Forecasting Inventory Demand Under Volatile Sales Patterns Using the Prophet Algorithm
DOI:
https://doi.org/10.32664/j-intech.v14i01.2032Kata Kunci:
Forecasting, Inventory, MAPE, Prophet, RMSEAbstrak
Inventory availability is a critical factor for companies to maintain operational continuity and customer satisfaction. However, many organizations still face challenges in forecasting demand, particularly when sales patterns are highly volatile and irregular. Although the Prophet forecasting algorithm has been widely used for time-series prediction, its behavior and robustness under unstable sales patterns remain insufficiently examined in practical inventory contexts. This study aims to evaluate the ability of the Prophet algorithm to forecast inventory demand using historical sales data characterized by fluctuating patterns. A quantitative time-series forecasting approach was applied using one year of secondary sales data obtained from PT XYZ. The data were cleaned to address missing values and aggregated into weekly time intervals to reduce noise. Five products with the highest transaction frequency were selected as case studies. Forecasting performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that Prophet is capable of generating reasonably accurate forecasts even under volatile demand conditions. The evaluation results indicate RMSE values ranging from 5.41 to 52.78 and MAPE values ranging from 5% to 23.46% across the five analyzed products. These findings provide empirical evidence that the Prophet algorithm can maintain forecasting robustness despite irregular demand patterns. However, the absence of comparisons with alternative forecasting models limits the strength of conclusions regarding its relative performance. This study contributes by providing empirical insight into the application of Prophet for inventory forecasting under volatile sales conditions and offers practical implications for improving inventory planning in data-driven decision-making environments.
Referensi
[1] I. Setiawan, “Rancang Bangun Aplikasi Peramalan Persediaan Stok Barang Menggunakan Metode Weighted Average (WMA) Pada Toko Barang XYZ,” J. Tek. Inform., vol. 13, no. 3, pp. 1–9, 2021.
[2] A. Lusiana and P. Yuliarty, “Penerapan Metode Peramalan (Forecasting) Pada Permintaan Atap di PT X,” Ind. Inov. J. Tek. Ind., vol. 10, no. 1, pp. 11–20, 2020, doi: 10.36040/industri.v10i1.2530.
[3] R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd ed. Melbourne, 2021.
[4] M. Gorelli, “Prophet: Automatic Forecasting Procedure,” 2024.
[5] B. K. Jha and S. Pande, “Time Series Forecasting Model for Supermarket Sales using FB-Prophet,” in ICCMC, 2021, pp. 547–554. doi: 10.1109/ICCMC51019.2021.9418033.
[6] S. Kartal, “Product Demand Forecasting with the Prophet Model,” 2023.
[7] E. Žunić, K. Korjenić, K. Hodžić, and D. Đonko, “Application of Facebook’s Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data,” Int. J. Comput. Sci. Inf. Technol., vol. 12, no. 2, pp. 23–36, 2020, doi: 10.5121/ijcsit.2020.12203.
[8] M. A. Muzakki, M. A. Sabila, S. Sundari, and B. Wisnuadhi, “Analisis Algoritma Prophet untuk Memprediksi Harga Pangan di Kota Bandung,” in Prosiding Industrial Research Workshop and National Seminar, 2021, pp. 659–664. doi: 10.30645/kesatria.v5i4.481.g476.
[9] C. Chatfield and H. Xing, The Analysis of Time Series: An Introduction with R, 7th ed. New York: CRC Press, Taylor & Francis Group, 2019. doi: 10.1201/9781351259446.
[10] R. J. Hyndman, Forecasting: Principles and Practice. University of Western Australia, 2014.
[11] R. Fildes, S. Ma, and S. Kolassa, “Retail forecasting: Research and practice,” Int. J. Forecast., vol. 38, no. 4, pp. 1283–1318, 2022, doi: 10.1016/j.ijforecast.2019.06.004.
[12] S. Raizada and J. R. Saini, “Comparative Analysis of Supervised Machine Learning Techniques for Sales Forecasting,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 11, 2021, [Online]. Available: http://www.ijacsa.thesai.org
[13] G. P. Zhang, Handbook of Natural Computing. Berlin, Heidelberg: Springer, 2019.
[14] J. Han, J. Pei, and H. Tong, Data Mining: Concepts and Techniques, 4th ed. 2023.
[15] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, 5th ed. Canada: John Wiley & Sons, 2016.
[16] K. Bandara, C. Bergmeir, and S. Smyl, “Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach,” Expert Syst. Appl., vol. 140, p. 112896, 2020.
[17] S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The M4 Competition: 100,000 time series and 61 forecasting methods,” Int. J. Forecast., vol. 36, no. 1, pp. 54–74, 2020, doi: 10.1016/j.ijforecast.2019.04.014.
[18] F. Petropoulos, D. Apiletti, V. Assimakopoulos, M. Z. Babai, D. K. Barrow, and S. Ben Taieb, “Forecasting: theory and practice,” Int. J. Forecast., vol. 38, pp. 705–871, 2022, doi: 10.1016/j.ijforecast.2021.11.001.
[19] C. Kuo, Modern Time Series Forecasting Techniques for Predictive Analytics and Anomaly Detection, 1st ed. 2024.
[20] S. J. Taylor and B. Letham, “Forecasting at Scale,” Am. Stat., 2018, doi: 10.7287/peerj.preprints.3190v2.
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2026 J-INTECH ( Journal of Information and Technology)
Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.
