Main Article Content

Abstract

Purpose: This study evaluates the adequacy of public data for applying Economic Order Quantity (EOQ), Safety Stock (SS), and Reorder Point (ROP) to PT Mayora Indah Tbk’s inventory for the 2024–2025 period.


Research Method: This study employs a descriptive quantitative approach with a documentary design. The data were drawn from consolidated financial statements and sustainability reports, and were then evaluated based on physical data requirements, relevant costs, demand, and lead time.


Results and Discussion: The public report provides only aggregate inventory values and does not disclose quantities, ordering costs, storage costs, or lead times for each material. The insurance coverage amount is not the annual premium, and data do not support the ordering frequency and previous SS parameters. Therefore, the estimates for EOQ, ROP, cost savings, and margin improvement cannot be validated.


Implications: Applying the model requires transaction data at the homogeneous material level, including physical usage, incremental costs, order history, lead times, and service targets. The research findings serve as the basis for improving inventory data management and for subsequent implementation studies.


Originality: This study highlights the methodological limitations of using consolidated financial statements to make operational EOQ decisions at large-scale FMCG companies.

Keywords

data readiness inventory management economic order quantity safety stock reorder point

Article Details

How to Cite
Rasidin, R., Sundari, A., Dinarsuci, G., Amanda, S. K., & Fitrianti, S. (2026). Optimizing Raw Material Inventory Costs at the Corporate Macro Level: A Financial EOQ Approach and Risk Mitigation at PT. Mayora Indah Tbk (2024–2025). Advances in Managerial Auditing Research, 4(3), 205–226. https://doi.org/10.60079/amar.v4i3.945

References

  1. Afiah, N. (2025). Analisis Pengelolaan Persediaan Bahan Baku Dengan Menggunakan Metode EOQ Pada Lewa Bakery. Bongaya Journal of Research in Accounting (BJRA), 8(2), 103–111. https://doi.org/10.37888/bjra.v8i2.714
  2. Ahakonye, L. A. C., Zainudin, A., Shanto, M. J. A., Lee, J.-M., Kim, D.-S., & Jun, T. (2024). A multi-MLP prediction for inventory management in manufacturing execution system. Internet of Things, 26, 101156. https://doi.org/https://doi.org/10.1016/j.iot.2024.101156
  3. Alam, M. K., Thakur, O. A., & Islam, F. T. (2023). Inventory management systems of small and medium enterprises in Bangladesh. Rajagiri Management Journal, 18(1), 8–19. https://doi.org/10.1108/RAMJ-09-2022-0145
  4. Arief, M., & Adi, T. W. (2024). Optimalisasi pengendalian persediaan pertamax pada fuel terminal X menggunakan metode Economic Order Quantity. Prosiding Seminar Nasional Teknologi Energi Dan Mineral, 4(1), 481–491. https://doi.org/10.53026/prosidingsntem.v4i1.180
  5. Arifani, A. (2023). Relationship between Working Capital Elements and Company Profitability. Advances: Jurnal Ekonomi & Bisnis, 1(2), 124–130. https://doi.org/10.60079/ajeb.v1i2.73
  6. Balkhi, B., Alshahrani, A., & Khan, A. (2022). Just-in-time approach in healthcare inventory management: Does it really work? Saudi Pharmaceutical Journal, 30(12), 1830–1835. https://doi.org/https://doi.org/10.1016/j.jsps.2022.10.013
  7. Berling, P., & Sonntag, D. R. (2022). Inventory control in production–inventory systems with random yield and rework: The unit‐tracking approach. Production and Operations Management, 31(6), 2628–2645. https://doi.org/10.1111/poms.13706
  8. Bucarey, V., Calderón, S., Muñoz, G., & Semet, F. (2024). Decision-Focused Predictions via Pessimistic Bilevel Optimization: A Computational Study BT - Integration of Constraint Programming, Artificial Intelligence, and Operations Research (B. Dilkina (ed.); pp. 127–135). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-60597-0_9
  9. Chakir, A., Andry, J. F., Ullah, A., Bansal, R., & Ghazouani, M. (2024). Engineering applications of artificial intelligence. Springer Nature.
  10. Essila, J. C. (2022). Strategies for reducing healthcare supply chain inventory costs. Benchmarking: An International Journal, 30(8), 2655–2669. https://doi.org/10.1108/BIJ-11-2021-0680
  11. Feng, Y., Abdus, S., Tuo, G., & Chen, S. (2022). Raw materials and production control with random supply and demand, an outside market and production capacity. Operations Research Letters, 50(6), 679–684. https://doi.org/https://doi.org/10.1016/j.orl.2022.10.008
  12. Guo, Y., Liu, F., Song, J.-S., & Wang, S. (2025). Supply chain resilience: A review from the inventory management perspective. Fundamental Research, 5(2), 450–463. https://doi.org/https://doi.org/10.1016/j.fmre.2024.08.002
  13. Hammler, P., Riesterer, N., Mu, G., & Braun, T. (2023). Multi-Echelon Inventory Optimization Using Deep Reinforcement Learning BT - Quantitative Models in Life Science Business: From Value Creation to Business Processes (J. K. Canci, P. Mekler, & G. Mu (eds.); pp. 73–93). Springer International Publishing. https://doi.org/10.1007/978-3-031-11814-2_5
  14. Huo, B., Li, D., & Gu, M. (2024). The impact of supply chain resilience on customer satisfaction and financial performance: A combination of contingency and configuration approaches. Journal of Management Science and Engineering, 9(1), 38–52. https://doi.org/https://doi.org/10.1016/j.jmse.2023.10.002
  15. Huynh, N., & Le, Q. N. (2025). From chain to capital: Supply chain risks and working capital management. Economics Letters, 247, 112100. https://doi.org/https://doi.org/10.1016/j.econlet.2024.112100
  16. Jaber, M. Y., & Peltokorpi, J. (2024). Economic order/production quantity (EOQ/EPQ) models with product recovery: A review of mathematical modeling (1967–2022). Applied Mathematical Modelling, 129, 655–672. https://doi.org/https://doi.org/10.1016/j.apm.2024.02.022
  17. Jadidi, O., Firouzi, F., & Sorooshian, S. (2025). A closed-form solution approach for optimal reorder point in economic order quantity models with uncertain demands. Decision Analytics Journal, 16, 100622. https://doi.org/https://doi.org/10.1016/j.dajour.2025.100622
  18. Khakbaz, A., Alfares, H. K., Amirteimoori, A., & Tirkolaee, E. B. (2024). A novel cross-docking EOQ-based model to optimize a multi-item multi-supplier multi-retailer inventory management system. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05790-9
  19. Mallick, R. K., Patra, K., & Mondal, S. K. (2023). A new economic order quantity model for deteriorated items under the joint effects of stock dependent demand and inflation. Decision Analytics Journal, 8, 100288. https://doi.org/https://doi.org/10.1016/j.dajour.2023.100288
  20. Milewski, D., & Wiśniewski, T. (2022). Regression analysis as an alternative method of determining the Economic Order Quantity and Reorder Point. Heliyon, 8(9). https://doi.org/10.1016/j.heliyon.2022.e10643
  21. Öztürk, H., & Konstantaras, I. (2025). EOQ model with defective products, batch shipment and partial backorders. Annals of Operations Research, 351(3), 1941–1988. https://doi.org/10.1007/s10479-025-06669-7
  22. Palanivel, M., Venkadesh, M., Vetriselvi, S., & Suganya, M. (2025). An analytics-driven economic order quantity model integrating fuzzy learning for deteriorating imperfect items in sustainable supply chains. Supply Chain Analytics, 10, 100120. https://doi.org/https://doi.org/10.1016/j.sca.2025.100120
  23. Pham, C. M., Lokuge, S., Nguyen, T.-T., & Adamopoulos, A. (2023). Exploring knowledge management enablers for blockchain-enabled food supply chain implementations. Journal of Knowledge Management, 28(1), 210–231. https://doi.org/10.1108/JKM-07-2022-0586
  24. Qin, H., Simchi‐Levi, D., Ferer, R., Mays, J., Merriam, K., Forrester, M., & Hamrick, A. (2022). Trading safety stock for service response time in inventory positioning. Production and Operations Management, 31(12), 4462–4474. https://doi.org/10.1111/poms.13869
  25. Reyaldi, F., Arianto, B., Moektiwibowo, H., & Mandagie, K. L. (2023). Analisis Pengendalian Persediaan Bahan Baku Dengan Pendekatan Metode Economic Order Quantity (Eoq). Jurnal Teknik Industri Universitas Dirgantara Marsekal Suryadarma (Unsurya), 12(1), 80–89. https://journal.universitassuryadarma.ac.id/index.php/jtin/article/view/1055
  26. Saldanha, J. P. (2022). Estimating the reorder point for a fill-rate target under a continuous review policy in the presence of non-standard lead-time demand distributions. Transportation Research Part E: Logistics and Transportation Review, 164, 102766. https://doi.org/https://doi.org/10.1016/j.tre.2022.102766
  27. Saldanha, J. P., Price, B. S., & Thomas, D. J. (2023). A nonparametric approach for setting safety stock levels. Production and Operations Management, 32(4), 1150–1168. https://doi.org/10.1111/poms.13918
  28. San-José, L. A., Sicilia, J., González-de-la-Rosa, M., & Febles-Acosta, J. (2022). Profit maximization in an inventory system with time-varying demand, partial backordering and discrete inventory cycle. Annals of Operations Research, 316(2), 763–783. https://doi.org/10.1007/s10479-021-04161-6
  29. Seyedan, M., Mafakheri, F., & Wang, C. (2023). Order-up-to-level inventory optimization model using time-series demand forecasting with ensemble deep learning. Supply Chain Analytics, 3, 100024. https://doi.org/https://doi.org/10.1016/j.sca.2023.100024
  30. Subur, S., & Andriani, M. (2025). Analisis Persediaan Bahan Baku Kelapa Sawit Dengan Menggunakan Metode Economic Order Quantity (EOQ). Jurnal Industri Samudra, 6(1), 32–37. https://doi.org/10.55377/jis.v6i1.11367
  31. Sukmana, S. D., & Wijaya, H. (2026). Evaluate the Effectiveness of the Inventory Accounting Information System in Supporting Internal Control. Advances: Jurnal Ekonomi & Bisnis, 4(3 SE-Articles), 542–557. https://doi.org/10.60079/ajeb.v4i3.811
  32. Teerasoponpong, S., & Sopadang, A. (2022). Decision support system for adaptive sourcing and inventory management in small- and medium-sized enterprises. Robotics and Computer-Integrated Manufacturing, 73, 102226. https://doi.org/https://doi.org/10.1016/j.rcim.2021.102226
  33. Utama, D. M., Santoso, I., Hendrawan, Y., & Dania, W. A. P. (2022). Integrated procurement-production inventory model in supply chain: A systematic review. Operations Research Perspectives, 9, 100221. https://doi.org/https://doi.org/10.1016/j.orp.2022.100221
  34. Vázquez-Serrano, J. I., Cárdenas-Barrón, L. E., Vicencio-Ortiz, J. C., Smith, N. R., Bourguet-Díaz, R. E., Céspedes-Mota, A., & Peimbert-García, R. E. (2025). An integrated analytical framework for inventory and pricing of perishable products in multi-echelon supply chains. Supply Chain Analytics, 12, 100157. https://doi.org/https://doi.org/10.1016/j.sca.2025.100157
  35. Vazquez Hernandez, J., & Elizondo Rojas, M. D. (2023). Improving spare parts (MRO) inventory management policies after COVID-19 pandemic: a Lean Six Sigma 4.0 project. The TQM Journal, 36(6), 1627–1650. https://doi.org/10.1108/TQM-08-2023-0245
  36. Yassine, N. (2022). Inventory planning under supplier uncertainty in a two-level supply chain. The International Journal of Logistics Management, 34(2), 497–516. https://doi.org/10.1108/IJLM-02-2021-0104
  37. Yeboah, S., Kjærland, F., & Kustec, I. (2025). Resilience in Cyclicality: Impact of Inventory Management Efficiency on Operational Profitability. Managerial and Decision Economics, 46(2), 832–842. https://doi.org/10.1002/mde.4406
  38. Zamani Dadaneh, D., Moradi, S., & Alizadeh, B. (2023). Simultaneous planning of purchase orders, production, and inventory management under demand uncertainty. International Journal of Production Economics, 265, 109012. https://doi.org/https://doi.org/10.1016/j.ijpe.2023.109012
  39. Zhou, Y., Shen, X., & Yu, Y. (2023). Inventory control strategy: based on demand forecast error. Modern Supply Chain Research and Applications, 5(2), 74–101. https://doi.org/10.1108/MSCRA-02-2023-0009