Inventory Management Optimization of Snack to Minimize Days Sales Inventory (DSI) and Total Cost

Yulida Intani  Dewi; Indra  Elfantoro; Mochamad Rafi Herdiana; Zulfa Fitri  Ikatrinasari

Authors

Yulida Intani Dewi University of Mercu Buana Author
Indra Elfantoro University of Mercu Buana Author
Mochamad Rafi Herdiana Universitas Mercu Buana Author
Zulfa Fitri Ikatrinasari University of Mercu Buana Author

Keywords:

inventory management optimization, days sales inventory, demand forecasting, economic order quantity, periodic order quantity

Abstract

This study aims to optimize inventory control for snack products in the retail industry, characterized by highly fluctuating demand and elevated Days Sales Inventory (DSI) levels. Focusing on Class A products with high DSI offers the greatest potential impact on inventory management efficiency. The approach integrates Holt-Winters forecasting, ARIMA (1,1,1), and Random Forest Regression with quantitative models such as Economic Order Quantity (EOQ) and Periodic Order Quantity (POQ), as well as stock control techniques including Safety Stock and Reorder Point, to determine the optimal order quantity and ordering time. Sales data for 21 weeks were processed to generate sales forecasts for the subsequent 31 weeks, covering weeks 22 through 52, using all three forecasting methods. The evaluation metrics indicate that Random Forest Regression achieved the best performance, with a Mean Absolute Error (MAE) of 42.4, Mean Absolute Percentage Error (MAPE) of 13.9%, and a Root Mean Squared Error (RMSE) of 46.7, The results show a significant reduction in DSI and total costs, contributing positively to strengthening the company’s cash flow. Further analysis over the 31-week period using the POQ method resulted in a decrease in DSI from the actual level of 111 days to 71 days, and also reduction in total cost from IDR 14.933.114 to IDR 10.104.863, representing a difference of IDR 4.828.250. In addition to the integrated forecasting and EOQ–POQ methods, it is recommended to enhance the adaptation of dynamic forecasting models that are more responsive to changes in demand patterns and to develop real-time monitoring systems using ERP or IoT technology to minimize the risks of stockouts and product spoilage. This research provides both practical and academic contributions toward achieving more efficient and sustainable inventory management for snack products.

Author Biographies

Yulida Intani Dewi, University of Mercu Buana

Departement of Industrial Engineering
Indra Elfantoro, University of Mercu Buana

Departement of Industrial Engineering
Zulfa Fitri Ikatrinasari, University of Mercu Buana

Departement of Industrial Engineering

References

[1] E. Puspitasari, A. Magfirah, and N. Apriani, “Perencanaan Distribusi Air Marina Menggunakan Metode DRP (Distribution Requirements Planning),” Scientific: Journal of Computer Science and Informatics, vol. 1, no. 1, pp. 38–45, 2024.

[2] E. H. Laaziz, “AI based forecasting in fast fashion industry: A review,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Jun. 2020. doi: 10.1088/1757-899X/827/1/012065.

[3] N. Hidayat, T. Warani, M. A. Pangestu, and R. Mikal, “Optimalisasi Pengendalian Persediaan Bahan Baku Dalam Peningkatan Efisiensi Operasional Pada UMKM Kebab dan Burger Foursist di Kota Tarakan,” Jurnal Ekonomi, Bisnis dan Manajemen, vol. 4, no. 2, pp. 247–265, Jun. 2025, doi: 10.58192/ebismen.v4i2.3571.

[4] W. S. Saputra, R. Ernawati, R. Wiwik, and A. Wulansari, “Analysis of Raw Material Inventory Control Using Economic Order Quantity (EOQ) Method at CV. XYZ,” 2021. [Online]. Available: https://ijcis.net/index.php/ijcis/indexJournalIJCIShomepage-https://ijcis.net/index.php/ijcis/index

[5] S. Arista, D. Irwati, and F. E. Putra, “Analisis Pengendalian Bahan Baku Dengan Pendekatan Metode Economic Order Quantity (EOQ):(Studi Kasus: PT. ABC),” Jurnal Teknologi dan Manajemen Industri Terapan, vol. 4, no. 3, pp. 559–571, 2025.

[6] V. Afrilia, “Analisis Optimalisasi Persediaan Barang Dengan Menggunakan Metode Economic Order Quantity Pada PT. Aneka usaha,” 2020. [Online]. Available: https://journal-computing.org/index.php/journal-cisa/index

[7] K. F. Firdaus and E. Widajanti, “Analisis Pengendalian Persediaan Bahan Baku Menggunakan Metode Material Requirement Planning (MRP) Pada Risma Brownies & Cake Gemolong,” Jurnal Riset Manajemen Dan Ekonomi (Jrime), vol. 2, no. 3, pp. 226–248, 2024.

[8] K. Chaowai and P. Chutima, “Demand Forecasting and Ordering Policy of Fast-Moving Consumer Goods with Promotional Sales in a Small Trading Firm,” Engineering Journal, vol. 28, no. 4, pp. 21–40, Apr. 2024, doi: 10.4186/ej.2024.28.4.21.

[9] L. Fan et al., “Change is safer: a dynamic safety stock model for inventory management of large manufacturing enterprise based on intermittent time series forecasting,” J. Intell. Manuf., vol. 36, no. 6, pp. 3983–4003, Jun. 2024, doi: 10.1007/s10845-024-02442-y.

[10] D. Mawonde and C. Demberere, “The Effect of Inventory Control Systems on Organisational Performance in the Mining Sector of Zimbabwe,” International Journal of Research and Innovation in Social Science, vol. 06, no. 01, pp. 273–279, 2022, doi: 10.47772/ijriss.2022.6118.

[11] R. Kumar and K. Kumari Pal, “Enhancing Economic Development Through Inventory Management Optimization in Agro-Based Industries in Bihar: A Comparative Study of EOQ And EPQ Models.” [Online]. Available: www.ijfmr.com

[12] H. Novandi Tambunan, R. Haribowo, M. Munizu, and S. Maruli Tua Pandiangan, “Operational Management for Business Production Processes,” 2025. [Online]. Available: http://lpppipublishing.com/index.php/ijessm

[13] L. Bartlett and F. Vavrus, “Comparative case studies: An innovative approach,” Nordic journal of comparative and international education (NJCIE), vol. 1, no. 1, 2017.

[14] A. W. K. Tan, S. N. Wahab, H. L. Tay, and S. C. Chin, Managing And Improving Warehouse Operations. World Scientific, 2025.

[15] M. R. Fadilah, H. R. Sofyan, and I. R. Maulia, “Analisis Perbandingan Metode Economic Order Quantity (EOQ) Dan Periodic Order Quantity (POQ) Terhadap Pengendalian Persediaan Studi Kasus Pada PT Sriwijaya Abadi Solusindo,” Jurnal Akademik Ekonomi Dan Manajemen, vol. 2, no. 1, pp. 277–287, 2025.

[16] M. M. Abdullah, “Using the single-exponential-smoothing time series model under the additive holt-winters algorithm with decomposition and residual analysis to forecast the reinsurance-revenues dataset,” Pakistan Journal of Statistics and Operation Research, vol. 20, no. 2, pp. 311–340, 2024.

[17] R. H. Teunter, M. Zied Babai, and A. A. Syntetos, “Simple Economic Order Quantity heuristics for stochastic inventory control,” IMA Journal of Management Mathematics, vol. 36, no. 4, pp. 649–667, 2025.

[18] A. Immadisetty, “Real-Time Inventory Management: Reducing Stockouts and Overstocks in Retail,” Journal of Recent Trends in Computer Science and Engineering (JRTCSE), vol. 13, no. 1, pp. 77–88, 2025.

[19] C. Wang and J. Wang, “Research on E-Commerce Inventory Sales Forecasting Model Based on ARIMA and LSTM Algorithm,” Mathematics, vol. 13, no. 11, p. 1838, 2025.

[20] R. Handayani and C. Afrianandra, “Menetapkan Periodic Order Quantity (POQ) (Studi Kasus Pada Pabrik Tempe Soybean),” Jurnal Ilmiah Mahasiswa Ekonomi Akuntansi (JIMEKA), vol. 7, no. 2, pp. 308–323, 2022.