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Unified Inventory

Discover a new approach to modern telecom inventory management for autonomous networks

What is telecom inventory management?

Telecom inventory management relies on solutions that provide a complete database to track detailed information about physical and logical network components, including, for example, cell towers, cables, switches, routers, antennas and network devices.

What are the requirements for telecom inventory management for autonomous networks?

Modern inventory solutions need to leverage a data-mesh approach to map end-to-end services to the physical network. This approach enables root cause analysis (RCA), domain stitching and correlation analysis for software configurations, network connections and relationships between elements.  

You can use this type of inventory system for data aggregation, normalization and as part of a network observability fabric. It serves as a single source of truth for your operational support systems (OSS) and business applications.

Today, communications service providers (CSPs) face the challenge of having to shrink operating costs while implementing new services faster and boosting revenues at the same time. Automation is critical to achieving these objectives. A precise and dynamic inventory system forms the foundation for effective automation by leveraging a deep understanding of network and services coupled with real-time insights.

What are the key benefits of modern inventory solutions for autonomous networks?

Observability

Through Observability, the inventory solution can discover existing and new network elements (physical, virtual or containerized) in a highly dynamic environment. The knowledge about relational connections and graphs provides the foundation for RCA and enables you to leverage machine learning (ML) platforms and other automation tools.

Normalization

With data aggregation, normalization and labeling, you can efficiently link the inventory solution to northbound systems. Normalization also enables multi-domain and multi-vendor stitching and the creation of service abstraction. And by defining relationships between physical elements and logical services it’s possible to identify the effects of a failure and to perform service impact analysis (SIA).

Optimization

A modern inventory solution provides insights that can be used to recommend the most efficient routes, energy and spectrum usage. ML tools enable pattern recognition among similar services, facilitating suggestions for the best route or service path based on prior service utilization. When integrated with intent-based orchestration, you benefit from a highly intelligent system capable of autonomously defining services and KPIs.

Digital twins

Modern inventory solutions are also a key element of network digital twins, which emulate network elements and their behavior. This improves SIA and, when combined with RCA, gives you insights into the future state of the network. Network digital twins also help you to identify critical resources for simulation and planning purposes.

Flexibility

It’s essential for inventory systems to work and integrate with systems from multiple vendors while enabling CSPs to create their own models, adapters and configurations. You can rely on our software development kits to achieve this flexibility. 

What is Nokia’s telecom inventory management solution?

The Nokia Unified Inventory solution provides all necessary information about services and service assets in near real-time, enabling modern operational functions like MLOps, AIOps, service orchestration and assurance. Better data integrity also helps you to improve your network automation efficiency and effectiveness.

Unified Inventory is an integral part of several Nokia products and solutions:
 

Nokia’s award-winning Digital Operations software is used by over 200 customers globally to design, delivering and assure services at scale and with speed. 

Case studies

Interested in learning how Nokia’s Orchestration Center has helped service providers around the globe? Find out about the lessons learned from real-world deployments in our case studies. 

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