Implementation of The Seasonal Autoregressive Integrated Moving Average Predictive Model on Raw Material Usage Data at PT. Plastik Karawang Flexindo

Muhammad Rindra Alfiansyah, Tukino Tukino, Agustia Hananto, Elfina Novalia

Abstract


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.


Keywords


Forecasting, SARIMA, Overstock, Stockout, Optimization

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References


H. Zhao, X. Zuo, and P. Lin, “Sales forecasting for chemical products by using SARIMA model,” in Proceedings of the 5th International Conference on Big Data and Education, 2022, pp. 419–427.

I. C. Baierle, L. Haupt, J. C. Furtado, E. T. Pinheiro, and M. A. Sellitto, “Forecasting Raw Material Yield in the Tanning Industry: A Machine Learning Approach,” Forecasting, vol. 6, no. 4, pp. 1078–1097, 2024.

R. V. Martono, Manajemen Logistik (Edisi Revisi). Gramedia Pustaka Utama, 2023.

C. R. Bhat, B. Prabha, C. Donald, S. Sah, and H. Patil, “SARIMA Techniques for Predictive Resource Provisioning in Cloud Environments,” in 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS), IEEE, 2023, pp. 1–5.

S. Chaturvedi, E. Rajasekar, S. Natarajan, and N. McCullen, “A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India,” Energy Policy, vol. 168, p. 113097, 2022.

F. R. Alharbi and D. Csala, “A seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) forecasting model-based time series approach,” Inventions, vol. 7, no. 4, p. 94, 2022.

R. J. Hyndman and G. Athanasopoulos, “Forecasting: Principles and practice (Third print edition),” Otexts Online Open-Access Textbooks, 2021.

S. Suraya and M. Sholeh, “Designing and implementing a database for thesis data management by using the python Flask Framework,” International Journal of Engineering, Science and Information Technology, vol. 2, no. 1, pp. 9–14, 2022.

C. R. Gunawan, N. Nurdin, and F. Fajriana, “Design of A Real-Time Object Detection Prototype System with YOLOv3 (You Only Look Once),” Int. J. Eng. Sci. Inf. Technol, vol. 2, no. 3, pp. 96–99, 2022.

A. Khang, V. Shah, and S. Rani, Handbook of Research on AI-Based Technologies and Applications in the Era of the Metaverse. IGI Global, 2023.

J. W. Creswell and J. Creswell, Research design. Sage publications Thousand Oaks, CA, 2003.

E. DePoy, Introduction to research-e-book: Understanding and applying multiple strategies. Elsevier Health Sciences, 2024.

D. Ivanov, Introduction to supply chain resilience: Management, modelling, technology. Springer Nature, 2021.

A. Kumar and S. G. Praveenakumar, Research methodology. Authors Click Publishing, 2025.

J. Mingers and L. P. Willcocks, The semiotics of information systems: a research methodology for the digital age. Springer Nature, 2023.

M. P. Stevens, “Polymer chemistry: an introduction,” , 1999.

J. A. Brydson, Plastics materials. Elsevier, 1999.

F. W. Billmeyer, Textbook of polymer science. John Wiley & Sons, 1984.

T. Tukino, S. S. Hilabi, and H. Romadhon, “Production RAW Material Inventory Control Information System at PT. SIIX EMS Indonesia,” Buana Information Technology and Computer Science, vol. 1, no. 1, pp. 8–11, 2020.

T. Falatouri, F. Darbanian, P. Brandtner, and C. Udokwu, “Predictive analytics for demand forecasting–a comparison of SARIMA and LSTM in retail SCM,” Procedia Computer Science, vol. 200, pp. 993–1003, 2022.

A. Joshi, M. Khosravy, and N. Gupta, “Machine learning for predictive analysis,” Proceedings of ICTIS, 2020.

M. Maharina et al., “Machine Learning Models for Predicting Flood Events Using Weather Data: An Evaluation of Logistic Regression, LightGBM, and XGBoost,” Journal of Applied Data Sciences, vol. 6, no. 1, pp. 496–507, 2025.




DOI: https://doi.org/10.52088/ijesty.v5i3.867

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International Journal of Engineering, Science, and Information Technology (IJESTY) eISSN 2775-2674