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Optimizing Asset Utilization through Intelligent Asset Allocation Management Systems

Mr. Shreyas Vinay Dhale1

Prof. Pritish Bisne1

1Trinity Academy of Engineering, Pune, India

 

Abstract

Effective asset tracking and management are critical for organizations to optimize resource utilization, re- duce operational costs, and enhance decision-making. Traditional asset management methods, reliant on manual processes or barcode systems, often suffer from inefficiencies, errors, and lack of real-time visibility. This paper proposes an innovative framework integrating Internet of Things (IoT), Radio Frequency Identification (RFID), and Artificial Intelligence (AI) to enable real-time asset tracking, predictive maintenance, and data-driven opti- mization. Through a mixed-methods approach, including a systematic literature review, stakeholder interviews, and simulation-based analysis, the study addresses challenges such as asset misplacement, underutilization, and data silos. The proposed system leverages IoT sensors, RFID tags, and AI algorithms to achieve 98% tracking accuracy, 25% improved asset utilization, and 30% reduced maintenance downtime. Case studies across man- ufacturing, healthcare, and logistics demonstrate the framework’s versatility. This research offers a scalable, secure, and technology-driven solution for modern asset management, contributing to operational excellence and sustainability.

Keywords: Asset Tracking, Asset Management, Internet of Things, RFID, Artificial Intelligence, Real-Time Monitoring, Predictive Maintenance

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