Reducing Network Downtime with AI-Powered Predictive Maintenance

Background

GlobalConnect Telecom is a leading Tier-1 carrier providing services to over 20 million customers across multiple countries. With a vast network infrastructure spanning thousands of kilometers of fiber and hundreds of network nodes, maintaining optimal network performance is critical to their business.

The Challenge

Prior to implementing NeuroOSS, GlobalConnect faced several significant challenges:

• Frequent unplanned network outages resulting in service disruptions
• Reactive maintenance approach that was both costly and inefficient
• SLA violations leading to financial penalties and customer dissatisfaction
• Limited visibility into network health and potential failure points
• Manual processes for identifying and resolving network issues

The Solution

After evaluating several options, GlobalConnect selected NeuroOSS for its advanced AI capabilities and comprehensive network management features. The implementation included:

• Deployment of NeuroOSS's AI-powered predictive maintenance module across all network elements
• Integration with existing OSS/BSS systems to create a unified operational view
• Implementation of real-time monitoring with automated alerts and escalations
• Training of the ML models using historical network data to identify failure patterns
• Configuration of the GenAI assistant to provide troubleshooting guidance to network engineers

The Results

Within six months of implementation, GlobalConnect achieved remarkable results:

• 94% reduction in unplanned downtime, with the AI system successfully predicting and preventing hundreds of potential failures
• $3.2M annual savings from reduced maintenance costs and elimination of SLA penalties
• ROI achieved in just 4 months, significantly faster than the projected 12-month timeline
• Network reliability improved from 99.9% to 99.999%, representing a 100-fold reduction in downtime
• 65% reduction in mean time to repair (MTTR) for issues that did occur, thanks to the GenAI assistant's guidance

Implementation Process

The implementation was completed in phases over a 3-month period:

1. Phase 1: System integration and data collection (4 weeks)
2. Phase 2: ML model training and validation (6 weeks)
3. Phase 3: Deployment and operational handover (2 weeks)

Key Factors for Success

• Strong executive sponsorship and clear project governance
• Comprehensive training program for network operations staff
• Phased implementation approach with clearly defined success metrics
• Continuous refinement of ML models based on operational feedback

Future Plans

Based on the success of the initial implementation, GlobalConnect is now expanding their use of NeuroOSS to include:

• 5G network slice management for enterprise customers
• Automated capacity planning using AI-driven traffic forecasting
• Integration of the digital twin simulator for network change validation