Citation :

Saikrishna Tarakampet, Subhash Tatavarthi, "Transforming Asset Management with Predictive Enterprise Applications: A Case Study Approach," International Journal of Computer Trends and Technology (IJCTT), vol. 74, no. 2, pp. 30-37, 2026. Crossref, https://doi.org/10.14445/22312803/IJCTT-V74I2P105

Abstract

To manage assets effectively, companies must also manage how efficiently they operate, minimize the cost involved with doing business, and be able to deliver services consistently and reliably [1]. Many companies depend on disparate systems with limited functionality while still relying heavily on manual processes for maintaining and tracking their asset inventory [2]. This often results in unplanned and excessive legal liability due to niche practices that create opportunities to operate outside the scope of their license agreements, thereby increasing their total costs [3]. In this paper we introduce a comprehensive architecture for building an Enterprise Asset Management (EAM) solution that would integrate the IT Asset Management (ITAM) module of ServiceNow [4], build on a single database to track and manage all assets - including hardware, software, cloud, Internet of Things - and incorporate a fully automated process to orchestrate the entire life cycle of an asset from its creation through to its endpoint. The authors present a case study demonstrating a 25% reduction in downtime of assets for a real-world customer after implementing our solution [8]. A predictive optimization model that utilizes telemetry and predictive machine Learning capabilities [7] is introduced as a way of anticipating asset maintenance requirements and enabling proactive response after identifying the requirement for maintenance. This approach creates an evolving infrastructure enabling improved asset management methodologies well into the future [9].

Keywords

Asset Management, ITAM, CMDB, Predictive Maintenance, ServiceNow, Asset Lifecycle Management, Enterprise Systems.

References

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