Implementation of The Seasonal Autoregressive Integrated Moving Average Predictive Model on Raw Material Usage Data at PT. Plastik Karawang Flexindo - Alfiansyah, Muhammad Rindra; Tukino, Tukino; Hananto, Agustia; Novalia, Elfina (Malikussaleh University, Aceh, Indonesia, 2025)
Fluctuations in raw material utilization in the manufacturing industry significantly impact production process efficiency, operational costs, and supply chain stability. Inaccurate planning and management of raw material inventories can lead to two extreme conditions: excess stock, which increases storage costs and the risk of expiration, or stock shortages, which could halt the production process and reduce productivity. To improve the accuracy of raw material consumption planning, this study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict raw material needs periodically based on historical data. The dataset used includes the consumption of Polyethylene (PE), High Density Polyethylene (HDPE), and Polypropylene (PP) from 2019 to 2025. The data is analyzed using a time series forecasting approach to identify trends and seasonal patterns. The SARIMA model is developed and optimized using three methods to search for the best parameters: Grid Search, Random Search, and Bayesian Optimization, to enhance prediction performance. The model's evaluation calculates the Mean Absolute Percentage Error (MAPE) as an accuracy indicator. The evaluation results show that although SARIMA can recognize seasonal patterns in raw material consumption, the prediction accuracy varies, with the best MAPE value being 16% and the highest being 34%. This indicates that external factors, such as market dynamics, government policies, global price fluctuations, and internal variables such as production schedules and customer demand, need to be considered to improve the model's precision. Overall, the application of SARIMA in this context provides a strategic contribution to supply chain management in the manufacturing industry, particularly in anticipating raw material needs, reducing uncertainty, and supporting more efficient and adaptive data-driven decision-making.