Implementation of Sugeno Fuzzy Logic Methods for Predicting Pie Crust Raw Material Stock

Abstract

Accurate prediction of raw material stocks is essential for cost management and effective production planning in the food industry. The Sugeno fuzzy logic method is employed to predict the stock levels of pie leather raw materials. This method aims to offer a reliable prediction system that enhances stock management, thereby minimizing the risks associated with overstocking or stock shortages. The performance of the model is evaluated using the average error percentage test, which yielded a result of 3.94%. This indicates an accuracy level of 96.06%, demonstrating a high degree of precision. The findings suggest that the Sugeno fuzzy logic method is a highly effective tool for predicting raw material requirements in the pie leather production process. The study underscores the potential of fuzzy logic methods in supply management, ensuring smooth production operations. By implementing this method, manufacturers can achieve better inventory control, leading to more efficient production planning and cost savings. The results validate the application of Sugeno fuzzy logic as a robust approach for inventory prediction, capable of significantly improving the overall management of raw material stocks in the food industry. This research highlights the practical benefits of advanced predictive models in optimizing supply chains, supporting continuous production flow, and enhancing the overall efficiency of production systems. Consequently, the use of fuzzy logic methods can play a critical role in streamlining production processes and maintaining optimal inventory levels, ultimately contributing to the success and sustainability of food manufacturing operations.