IMPROVING DISTRIBUTION INVENTORY CONTROL THROUGH FIFO AND COSO FRAMEWORK AT PT. MITRA KENCANA DISTRIBUSINDO

Regina Luntungan (1), Sintia Nurani Korompis (2), Elisabeth Deisi Malonda (3), Noah Smith (4)
(1) Politeknik Negeri ManadoID Indonesia,
(2) Politeknik Negeri ManadoID Indonesia,
(3) Politeknik Negeri ManadoID Indonesia,
(4) University WaikatoNZ New Zealand

Abstract

This study aims to analyze the effectiveness of inventory internal control using the First In, First Out (FIFO) method at PT Mitra Kencana Distribusindo, particularly in the Non-Food Warehouse. This research employed a descriptive qualitative approach with an evaluative orientation based on the COSO internal control framework. Data were collected through structured interviews with two purposively selected informants, direct non-participant observation of warehouse activities, and documentation review of receiving notes, cash invoices, warehouse cards, stock opname documents, aging reports, non-food inventory data, and checker duty documents. Data analysis was carried out through data reduction, data display, and conclusion drawing by grouping the findings into the five COSO components and FIFO implementation criteria. The findings indicate that inventory control has been implemented through receiving and issuing procedures, segregation of duties, authorization, internal and external checker functions, warehouse cards, stock opname, aging reports, and FIFO-based inventory circulation. However, the control system is not yet fully optimal because several critical weaknesses remain, particularly incomplete batch/expiry information, limited written follow-up documentation for stock opname results, insufficient FIFO-based warehouse layout control, and inadequate follow-up records for discrepancies, returns, damaged goods, defective goods, and items approaching expiry. This study concludes that FIFO-based inventory internal control is sufficiently effective, but still requires stronger documentation, monitoring, and corrective action procedures to improve inventory accuracy, traceability, and risk responsiveness.

Full text article

Generated from XML file

References

Ahmadkhan, K., Ahmadirad, Z., Karaminezhad, K., SeyedKhamoushi, F., Karimi, K., & Khakpash, F. (2026). A novel blockchain-based approach for enhanced food supply chain traceability and waste mitigation. British Food Journal, 128(1), 426–466. https://doi.org/10.1108/BFJ-05-2024-0430

Ai, L., Zhao, Q., & Wu, Z. (2026). An inventory-routing problem for end-of-life traction batteries considering incentive-dependent returns and storage time limitations. Expert Systems with Applications, 301, 130306. https://doi.org/10.1016/j.eswa.2025.130306

Akbari, A. H., Jafari, M., & Akhavan, P. (2026). A Data-Driven Multi-Objective optimization framework for dynamic job shop scheduling with order Acceptance, inventory and Energy-Aware decisions. Computers & Industrial Engineering, 214, 111886. https://doi.org/10.1016/j.cie.2026.111886

Alkhedher, M. J. (2026). A stochastic inventory model with elastic demand and random lead time. Journal of Engineering Research. https://doi.org/10.1016/j.jer.2026.06.017

Arunadevi, E., & Umamaheswari, S. (2026). Dynamic inventory optimization for three-warehouse: Balancing freshness-driven demand and preservation investments. Expert Systems with Applications, 297, 129233. https://doi.org/10.1016/j.eswa.2025.129233

Asghari, M., Jaber, M. Y., Afshari, H., & Searcy, C. (2026). Deteriorating inventory models: A comprehensive review. Computers & Industrial Engineering, 216, 111993. https://doi.org/10.1016/j.cie.2026.111993

Bao, L., & Yu, Y. (2026). Managing perishable production systems with extended shelf life: The role of sustainability. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2026.05.024

Cuong, T. N., Kim, H.-S., Bao Long, L. N., You, S.-S., & Tan, N. D. (2025). Deep learning-enhanced quantum optimization for integrated job scheduling in container terminals. Engineering Applications of Artificial Intelligence, 148, 110431. https://doi.org/10.1016/j.engappai.2025.110431

Dalalah, D. (2025). Optimal order quantities of a multi-period inventory of compatible products. Computers & Industrial Engineering, 207, 111338. https://doi.org/10.1016/j.cie.2025.111338

Douaa, E. G., Lina, A., & Maria, L. (2026). A simulation-driven (r, Q) replenishment policy for perishable blood inventory management. 7th International Conference on Industry of the Future and Smart Manufacturing (Former International Conference on Industry 4.0 and Smart Manufacturing), 277, 484–493. https://doi.org/10.1016/j.procs.2026.02.090

Freund, D., & van Ryzin, G. (2025). Pricing fast and slow: Limitations of dynamic pricing mechanisms in ride-hailing. Transportation Research Part C: Emerging Technologies, 181, 105314. https://doi.org/10.1016/j.trc.2025.105314

Hajej, Z., & Gharbi, A. (2026). An optimal production control for stochastic manufacturing systems under perishable products. Journal of Quality in Maintenance Engineering, 32(2), 357–390. https://doi.org/10.1108/JQME-09-2025-0111

Hasiloglu-Ciftciler, M., & Kaya, O. (2025). Dynamic inventory control and pricing strategies for perishable products considering both profit and waste. Computers & Operations Research, 181, 107103. https://doi.org/10.1016/j.cor.2025.107103

Hosseini-Motlagh, S.-M., Samani, M. R. G., & Kordhaghi, H. (2026). A possibilistic programming approach in an integrated fuzzy periodic review model and clustering strategy for optimizing platelet supply chain. Expert Systems with Applications, 298, 129539. https://doi.org/10.1016/j.eswa.2025.129539

Imeri, A., Fikar, C., & Reiner, G. (2025). Unveiling Shelf-life and Consumer Behavior Dynamics in Perishable Inventory Planning. 11th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2025, 59(10), 2856–2861. https://doi.org/10.1016/j.ifacol.2025.09.480

Jahan, I., Kamal, K. T., Bhattacharjee, P., Taqi, H. Md. M., & Ali, S. M. (2025). Improving consumer awareness for reducing food waste using partial least squares structural equation modelling (PLS-SEM) approach. Cleaner and Responsible Consumption, 17, 100282. https://doi.org/10.1016/j.clrc.2025.100282

Kandasamy, J., Vimal, K. E. K., Singh, A. P., Magnani, A., Gokhale, A., & Jagtap, S. (2025). Analysis of key challenges to implementation of FEFO in perishable food supply chain. Journal of Agriculture and Food Research, 21, 101848. https://doi.org/10.1016/j.jafr.2025.101848

Kouki, C., Drent, M., Babai, M. Z., & Drent, C. (2026). Dedicated maintenance and repair shop control for spare parts networks. European Journal of Operational Research, 331(3), 809–822. https://doi.org/10.1016/j.ejor.2025.10.044

Kuo, H.-A., Hong, T.-Y., & Chien, C.-F. (2025). A deep reinforcement learning based digital twin framework for resilient production planning under demand uncertainty and an empirical study in semiconductor wafer fabrication. Computers & Industrial Engineering, 208, 111389. https://doi.org/10.1016/j.cie.2025.111389

Lunet, M., Buisman, M., Neves-Moreira, F., & Amorim, P. (2026). Aged products spillover effect and the value of holding inventory under stochastic demand: The case of Port wine. International Journal of Production Economics, 297, 110007. https://doi.org/10.1016/j.ijpe.2026.110007

Melesse, T. Y., Sanna, J., Braggio, M., Peer, M. S., & Orrù, P. F. (2026). Simulation-Based Assessment of Warehouse Logistics: A Case Study in Beverage Distribution. 7th International Conference on Industry of the Future and Smart Manufacturing (Former International Conference on Industry 4.0 and Smart Manufacturing), 277, 3531–3539. https://doi.org/10.1016/j.procs.2026.02.388

Mohamadi, N., Transchel, S., & Fransoo, J. C. (2025). Coordinate or collaborate? Reducing food waste in perishable-product supply chains. European Journal of Operational Research, 323(3), 795–809. https://doi.org/10.1016/j.ejor.2024.12.039

Najafi, M., & Zolfagharinia, H. (2024). A Multi-objective integrated approach to address sustainability in a meat supply chain. Omega, 124, 103011. https://doi.org/10.1016/j.omega.2023.103011

Niedermayr, D., Wolfartsberger, J., Bokor, B., Seiringer, W., & Altendorfer, K. (2026). VISP: Using Virtual Reality to Teach Production Process Optimization through Gamified Simulation. 7th International Conference on Industry of the Future and Smart Manufacturing (Former International Conference on Industry 4.0 and Smart Manufacturing), 277, 3205–3213. https://doi.org/10.1016/j.procs.2026.02.356

Olvera, V., Guerrero, C., & Segura, E. (2026). Control of a production–inventory system optimized with LQR for dynamic demand management. International Journal of Production Economics, 297, 109713. https://doi.org/10.1016/j.ijpe.2025.109713

Poursoltani, H., Honarvar, M., & Abedsoltan, H. (2026). Developing an integrated model of hierarchical hub location and inventory control for perishable products in urban and rural areas: A case study in food supply chain. Computers & Operations Research, 192, 107481. https://doi.org/10.1016/j.cor.2026.107481

Rautela, R., Sharma, V., & Agarkar, B. (2026). Chapter 36—Predictive modeling for demand forecasting and inventory management to minimize food waste. In T. Sarkar & A. Haldorai (Eds.), Artificial Intelligence in Food Science (pp. 687–700). Academic Press. https://doi.org/10.1016/B978-0-443-26468-9.00048-5

Rodrigues, G. de A., Junqueira, L., Vieira, J. G. V., Silva Júnior, O. S. da, & Cunha, C. B. da. (2025). Rural last-mile distribution using a hybrid heuristic-multi-criteria decision-making framework. Expert Systems with Applications, 289, 128331. https://doi.org/10.1016/j.eswa.2025.128331

Saisud, D., & Kongkaew, W. (2026). A hybrid CORELAP-association rules and fuzzy AHP framework for plant layout optimization in the para rubber plywood industry. Expert Systems with Applications, 295, 128766. https://doi.org/10.1016/j.eswa.2025.128766

Sharifi, M., Taghipour, S., Abhari, A., & Rysz, M. (2026). A novel mathematical model for the scheduling of a zero inventory production: An application of process scheduling in fog computing. Computers & Operations Research, 185, 107284. https://doi.org/10.1016/j.cor.2025.107284

Temizöz, T., Imdahl, C., Dijkman, R., Lamghari-Idrissi, D., & van Jaarsveld, W. (2025). Deep Controlled Learning for Inventory Control. European Journal of Operational Research, 324(1), 104–117. https://doi.org/10.1016/j.ejor.2025.01.026

Tolio, T., Magnanini, M. C., Gatti, G., Grieco, A., & Caricato, P. (2025). A resilient optimization methodology for integrated workforce scheduling and system configuration in manufacturing. 11th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2025, 59(10), 1832–1837. https://doi.org/10.1016/j.ifacol.2025.09.308

Touzout, F. A., Ladier, A.-L., & Hadj-Hamou, K. (2026). Comparing four MILP formulations for the time-dependent inventory routing problem with FIFO-compliant travel times. Computers & Operations Research, 194, 107577. https://doi.org/10.1016/j.cor.2026.107577

Verma, A., & Samanta, S. K. (2026). Analytical and simulation studies of GI/M/1 queue integrated with an (s,Q) inventory policy. Mathematics and Computers in Simulation, 246, 591–620. https://doi.org/10.1016/j.matcom.2025.09.032

Vogt, T., Lowery, B., Sachs, A.-L., & Thonemann, U. W. (2026). Inventory control and picking behavior: The roles of sustainability messages and price discounts. European Journal of Operational Research, 331(1), 156–169. https://doi.org/10.1016/j.ejor.2025.09.028

Wang, S., Kua, J., Jin, J., Wong, Y. W., Jayaraman, P. P., & Pang, Z. (2026). Assisting mission-critical traffic flows with Active Queue Management in Industrial Internet of Things. Journal of Industrial Information Integration, 53, 101170. https://doi.org/10.1016/j.jii.2026.101170

Yu, V. F., Salsabila, N. Y., Gunawan, A., & Siswanto, N. (2025). A three-stage matheuristic for the blood stochastic inventory routing problem. Transportation Research Part E: Logistics and Transportation Review, 200, 104143. https://doi.org/10.1016/j.tre.2025.104143

Zhao, X., Jia, P., Li, H., & Si, R. (2025). Allocating carbon emissions from crude oil tanker shipping: Full voyage lifecycle perspective. Transportation Research Part D: Transport and Environment, 147, 104919. https://doi.org/10.1016/j.trd.2025.104919

Authors

Regina Luntungan
reginaluntungan74@gmail.com (Primary Contact)
Sintia Nurani Korompis
Elisabeth Deisi Malonda
Noah Smith
Luntungan, R., Korompis, S. N., Malonda, E. D., & Smith, N. (2026). IMPROVING DISTRIBUTION INVENTORY CONTROL THROUGH FIFO AND COSO FRAMEWORK AT PT. MITRA KENCANA DISTRIBUSINDO. Journal Markcount Finance, 4(4), 299–316. https://doi.org/10.70177/jmf.v4i4.4181

Article Details

Similar Articles

1 2 > >> 

You may also start an advanced similarity search for this article.