Automated Fixed Asset Management System with Predictive Analytics

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

Analytics · Asset management · Big data · Data modeling · IT asset management · Predictive analytics · Predictive modelling · Procurement · Scalability · Big Data and Business Intelligence · Financial Distress and Bankruptcy Prediction · Organizational and Employee Performance