Fuzzy Logic in Gastronomy 4.0 Applications in Food and Beverage Industry

Industry 4.0 is a transformation process that increases efficiency and flexibility through automation, real-time data analysis, and digital integration in production processes. This approach has led to the emergence of a new phenomenon called Gastronomy 4.0, especially in the food and beverage sector. Gastronomy 4.0 covers the use of digital technologies such as artificial intelligence, machine learning, data analytics, and automation in food production, kitchen management, service processes, and consumer experiences. However, since there are uncertainties and complex variables in these processes, the fuzzy logic method stands out as an effective tool that facilitates decisionmaking in uncertain situations. Unlike classical logic, fuzzy logic can process partially correct or incorrect results by allowing degrees of accuracy. With this feature, it enables flexible and dynamic decision-making processes in Gastronomy 4.0 applications. The study identified seven basic areas where the fuzzy logic method can be applied within the scope of Gastronomy 4.0. The first of these is the optimization of cooking processes. The second area is the precise determination of cooking degrees and times. The third application area can be shown as the adjustment of ingredient amounts in recipes. The fourth area is personalized recipe development processes for individual preferences. The fifth application point is the organization and optimization of flavor and taste profiles. The sixth area is the development of menu planning and pricing strategies. Finally, the seventh application area is determined as inventory management and supply chain optimization. The results of this study show that the integration of the fuzzy logic approach into Gastronomy 4.0 processes will provide significant advantages in kitchen operations. In particular, the main contributions expected are that decision-making mechanisms will become more sensitive, flexible, and efficient. It is anticipated that significant improvements such as improving quality control in food production processes, developing personalized services, and optimizing resource management will be possible thanks to this integration.