Automated Fixed Asset Management System with Predictive Analytics

Neil Vincent D.  Alvior; Jake R.  Pomperada

doi:10.52006/main.v8i2.1344

Neil Vincent D. Alvior University of Negros Occidental-Recoletos, Bacolod City, Philippines https://orcid.org/0009-0006-6881-5769
Jake R. Pomperada University of Negros Occidental-Recoletos, Bacolod City, Philippines

DOI: https://doi.org/10.52006/main.v8i2.1344

Keywords: science and technology, auditing of fixed assets, system development and testing, asset management practices, forecasting the depreciation of an office’s fixed assets, Philippines

Abstract

To address the inefficiencies of manual data entry, this study developed an Automated Fixed Asset Management System with Predictive Analytics to modernize organizational asset tracking. Built using Laravel, the system integrates QR code technology for real-time tracking and employs Linear Regression and Random Forest algorithms to forecast maintenance needs and asset depreciation. An evaluation involving 12 respondents from Yusay Credit & Finance Corporation yielded an "Excellent" mean score of 4.60, confirming the system’s usability, reliability, and offline capability. The findings demonstrate that the system effectively replaces traditional methods by enhancing operational efficiency, data security, and resource allocation, offering a scalable solution for SMEs. Future research is recommended to focus on integrating procurement systems, maintaining models with live data, and exploring blockchain for enhanced transparency.

Automated Fixed Asset Management System with Predictive Analytics

Abstract

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