Peramalan Penjualan Produk Susu Steril PT XYZ di Pasar Jakarta Tahun 2025 Menggunakan Model ARIMA (Autoregressive Integrated Moving Average): Sales Forecast for PT XYZ’s Sterilized Milk Products in the Jakarta Market for 2025 Using the ARIMA (Autoregressive Integrated Moving Average) Model

Akbar Bimo Prastio; Imam Widhiono; Sri Lestari; Budi Dharmawan

doi:10.33019/jia.v7i1.6347

Authors

Akbar Bimo Prastio Jenderal Soedirman University
Imam Widhiono Jenderal Soedirman University
Sri Lestari Jenderal Soedirman University
Budi Dharmawan Jenderal Soedirman University

DOI:

https://doi.org/10.33019/jia.v7i1.6347

Keywords:

ARIMA, FMCG, Sales Forecast, Sterilized Milk

Abstract

This study examines the sales forecast for PT XYZ’s sterilized milk products, recognizing the critical importance of accurate forecasting in the dynamic and competitive fast-moving consumer goods (FMCG) sector. Reliable forecasts are essential for distributors to optimize demand planning and inventory control. The Autoregressive Integrated Moving Average (ARIMA) model was applied to historical monthly sales data from 16 distributors in the Jakarta market covering the 2022–2024 period. The ARIMA procedure included data collection and visualization, stationarity testing (ADF, ACF, PACF), differencing, model identification, parameter estimation, and optimal model selection. The results reveal monthly sales fluctuations with a clear downward trend toward the end of the observation period, with the ARIMA (1,0,1) model emerging as the best fit. Forecasts for 2025 indicate a continued decline in sales value, underscoring the need for stronger collaboration between PT XYZ and its distributors in promotional planning and inventory management to adapt to market shifts and capitalize on emerging opportunities.

Downloads

Download data is not yet available.

References

B, R., Chirag, C., & Pai B, G. (2024). Use of predictive analysis, forecasting the trends in the FMCG. SDM Institute for Management Development. https://www.sdmimd.ac.in/marketingconference2024/papers/IMC2479.pdf

Basson, L. M., Kilbourn, P. J., & Walters, J. (2019). Forecast accuracy in demand planning: A fast-moving consumer goods case study. Journal of Transport and Supply Chain Management, 13. https://doi.org/10.4102/jtscm.v13i0.427

Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 10, 1847979018808673. https://doi.org/10.1177/1847979018808673

Heizer, J., Render, B., & Munson, C. (2017). Operations management: Sustainability and supply chain (12th ed.). Pearson Education.

Henchion, M., Moloney, A. P., Hyland, J., Zimmermann, J., & McCarthy, S. (2021). Review: Trends for meat, milk and egg consumption for the next decades and the role played by livestock systems in the global production of proteins. Animal, 15, 100287. https://doi.org/10.1016/j.animal.2021.100287

Juliamida, I. S., Haludin, G., Minarso, M. F., Natalia, I., Niser, A., & Iskandar, H. (2024). Analisis penerapan konsep PDCA pada PT Mayora Indah Tbk dalam meningkatkan penjualan dan daya saing di industri FMCG, 4(3).

Khoiri, H. A. (2023). Analisis deret waktu univariat. UNIPMA Press. https://eprint.unipma.ac.id/323/1/1.%20Bu%20Halwa%2015%20Eks%2C%20B5.pdf

Ma, L., Hu, C., Lin, R., & Han, Y. (2018). ARIMA model forecast based on EViews software. IOP Conference Series: Earth and Environmental Science, 208, 012017. https://doi.org/10.1088/1755-1315/208/1/012017

Mahadira, F., & Purwanegara, M. S. (2014). A study of brand consumption and consumer satisfaction towards fast-moving consumer goods. Journal of Business and Management, 3(1), 61–75.

Meher, B. K., Hawaldar, I. T., Spulbar, C. M., & Birau, F. R. (2021). Forecasting stock market prices using mixed ARIMA model: A case study of Indian pharmaceutical companies. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3771334

Nuryani, F. T., Nurkesuma, N., & Hadibrata, B. (2022). Korelasi keputusan pembelian: Kualitas produk, persepsi harga dan promosi (literature review manajemen pemasaran). Jurnal Ekonomi Manajemen Sistem Informasi, 3(4). https://d1wqtxts1xzle7.cloudfront.net/108066244/612-libre.pdf

Perera, H. N., & Fahimnia, B. (2024). Multi-period ordering decisions in the presence of retail promotions. European Journal of Operational Research, 319(3), 763–776. https://doi.org/10.1016/j.ejor.2024.07.009

Prasetyo, K., Putri, D. D., Kartika Eka Wijayanti, I., & Zulkifli, L. (2023). Forecasting of red chilli prices in Banyumas Regency: The ARIMA approach. E3S Web of Conferences, 444, 02017. https://doi.org/10.1051/e3sconf/202344402017

Pusat Data dan Sistem Informasi Pertanian. (2022). Statistik pertanian. Pusat Data dan Sistem Informasi Pertanian, Kementerian Pertanian Republik Indonesia. https://satudata.pertanian.go.id/assets/docs/publikasi/BUKU_STATISTIK_PERTANIAN_2022_compressed.pdf

Regina, T., & Jodiawan, P. (2021). Proposed improvement of forecasting using time series forecasting of fast-moving consumer goods. JIEMS (Journal of Industrial Engineering and Management Systems), 14(1). https://doi.org/10.30813/jiems.v14i1.2418

Santos, V., Sampaio, M., & Alliprandini, D. H. (2020). The impact of product variety on fill rate, inventory and sales performance in the consumer goods industry. Journal of Manufacturing Technology Management, 31(7), 1481–1505. https://doi.org/10.1108/JMTM-06-2019-0213

Selvakumar, S., Renugadevi, G., Vinishah, N., & Yashwanth, R. (2024). Sales forecasting based on time series analysis. 2024 International Conference on Science Technology Engineering and Management (ICSTEM), 1–7. https://doi.org/10.1109/ICSTEM61137.2024.10560659

Suwignjo, P., Panjaitan, L., Baihaqy, A. R., & Rusdiansyah, A. (2023). Predictive analytics to improve inventory performance: A case study of an FMCG company. Operations and Supply Chain Management: An International Journal, 293–310. https://doi.org/10.31387/oscm0530390

Tahirov, N. (2024). Service level anchoring in demand forecasting: The moderating impact of retail promotions and product perishability [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.13942993

Tajrin, T., Hendra, H., & Gurning, J. F. (2024). Analisis performansi metode autoregressive integrated moving average (ARIMA) dalam pengendalian persediaan susu. Jurnal Teknik Informasi dan Komputer (Tekinkom), 7(1), 481. https://doi.org/10.37600/tekinkom.v7i1.1222

Tampubolon, A. L., Dani, D., Andini, Y., Lala, L., Mutiara, M., Sinaga, C., Silalahi, R., & Ompusunggu, D. P. (2024). Aplikasi dan analisis statistik menggunakan Stata untuk TSLS, Probit, Logit, dan Tobit. Jurnal Ekonomi dan Pembangunan Indonesia, 2(1), 69–72. https://doi.org/10.61132/jepi.v2i1.281

Trihatmoko, R. A., & Mulyani, R. (2018). Distribution strategy for new product marketing success: Fast moving consumer goods (FMCG) business. Management and Human Resource Research Journal, 7(12). http://repo.unsa.ac.id/216/1/Distribution%20Strategy%20For%20New%20Product%20Marketing%20Success.pdf

Trihatmoko, R. A., Mulyani, R., & Lukviarman, N. (2018). Product placement strategy in the business market competition: Studies of fast moving consumer goods. Business and Management Horizons, 6(1), 150. https://doi.org/10.5296/bmh.v6i1.13239

Wulandari, S., & Bowo, P. A. (2019). Economic education analysis journal. Economic Education Analysis Journal. https://doi.org/10.15294/eeaj.v13i2.35717