This chapter discusses machine learning in the catering industry in detail. In the next section, the authors describe the background and review relevant research. Then, the authors discuss the opportunities and challenges that machine learning brings to the catering industry, as well as solutions and recommendations to the issues. Finally, the authors conclude the chapter.
TopBackground
The literature on machine learning has surged in recent years. Jordan and Mitchell (2015) introduce machine learning and its applications. They discuss what machine learning is, which areas it relates to, and the value of machine learning in various industries, including healthcare, education, manufacturing, service, finance, and marketing.
Notably, the application of machine learning to the catering industry is widely documented in the literature. In China, prestigious hotpot restaurants like Haidilao use robots with machine learning to replace human staff (Zheng, 2021); in Korea, LG Electronics design robots that can be utilized to carry and deliver foods to customers (Cho, 2020); and in Thailand, five types of restaurant service robots (Order One, Order Two, Serve One, Serve Two, and Slim) work in MK Company’s restaurants (Eksiri & Kimura, 2015). With the advances of machine learning, an increasing number of catering companies use this technology to better anticipate the demand from customers (Hess et al., 2021). For example, Yu and Fu (2020) deploy machine learning models to predict the demand for Japanese food based on questionnaires about dietary habits and personal information. Machine learning is also adopted to predict the degree of processing for food (Menichetti et al., 2021) and wine quality (Dahal et al., 2020).