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International Journal of Science, Strategic Management and Technology

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ISSN: 3108-1762 (Online)
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INVENTRA: A WEB-BASED INVENTORY MANAGEMENT SYSTEM USING FRONTEND TECHNOLOGIES

AUTHORS:
Rethika V
Sri dharani D
Mentor
Dr. T.R. Nisha Dayana
Affiliation
Department of Computer Science and Information Technology VELS Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

Inventory management is a critical business function ensuring that the right products are available at the right time. Traditional manual methods are labour-intensive, error-prone, and inefficient, particularly in medium to large-scale environments. This paper presents Inventra—a modern, web-based inventory management system developed entirely using HTML5, CSS3, and JavaScript (ES6+). The system provides a responsive, dashboard-driven interface enabling businesses to manage products, track stock movements, monitor low-stock alerts, generate analytical reports, and manage user roles from any device through a standard web browser, without server-side installation. Six core modules are implemented: Product Management, Stock In/Out Transactions, Alert Management, Report Generation and Analytics, User and Role Management, and an interactive Dashboard Overview. Chart.js is leveraged for dynamic data visualisation, Material Symbols for iconography, and a CSS variable system enforces a cohesive design language throughout. The system demonstrates a practical, accessible, and scalable approach to modernising inventory operations through pure frontend technologies.

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V, R. & D, S. D. (2026). Inventra: A Web-Based Inventory Management System using Frontend Technologies. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i4.646

V, Rethika, and Sri D. "Inventra: A Web-Based Inventory Management System using Frontend Technologies." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.646.

V, Rethika, and Sri D. "Inventra: A Web-Based Inventory Management System using Frontend Technologies." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.646.

References
[1] X. Yin, X. Li, Q. Gao, and H. Li, "Inventory defect detection algorithm based on convolutional neural network," Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6071–6079, 2019.

[2] A. Kumari, P. Ashrani, M. Basha, and B. S. A. Kumar, "An approach for identifying and tracking inventory items using computer vision and sensor technologies," in Proc. IEEE ICMLA, 2023.

[3] M. Ravi, K. M. Parvesh Ahmed, and P. Sengottaiyan, "IoT based inventory status monitoring and alert system," in Proc. IEEE ICIRD, 2023.

[4] Y. Liang, L. Chen, and B. Xu, "Design of intelligent management system for inventory control," in Proc. Int. Conf. Smart Systems and Energy Management, 2022.

[5] X. Zhang, Z. Wang, H. Yu, M. Liu, and B. Xing, "Research on visual inspection technology in automatic assembly," IEEE Access, vol. 10, pp. 12340–12351, 2022.

[6] K. Thakur, A. Adhya, and C. Bajpai, "Inventory management using image processing and data analytics," in Proc. IEEE ICCMC, 2021, pp. 516–519.

[7] H. S. Tasin, M. S. Sarkar, and M. A. Rahman, "Design of automated detection system for out-of-stock items," in Proc. IEEE ICCCNT, 2021.

[8] L. Qing, K. Yang, W. Tan, and J. Li, "Automated detection of inventory using deep learning," IEEE Transactions on Industrial Electronics, vol. 68, no. 5, pp. 4321–4330, 2020.

[9] U. Andrijasevic, J. Kocic, and V. Nesic, "Inventory status detection using recurrent neural networks," in Proc. IEEE ICET, 2020.

[10] V. Vishnani, A. Adhya, and C. Bajpai, "Inventory detection using image processing," in Proc. IEEE ICARCV, 2020.
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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