The Use of Machine Learning Algorithms for Supply Chain Optimization at PT. XYZ

Diomen Syahputra Manik; Nazaruddin Matondang; Nismah Panjaitan

doi:10.32734/jsti.v28i1.22207

Authors

Diomen Syahputra Manik Universitas Negeri Medan
Nazaruddin Matondang Universitas Sumatera Utara
Nismah Panjaitan

DOI:

https://doi.org/10.32734/jsti.v28i1.22207

Keywords:

demand forecasting, machine learning, random forest, prophet, gradient boosting regressor

Abstract

Increased demand fluctuations pose a major challenge in supply chain management, particularly in the fast-food beverage industry like PT. XYZ. This research aims to build and evaluate a demand forecasting model based on machine learning, considering multivariate variables such as product price, seasonal trends, weather, per capita income, population, and historical sales data. The three algorithms used are Random Forest Regressor, Gradient Boosting Regressor, and Prophet Time Series Model. This research method employs a quantitative approach with descriptive-predictive analysis based on time-series data. Model evaluation was conducted using MAE, MSE, RMSE, and MAPE metrics. The research results indicate that Prophet has the highest accuracy (MAPE: 2.33%) and excels in capturing seasonal trends, while Random Forest ranks second (MAPE: 2.47%) with an advantage in comprehensively handling multivariate variables. Gradient Boosting yields the lowest accuracy (MAPE: 2.70%). The conclusion of this study recommends the use of Prophet for short-term seasonal-based predictions, while Random Forest is more suitable for medium to long-term strategic planning. The combination of the two has the potential to become an accurate and adaptive hybrid approach for optimizing the demand forecasting system at PT. XYZ.

Downloads

Download data is not yet available.

References

[1] L. Li, T. Chi, T. Hao, and T. Yu, “Customer Demand Analysis Of The Electronic Commerce Supply Chain Using Big Data,” Ann. Oper. Res., vol. 268, pp. 113–128, 2018.

[2] T. Hines, Supply Chain Strategies: Demand Driven and Customer Focused. Routledge, 2024.

[3] C. W. Chase, Demand-driven Forecasting: A Structured Approach To Forecasting. John Wiley & Sons, 2013.

[4] W. Albrecht and M. Steinrücke, “Coordinating Continuous-time Distribution and Sales Planning of Perishable Goods with Quality Grades,” Int. J. Prod. Res., vol. 56, no. 7, pp. 2646–2665, 2018.

[5] S. M. Akid, A. Ishak, and S. Sinulingga, “Jurnal Sistem Teknik Industri The Design Of A Demand Forecasting Model Of Glass Bottled Tea Products With Machine Learning Approach,” Jurnal Sistem Teknik Industri, vol. 27, no. 2, pp. 57–65, 2025.

[6] B. Keleş, P. Gómez-Acevedo, and N. I. Shaikh, “The Impact of Systematic Changes in Weather on The Supply and Demand of Beverages,” Int. J. Prod. Econ., vol. 195, pp. 186–197, 2018.

[7] E. Beard, J. Brown, R. West, E. Kaner, P. Meier, and S. Michie, “Associations Between Socio-economic Factors and Alcohol Consumption: A Population Survey of Adults in England,” PLoS One, vol. 14, no. 2, p. 209, 2019.

[8] N. Fose, A. R. Singh, S. Krishnamurthy, M. Ratshitanga, and P. Moodley, “Empowering Distribution System Operators: A Review of Distributed Energy Resource Forecasting Techniques,” Heliyon, vol. 10, no. 15, 2024.

[9] P. K. Tripathi, C. K. Singh, R. Singh, and A. K. Deshmukh, “A farmer-centric agricultural decision support system for market dynamics in a volatile agricultural supply chain,” Benchmarking: An International Journal, vol. 30, no. 10, pp. 3925–3952, 2023.

[10] N. B. Bhavikatta, “AI-Driven Inventory Optimization in Supply Chains: A Comprehensive Review on Reducing Stockouts and Mitigating Overstock Risks,” Journal of Computer Science and Technology Studies, vol. 7, no. 7, pp. 01–13, 2025.

[11] S. B. Dalimunthe, S. Sinulingga, and R. Ginting, “JSTI Jurnal Sistem Teknik Industri Implementation Of Machine Learning in Demand Forecasting: A Review of Method Used in Demand Forecasting with Machine Learning,” Jurnal Sistem Teknik Industri (JSTI), vol. 25, no. 1, p. 2023, 2023, doi: 10.32734/jsti.

[12] A. A. Nasution, N. Matondang, and A. Ishak, “Inventory Optimization Model Design with Machine Learning Approach in Feed Mill Company,” Jurnal Sistem Teknik Industri, vol. 24, no. 2, pp. 254–272, 2022.

[13] E. El-Shafeiy, M. Alsabaan, M. I. Ibrahem, and H. Elwahsh, “Real-time Anomaly Detection for Water Quality Sensor Monitoring Based on Multivariate Deep Learning Technique,” Sensors, vol. 23, no. 20, p. 8613, 2023.

[14] S. Balasubramanian, M. Vodenicharova, and C. Srinu, “From Data to Decisions Leveraging Machine Learning in Supply-Chain Management,” Journal of Propulsion Technology, vol. 44, no. 4, pp. 4218–4225, 2023.

[15] N. Sembiring, W. B. Sari Harahap, F. A. Khalif, K. Chandra, and C. Herlim, “Forecasting Demand for Donuts Using a Machine Learning Approach,” Proceedings - ELTICOM 2024: 8th International Conference on Electrical, Telecommunication and Computer Engineering: Tech-Driven Innovations for Global Organizational Resilience, pp. 159–164, 2024, doi: 10.1109/ELTICOM64085.2024.10864725.

[16] M. Zohdi, M. Rafiee, V. Kayvanfar, and A. Salamiraad, “Demand Forecasting Based Machine Learning Algorithms on Customer Information: An Applied Approach,” International Journal of Information Technology, vol. 14, no. 4, pp. 1937–1947, 2022.

[17] G. B. R. Matahurila, F. P. Putra, L. F. Himmah, A. Maulana, S. Riyadi, and M. Y. R. Pandin, “Analisis Pengaruh Forecasting Demand Terhadap Efisiensi Manajemen Persediaan,” As-Syirkah: Islamic Economic & Financial Journal, vol. 3, no. 3, pp. 1475–1489, 2024.

[18] N. Walliman, Research Methods: The Basics. Routledge, 2021.

[19] BMKG, “Data suhu dan curah hujan tahunan Indonesia tahun 2022–2024,” Badan Meteorologi, Klimatologi, dan Geofisika. Accessed: Oct. 05, 2024. [Online]. Available: https://www.bmkg.go.id

[20] BPS, “Pendapatan per Kapita dan Jumlah Penduduk Indonesia 2022–2024,” Badan Pusat Statistik. Accessed: Oct. 05, 2024. [Online]. Available: https://www.bps.go.id

[21] I. Winarto, R. H. A. Shiddieqy, and S. T. Rivai Wardhani, Manajemen Operasi, Perencanaan Produksi, dan Pengendalian Persediaan. Penerbit Andi, 2025.

[22] E. Arif, I. Suherman, and A. P. Widodo, Revolusi Prediksi Saham: Pemanfaatan Machine Learning dan Analisis Sentimen dalam Dunia Keuangan. Greenbook Publisher, 2025.

[23] M. A. M. R. Setiawan, K. Kusrini, and A. D. Hartono, “Menggunakan Metode Machine Learning Untuk Memprediksi Nilai Mahasiswa Dengan Model Prediksi Multiclass,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 10, no. 1, pp. 190–204, 2025.

[24] N. Sunendar, H. P. Putro, and R. Hesananda, “Prediksi Penjualan Aerosol Menggunakan Algoritma ARIMA, LSTM Dan GRU,” INSOLOGI: Jurnal Sains dan Teknologi, vol. 4, no. 1, pp. 113–126, 2025.

[25] M. A. Purnama, J. Ramadhani, Y. S. Anugraha, and L. Efrizoni, “Perbandingan Performa Algoritma Random Forest Dan Gradient Boosting Dalam Mengklasifikasi Churn Telco,” Techno.com, vol. 23, no. 3, 2024.

[26] R. Kaestria and E. F. Himmah, “Implementasi Bahasa Pemrograman Python untuk Path analysis,” Jurnal Komputasi, vol. 11, no. 2, pp. 105–117, 2023.

[27] T. Hassan, M. Majeed, and M. Ahmad, “Optimizing SVM Performance through Combinatorial Hyperparameter Tuning and Model Selection,” International Journal Bioautomation, vol. 29, no. 2, p. 117, 2025.

[28] A. Wiejaya and I. Fenriana, “Prediksi Harga Saham Top 10 NASDAQ dengan Time Series Prophet,” bit-Tech, vol. 7, no. 2, pp. 252–262, 2024.

[29] E. R. Susanto and A. Cahyana, “Penerapan Algoritma XGBoost untuk Prediksi Diabetes: Analisis Confusion Matrix dan ROC Curve,” Fountain of Informatics Journal, vol. 10, no. 1, pp. 40–50, 2025.