Associate professor, Department of Mathematics Ramlubhai Shani Rajkiya Mahila Mahavidhyalay, Piliphit, UP
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An Analysis of the Value of an Efficient Inventory Management System
International Journal of Physics, Chemistry, Mathematics and Biology International Open Access, Peer-reviewed, Refereed Journal
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DOI:
10.65919/ijpcmb.2026.v2i2001
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Abstract
The idea of inventory control systems and their significance in guaranteeing efficient business operations are examined in this study article. The paper describes how businesses use inventory systems to control stock levels, cut waste, save operating expenses, and increase customer satisfaction using secondary data from textbooks, journals, case studies, and online resources. The results show that an effective inventory control system improves decision-making, boosts profitability, avoids stock-outs, and guaranties accurate record-keeping. The study also emphasizes how technology, such automated tracking tools, ERP systems, and barcoding, may increase inventory accuracy.
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Received
25 March 2026
Accepted
08 May 2026
Published
30 June 2026
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© 2026 The Author(s). The author(s) retain copyright and grant the journal the right of first publication. This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License .
How to Cite
Dr. Ravendra kumar (2026). An Analysis of the Value of an Efficient Inventory Management System. International Journal of Physics, Chemistry, Mathematics and Biology, 2(2), 1-8. https://doi.org/10.65919/ijpcmb.2026.v2i2001
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No external funding information has been declared unless stated in the published PDF.
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The authors declare no conflict of interest unless otherwise stated in the article.
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References
Showing first 3 references. Click “Show All References” to view complete list.
- 1. Albayrak Ăśnal, Ă–., Erkayman, B., & Usanmaz, B. (2023). Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-022-09879-5 ResearchGate
- 2. Mashayekhy, Y., Babaei, A., Yuan, X.-M., & Xue, A. (2022). Impact of Internet of Things (IoT) on Inventory Management: A Literature Survey. Logistics, 6(2), 33. https://doi.org/10.3390/logistics6020033 MDPI
- 3. Villegas-Ch, W., Maldonado Navarro, A., & Sanchez-Viteri, S. (2024). Optimization of inventory management through computer vision and machine learning technologies. Intelligent Systems with Applications, 24, 200438. https://doi.org/10.1016/j.iswa.2024.200438 ScienceDirect
- 4. Sbai, N., et al. (2023). Simulation-Based Approach for Multi-Echelon Inventory System Selection: Case of Distribution Systems. Processes, 11(3), 796. https://www.mdpi.com/2227-9717/11/3/796 MDPI
- 5. Geevers, K., et al. (2024). Multi-echelon inventory optimization using deep reinforcement learning. Central European Journal of Operations Research. https://doi.org/10.1007/s10100-023-00872-2 SpringerLink
- 6. Gioia, D. G., & Minner, S. (2023). On the value of multi-echelon inventory management strategies for perishable items with on-/off-line channels. Transportation Research Part E: Logistics and Transportation Review, 178, 103354.
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