<![CDATA[Seafood based culinary businesses face significant challenges in inventory management due to fluctuating and unpredictable customer demand. Inaccurate forecasting can lead to either excess stock, resulting in waste and increased storage costs, or stock shortages, which disrupt production and reduce customer satisfaction. Among the various raw materials used, crab is the most critical, accounting for 80.6% of total raw material demand, making it essential to forecast its demand accurately to maintain operational efficiency and avoid disruptions. This study addresses the inventory problem by applying two time series forecasting methods SARIMA and Prophet to predict weekly crab demand. The performance of both models was evaluated using RMSE and MAE to assess the accuracy and reliability of their predictions over time. The SARIMA model with parameters (2,1,10)(0,1,2)[12] achieved the best forecasting performance, with an RMSE of 1.32752 and MAE of 1.24207, clearly outperforming the Prophet model, which recorded an RMSE of 1.4623 and MAE of 1.3506. These results demonstrate that SARIMA is more effective in capturing seasonal patterns and demand trends in crab usage data. In conclusion, the SARIMA model offers more precise and reliable forecasts, making it a more suitable tool for supporting raw material inventory decision-making in seafood culinary businesses, particularly when dealing with high-demand ingredients such as crab.]]>
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