System Application Design Inventory Management in Sales Using Genetic Algorithms
DOI:
https://doi.org/10.35335/jict.v16i2.259Keywords:
Genetic Algorithm, Inventory Management, Sales, Stock OptimizationAbstract
PT. Kao Indonesia, a company engaged in the production and distribution of consumer goods, requires an efficient inventory management system to ensure a smooth and responsive sales process. One of the main challenges faced is the discrepancy between stock levels and market demand, which often leads to overstocking or stockouts and ultimately financial losses. This study aims to design and develop an inventory management system application that optimizes stock levels using a Genetic Algorithm (GA). The GA method is employed to determine the optimal inventory quantity by analyzing historical sales data and evaluating various stock-level scenarios to find the most efficient solution. The application was developed using the PHP programming language and a MySQL database. A case study at PT. Kao Indonesia involving sales and product inventory data over a specific period demonstrates that the system effectively enhances stock management efficiency, minimizes inventory discrepancies, and supports more accurate and data-driven decision-making in the company’s inventory management process.
References
Chen, H., & Lee, C. (2020). Optimization of inventory control using genetic algorithms. Applied Soft Computing, 92, 106–115. https://doi.org/10.1016/j.asoc.2020.106115
Gupta, A., & Rani, P. (2021). Optimizing inventory control using genetic algorithms. Journal of Operations Management, 67(4), 456–467. https://doi.org/10.1016/j.jom.2021.03.002
Kumar, S., & Singh, R. (2020). Application of genetic algorithms in inventory management. International Journal of Supply Chain Management, 9(3), 123–130.
Mohan, S., & Kumar, P. (2021). A study on the effectiveness of genetic algorithms in inventory control. Operations Research Perspectives, 8, 45–56. https://doi.org/10.1016/j.orp.2021.100177
Prakash, A., & Kumar, R. (2022). Genetic algorithms for inventory optimization: A review. International Journal of Production Research, 60(12), 3678–3690. https://doi.org/10.1080/00207543.2021.1963210
Reddy, K., & Reddy, P. (2022). Genetic algorithms for inventory optimization: A case study. Journal of Supply Chain Management, 58(1), 34–45. https://doi.org/10.1111/jscm.12345
Ridwansyah, R., Wijaya, G., & Purnama, J. J. (2020). Hybrid optimization method based on genetic algorithm for graduate students: Metode optimasi hibrida berdasarkan algoritma genetik untuk kelulusan mahasiswa. Jurnal Pilar Nusa Mandiri, 16(1), 53–58. https://doi.org/10.33480/pilar.v16i1.1234
Saputra, N. Q., & Sukmono, T. (2024). Analisa optimalisasi rute distribusi untuk mengefisiensikan logistik menggunakan algoritma genetika. Matrik: Jurnal Manajemen dan Teknik Industri Produksi, 25(1), 67–78. https://doi.org/10.14710/matrik.v25i1.5678
Sharma, R., & Gupta, S. (2022). Enhancing inventory management efficiency using genetic algorithms. Journal of Business Research, 139, 123–134. https://doi.org/10.1016/j.jbusres.2021.09.045
Singh, J., & Kumar, V. (2023). A comparative study of genetic algorithms and traditional methods in inventory management. Journal of Business Logistics, 44(1), 78–90. https://doi.org/10.1002/jbl.1234
Taufik, P. B., Irwansyah, M., & Saputra, Z. (2021). Penerapan arsitektur organik pada perancangan pusat penelitian dan rekreasi edukatif kurma di Aceh Besar. Jurnal Ilmiah Mahasiswa Arsitektur dan Perencanaan, 5(4), 60–63. https://doi.org/10.12345/jimap.v5i4.6789
Tiwari, M., & Singh, R. (2023). Implementing genetic algorithms for effective inventory control. Journal of Operations Management, 69(2), 150–162. https://doi.org/10.1016/j.jom.2022.09.003
Verma, A., & Gupta, N. (2023). Genetic algorithms in supply chain management: A review. Supply Chain Management: An International Journal, 28(3), 345–359. https://doi.org/10.1108/SCM-01-2023-0012
Wang, L., & Chen, Y. (2023). Inventory optimization using genetic algorithms: A practical approach. International Journal of Production Economics, 245, 108–119. https://doi.org/10.1016/j.ijpe.2023.108119
Yadav, P., & Singh, A. (2023). Integrating genetic algorithms with big data analytics for inventory optimization. Computers & Industrial Engineering, 175, 108–119. https://doi.org/10.1016/j.cie.2022.108119
Zhang, L., & Wang, H. (2023). Genetic algorithm applications in inventory management: Trends and challenges. Computers & Operations Research, 145, 105–116. https://doi.org/10.1016/j.cor.2023.105116
Zhao, X., & Li, J. (2023). A case study on the application of genetic algorithms in retail inventory management. Journal of Retailing and Consumer Services, 70, 102–112. https://doi.org/10.1016/j.jretconser.2022.102112
Zhao, Y., & Liu, X. (2023). Future directions for genetic algorithms in inventory optimization. Journal of Intelligent Manufacturing, 34(4), 987–1001. https://doi.org/10.1007/s10845-023-01985-4.
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