Optimization of Supply Chain Processes in the Retail Sector: A Data-Driven Simulation Approach for Inventory Management

Haris Ihsanil  Huda; Haryo  Kusumo; Eni  Endaryati

doi:10.51903/jmi.v3i1.47

Authors

Haris Ihsanil Huda Universitas Sains dan Teknologi Komputer
Haryo Kusumo Universitas Sains dan Teknologi Komputer
Eni Endaryati Universitas Sains dan Teknologi Komputer

DOI:

https://doi.org/10.51903/jmi.v3i1.47

Keywords:

Retail Sector , Inventory Management , Simulation Techniques

Abstract

Efficient supply chain management in the retail sector is crucial for cost reduction and enhancing customer satisfaction. This study analyzes and optimizes inventory management using a data-driven simulation approach. By leveraging data on customer demand patterns, replenishment cycles, and delivery times, the research develops simulation models to evaluate inventory strategies under various scenarios, including high demand and seasonal fluctuations. The findings reveal optimal inventory strategies that minimize holding costs while maintaining product availability. Key performance metrics, such as fill rate and stockout levels, show significant improvements when simulation-based strategies are implemented. For example, under high-demand scenarios, the proposed model achieved a 20% reduction in holding costs and a 15% increase in fill rates compared to traditional methods. These results highlight the importance of integrating simulation techniques into retail supply chain management to enhance decision-making and operational efficiency. This research contributes to the literature by providing a practical framework for inventory optimization and offers actionable recommendations for retail managers to adopt technology-driven approaches. Future research should explore applying these methods to retail sectors with diverse demand characteristics.

References

Abaku, E. A., Edunjobi, T. E., & Odimarha, A. C. (2024). Theoretical approaches to AI in supply chain optimization: Pathways to efficiency and resilience. International Journal of Science and Technology Research Archive, 6(1), 92–107.

Alghamdi, O. A., & Agag, G. (2024). Competitive advantage: A longitudinal analysis of the roles of data-driven innovation capabilities, marketing agility, and market turbulence. Journal of Retailing and Consumer Services, 76. https://doi.org/10.1016/j.jretconser.2023.103547

Almazmomi, N., Ilmudeen, A., & Qaffas, A. A. (2022). The impact of business analytics capability on data-driven culture and exploration: achieving a competitive advantage. Benchmarking, 29(4), 1264–1283. https://doi.org/10.1108/BIJ-01-2021-0021

Babayev, F., & Balajayeva, T. (2023). Ways of Increasing the Competitiveness of Food Industry Enterprises. International Journal of Innovative Technologies in Economy, 4(44). https://doi.org/10.31435/rsglobal_ijite/30122023/8069

Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Annals of Operations Research, 333(2–3), 627–652. https://doi.org/10.1007/s10479-021-03956-x

Deiva Ganesh, A., & Kalpana, P. (2022). Future of artificial intelligence and its influence on supply chain risk management – A systematic review. In Computers and Industrial Engineering (Vol. 169). https://doi.org/10.1016/j.cie.2022.108206

Feng, Y., Lai, K. hung, & Zhu, Q. (2022). Green supply chain innovation: Emergence, adoption, and challenges. International Journal of Production Economics, 248. https://doi.org/10.1016/j.ijpe.2022.108497

García-Cutrín, J., & Rodríguez-García, C. (2024). Enhancing Corporate Sustainability through Just-In-Time (JIT) Practices: A Meta-Analytic Examination of Financial Performance Outcomes. Sustainability (Switzerland) , 16(10). https://doi.org/10.3390/su16104025

Hugo A. DeCampos, Claudia R. Rosales, & Sriram Narayanan. (2022). Supply chain horizontal complexity and the moderating impact of inventory turns: A study of the automotive component industry. International Journal of Production Economics, 245.

Ikevuje, A. H., Anaba, D. C., & Iheanyichukwu, U. T. (2024). Optimizing supply chain operations using IoT devices and data analytics for improved efficiency. Magna Scientia Advanced Research and Reviews, 11(2), 70–79.

Joel, O. T., & Oguanobi, V. U. (2024). Data-driven strategies for business expansion: Utilizing predictive analytics for enhanced profitability and opportunity identification. International Journal of Frontiers in Engineering and Technology Research, 6(02), 71–81.

Kayikci, Y., Demir, S., Mangla, S. K., Subramanian, N., & Koc, B. (2022). Data-driven optimal dynamic pricing strategy for reducing perishable food waste at retailers. Journal of Cleaner Production, 344. https://doi.org/10.1016/j.jclepro.2022.131068

Khedr, A. M., & S, S. R. (2024). Enhancing supply chain management with deep learning and machine learning techniques: A review. Journal of Open Innovation: Technology, Market, and Complexity, 10(4). https://doi.org/10.1016/j.joitmc.2024.100379

Kolasani, S. (2023). Blockchain-driven Supply Chain Innovations and Advancement in Manufacturing and Retail industries. Transactions on Latest Trends in IoT, 6(6), 1–26.

Kumar, A., Singh, R. K., & Modgil, S. (2023). Influence of data-driven supply chain quality management on organizational performance: evidences from retail industry. TQM Journal, 35(1), 24–50. https://doi.org/10.1108/TQM-06-2020-0146

Kurdi, B. Al, Alzoubi, H. M., Akour, I., & Alshurideh, M. T. (2022). The effect of blockchain and smart inventory system on supply chain performance: Empirical evidence from retail industry. Uncertain Supply Chain Management, 10(4), 1111–1116. https://doi.org/10.5267/j.uscm.2022.9.001

Lăzăroiu, G., Androniceanu, A., Grecu, I., Grecu, G., & Neguriță, O. (2022). Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing. Oeconomia Copernicana, 13(4), 1047–1080. https://doi.org/10.24136/oc.2022.030

Li, J., & Liu, X. W. (2024). An agent-based simulation model for analyzing and optimizing omni-channel retailing operation decisions. Journal of Retailing and Consumer Services, 79. https://doi.org/10.1016/j.jretconser.2024.103845

Nagy-Bota, S., & Moldovan, L. (2022). Key Differences and Common Aspects of Logistics and Supply Chain Management. Acta Marisiensis. Seria Technologica, 19(1), 42–46. https://doi.org/10.2478/amset-2022-0008

Nguyen, D. T., Adulyasak, Y., Cordeau, J. F., & Ponce, S. I. (2022). Data-driven operations and supply chain management: established research clusters from 2000 to early 2020. International Journal of Production Research, 60(17), 5407–5431. https://doi.org/10.1080/00207543.2021.1956695

Ooi, K. B., Tan, G. W. H., Al-Emran, M., Al-Sharafi, M. A., Arpaci, I., Zaidan, A. A., Lee, V. H., Wong, L. W., Deveci, M., & Iranmanesh, M. (2024). The Metaverse in Engineering Management: Overview, Opportunities, Challenges, and Future Research Agenda. IEEE Transactions on Engineering Management, 71, 13882–13889. https://doi.org/10.1109/TEM.2023.3307562

Ouda, E., Sleptchenko, A., & Simsekler, M. C. E. (2023). Comprehensive review and future research agenda on discrete-event simulation and agent-based simulation of emergency departments. In Simulation Modelling Practice and Theory (Vol. 129). https://doi.org/10.1016/j.simpat.2023.102823

Oyedijo, A., Kusi-Sarpong, S., Mubarik, M. S., Khan, S. A., & Utulu, K. (2024). Multi-tier sustainable supply chain management: a case study of a global food retailer. Supply Chain Management, 29(1), 68–97. https://doi.org/10.1108/SCM-05-2022-0205

Pasupuleti, V., Thuraka, B., Kodete, C. S., & Malisetty, S. (2024). Enhancing Supply Chain Agility and Sustainability through Machine Learning: Optimization Techniques for Logistics and Inventory Management. Logistics, 8(3), 73. https://doi.org/10.3390/logistics8030073

Patrick Azuka Okeleke, Daniel Ajiga, Samuel Olaoluwa Folorunsho, & Chinedu Ezeigweneme. (2024). Predictive analytics for market trends using AI: A study in consumer behavior. International Journal of Engineering Research Updates, 7(1), 036–049. https://doi.org/10.53430/ijeru.2024.7.1.0032

Ritu Khanna, K.A. Patel, & Patel Nirmal Rajnikant. (2023). Review Presentation of Different Economic Order Quantity (EOQ) Models and Their Application. Journal of Advanced Zoology, 44(S-5), 1284–1288. https://doi.org/10.17762/jaz.v44is-5.1210

Sakai, T., Hara, Y., Seshadri, R., Alho, A. R., Hasnine, M. S., Jing, P., Chua, Z. Y., & Ben-Akiva, M. (2022). Household-based E-commerce demand modeling for an agent-based urban transportation simulation platform. Transportation Planning and Technology, 45(2), 179–201. https://doi.org/10.1080/03081060.2022.2084397

Shoomal, A., Jahanbakht, M., Componation, P. J., & Ozay, D. (2024). Enhancing supply chain resilience and efficiency through internet of things integration: Challenges and opportunities. Internet of Things (Netherlands), 27. https://doi.org/10.1016/j.iot.2024.101324

Stieler, D., Schwinn, T., Leder, S., Maierhofer, M., Kannenberg, F., & Menges, A. (2022). Agent-based modeling and simulation in architecture. In Automation in Construction (Vol. 141). https://doi.org/10.1016/j.autcon.2022.104426

Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3. https://doi.org/10.1016/j.sca.2023.100026

Toluwalase Vanessa Iyelolu, Edith Ebele Agu, Courage Idemudia, & Tochukwu Ignatius Ijomah. (2024). Driving SME innovation with AI solutions: overcoming adoption barriers and future growth opportunities. International Journal of Science and Technology Research Archive, 7(1), 036–054. https://doi.org/10.53771/ijstra.2024.7.1.0055

Yang, L., & Shami, A. (2022). IoT data analytics in dynamic environments: From an automated machine learning perspective. Engineering Applications of Artificial Intelligence, 116. https://doi.org/10.1016/j.engappai.2022.105366