Monte Carlo Simulation for Seasonal Stock Prediction of Seasoning at AH FOOD - Nurhalizah, Ammanda Putri; Ariesta, Atik (Jurnal Algoritma, 2025)
AH FOOD is a business operating in the food industry, selling various seasoning products such as balado seasoning, balado chili sauce, Miwon, and others. The problem faced involves seasoning products that have specific characteristics, such as a limited shelf life and dependence on the availability of raw materials. This availability is often influenced by general market conditions, weather or seasons, and raw material prices. Therefore, this study aims to predict sales stock at AH FOOD based on Indonesia's seasons, namely the rainy and dry seasons. The method used in this research to predict stock is the Monte Carlo method. This method was chosen due to its ability to handle uncertainty and seasonal variability, making it superior to other methods such as time series regression in predicting seasonal stock. The Mean Absolute Percentage Error (MAPE) was used to measure the accuracy level of the prediction simulation. The results of the accuracy using MAPE showed that the Monte Carlo method is adequate and feasible to use, with an average error value of 26% for the rainy season and 27% for the dry season. This helps AH FOOD optimize stock management, reduce losses due to product expiration, and increase storage efficiency. Based on the MAPE results, the Monte Carlo method is effectively used to predict seasoning stock sales at AH FOOD based on Indonesia's seasonal divisions.