Optimization of Coffee Inventory and Replenishment Planning under Demand Uncertainty: A Linear Programming Approach
DOI:
10.29303/jm.v8i2.12437Published:
2026-06-30Downloads
Abstract
This study develops a multi-period linear programming model to optimize coffee inventory and replenishment planning under demand uncertainty. The model integrates inventory balance, replenishment capacity, storage capacity, and safety stock constraints to determine cost-efficient replenishment quantities and ending inventory levels for three coffee products: Robusta, Arabica, and Blend. Simulated data over six planning periods were analyzed under low, medium, and high demand scenarios using PuLP in Python. The results show that optimal solutions were obtained under low and medium demand conditions, with total inventory costs of Rp 286,836,000 and Rp 480,466,000, respectively. Under low demand, inventory was maintained exactly at safety stock levels, reflecting a just-in-time strategy. Under medium demand, the model temporarily increased Robusta inventory to anticipate future demand. However, the high-demand scenario was infeasible, indicating insufficient replenishment capacity. The model provides a practical decision support tool for cost-efficient and resilient coffee inventory management.
Keywords:
Coffee inventory demand uncertainty linear programming replenishment planningReferences
Achkar, V. G., Brunaud, B. B., Pérez, H. D., Musa, R., Méndez, C. A., & Grossmann, I. E. (2024). Extensions to the guaranteed service model for industrial applications of multi-echelon inventory optimization. European Journal of Operational Research, 313(1), 192–206. https://doi.org/10.1016/j.ejor.2023.08.013
Alifadillah, A., & Supriatna, H. (2023). Web-Based Coffee Inventory Application. Majalah Bisnis & IPTEK, 16(2), 309–317. https://doi.org/10.55208/xbbys974
Aouam, T., Ghadimi, F., & Vanhoucke, M. (2021). Finite inventory budgets in production capacity and safety stock placement under the guaranteed service approach. Computers & Operations Research, 131, 105266. https://doi.org/10.1016/j.cor.2021.105266
Bolaños-Zúñiga, L., & Vidal-Holguin, C. J. (2020). The impact of inventory holding costs on the strategic design of supply chains. Revista Facultad de Ingeniería Universidad de Antioquia. https://doi.org/10.17533/udea.redin.20200692
Cotta, D., & Salvador, F. (2020). Exploring the antecedents of organizational resilience practices – A transactive memory systems approach. International Journal of Operations & Production Management, 40(9), 1531–1559. https://doi.org/10.1108/IJOPM-12-2019-0827
Goyal, S., & Kumar, V. (2025). A Simplified Linear Programming Approach for Inventory Optimization in Supply Chains. 2025 14th International Conference on System Modeling & Advancement in Research Trends (SMART), 295–298. https://doi.org/10.1109/SMART66937.2025.11389540
Gupta, C., Kumar, V., & Kumar, K. (2023). A Linear Programming Approach to Optimize the Storage Capacity. 2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART), 508–511. https://doi.org/10.1109/SMART59791.2023.10428501
Islamiyah, A. H., Sa’adah, U., & Karim, C. (2025). MILP Model Solution Steps: Implementation of Big M Simplex and Branch and Bound in the Coffee Supply Chain. CAUCHY: Jurnal Matematika Murni Dan Aplikasi, 10(2), 764–776. https://doi.org/10.18860/cauchy.v10i2.35380
Ko, Y. D., Song, B. D., & Park, K. (2020). Efficient food service chain management considering substitute products. Asia Pacific Journal of Marketing and Logistics, 33(2), 667–685. https://doi.org/10.1108/APJML-08-2019-0485
Kozlova, A., Kurashkin, S., & Boyko, A. (2025). Optimization of Warehouse Processes Using Machine Learning and Linear Programming. 2025 International Russian Smart Industry Conference (SmartIndustryCon), 1111–1116. https://doi.org/10.1109/SmartIndustryCon65166.2025.10986102
Kusomrosananan, T., & Phumchusri, N. (2024). Inventory Policy Improvement with Periodic Review for Perishable Goods: A Case Study of a Retail Coffee Shop in Thailand. Engineering Journal, 28(6), 59–73. https://doi.org/10.4186/ej.2024.28.6.59
Kusuma Wardana, M. F., Putri, H. B., & Tambunan, F. H. (2025). Implementation of Economic Order Quantity (Eoq) In Inventory Management: A Case Study of Chopfee Coffee Shop. Jurnal Ekobistek, 14(1), 17–23. https://doi.org/10.35134/ekobistek.v14i1.867
Nasution, A. S., Simbolon, O. B., Muliawati, T., Edriani, T. S., Noor, D. M. M., & Fauzi, R. (2025). Raw Material Inventory Control Using The Period Order Quantity (POQ) Method to Reduce Stockout and Overstock Risks. VYGOTSKY, 7(2), 97–110. https://doi.org/10.30736/voj.v7i2.1163
Paradis, G. (2025). WS3: An open-source Python framework for integrated simulation and optimization of forest landscape and wood supply systems. https://doi.org/10.31223/X55R1X
Pathak, K., Yadav, A. S., & Agarwal, P. (2024). Optimizing Two-Warehouse Inventory for Shelf-Life Stock with Time-Varying Bi-Quadratic Demand Under Shortages and Inflation. Mathematical Modelling of Engineering Problems, 11(2), 446–456. https://doi.org/10.18280/mmep.110216
Torabzadeh, S. A., Nejati, E., Aghsami, A., & Rabbani, M. (2022). A dynamic multi-objective green supply chain network design for perishable products in uncertain environments, the coffee industry case study. International Journal of Management Science and Engineering Management, 17(3), 220–237. https://doi.org/10.1080/17509653.2022.2055672
Woubante, G. W. (2017). The Optimization Problem of Product Mix and Linear Programming Applications: Case Study in the Apparel Industry. Open Science Journal, 2(2). https://doi.org/10.23954/osj.v2i2.853
License
Copyright (c) 2026 Lingga Gita Dwikasari, Dilla Afriansyah
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
