Subex Capacity Management

With intense competition and ever-increasing Capex intensity, CSPs require a superior capacity management solution to equip them with:

• Intelligent network investment plans
• End-to-end predictive capacity analytics
• Proactive customer experience analytics
• Capital and operational cost optimization

Built on advanced analytics models coupled with strong telecom domain expertise, Subex's Capacity Management helps CSPs plan and optimize their network to provide the best customer experience.

In today’s highly competitive ecosystem, CSPs need to deal with growing Capex intensity, coupled with the fact that revenues are plateauing. At the same time, customer expectations and network usage are on the rise, adding further pressure to CSPs who also have to cope with constant technology evolution (towards LTE-advanced pro, 5G, and IoT). Intelligent capacity management is the best way to reap expected returns from LTE-advanced, 5G, IoT, and other nascent network technologies.

As Subex, we implement capacity management in three ways:

1. Network Investment Planning

CSPs need accurate, timely, and intelligent network investment decisions to ensure Capex is spent optimally and yields maximum ROI. Network Investment Planning leverages intelligent data management and domain-driven data science capabilities to:

• Accurately forecast network growth for capacity enhancement, optimization, and augmentation
• Specify when, where and with what to invest in a multi-vendor and multi-technology ecosystem
• Drive prioritized network investment decisions from technical, financial and customer experience metrics
• Run what-if simulations to accommodate the unknown and unforeseen in any business plan

2. Smart Capacity Analytics

In a multi-vendor and multi-technology network, stakeholders need to take action to augment the capacity and improve the quality of the network. What CSPs need today is a centralized feature to get insights into the impact of such changes on network capacity, run configuration consistency checks concerning baseline value, and drive acceptance process for new service and technology planning.

Smart Capacity Analytics automates network capacity optimization expertise and leverages various machine learning techniques to bring significant enhancement in operational efficiency. It equips CSPs to:

• Derive insights into the current state of network elements and perform root cause analysis of worst offenders
• Drive end-to-end capacity performance improvement
• Seamlessly manage the performance acceptance process in a multi-vendor ecosystem

3. Customer Experience Analytics

CSPs are continually challenged to meet the ever-rising expectations of their customers. Customer Experience Analytics is an end-to-end Quality-of-Experience (QoE) analytics solution that embraces this challenge. It leverages advanced statistical and machine learning models coupled with strong visualization capabilities, enabling CSPs to:

• Proactively identify network bottlenecks and issues impacting QoE
• Troubleshoot issues from a holistic perspective
• Understand the impact of the network on High-Value Customers (HVCs)
• Identify critical hotspots for capacity augmentation
• Derive critical insights in the form of geo-analytics dashboards depicting mobile device data correlated with network performance

As the telecom industry prepares to move to the 5G, it will look forward to the benefits that come with the technology such as faster speeds, low latencies, seamless connectivity, besides the possibilities of opening new business revenue streams for CSPs. 5G will bring with more unique use cases in areas beyond the consumer realm, with enterprise being a key focus area and a new point of entry for CSPs. However, while 5G does hold this promise leading to CSPs gearing up for this new technology, are they getting 4G right? Are their existing tools for 4G Capacity Planning and Network Capacity Optimization helping them make the most out of their 4G network investments done in the past?

Most CSPs may not have a standard 4G Network Capacity Management tool in the first place, and those that do, may not be able to realize the return on their network investments recommendations generated in the past. One potential reason could be the limitations that come with their existing tool to ingest, process, and correlate data flowing from different domains.

Another could be their current Network Capacity Management Process does not offer flexibility to network planners to run multiple iterations and simulations before zeroing on a final plan. Along with it, network capacity planning best practices often get lost because of lack of capability of a network planning tool to take feedback from past actions and record it for future usage.

Traditionally, network capacity management metrics focus on technical KPIs as the only set of parameters used for network capacity planning. Though the approach worked fine a few years back, the drawback of this method is that the network capacity planning process outcome only generates a list of network elements required for capacity upgrade and augmentation. It doesn’t provide any priority-based investment decisions which can help a CSP invest in the areas capable of generating quick revenue while ensuring the customer experience is not compromised. All this can be achieved only with advanced analytics-driven network capacity and performance management systems built on Artificial Intelligence and Machine Learning. Such systems would be capable of providing meaningful insights from not just technical KPIs but millions of records flowing in the form of business and customer experience metrics, which otherwise gets missed.