Why network operators are taking a localised view to planning and optimising their networks

network planning and optimisation

Network planning and optimisation has never been more important within the telecoms industry; new competition, shrinking profits and higher customer demand means operators need to improve their service performance, whilst at the same time minimising their expenditure. This is driving new approaches to planning and optimisation, in which network engineers are utilising more diverse data to make operational decisions with the greatest impact. Network operators are beginning to now get answers to traditionally difficult questions such as “What experience do my enterprise customers really have?” or “What is the true value of existing and new cell tower sites?”


The problem with traditional network planning and optimisation


Traditionally, network planning and optimisation teams have used network performance statistics and network equipment faults to understand the networks behaviour, and plan their future investments. However, by using this information in isolation, engineers are blinded to the subscriber impact of their activities. For example, an engineer may look at the cell towers with the poorest performance across the entire country, and flag them for maintenance or new investment. But without correlating this with subscribers in the area, or service uptake, it’s unclear whether this is in fact the best investment.


This is a well-known issue across the telecoms industry, and the problem lies in the traditional operational support systems (OSS) in place. Older OSS tools typically do not provide the mix of data required to understand subscriber activity and the impact of network planning and optimisation activities.

Luckily most operators have now embraced the need for OSS transformation, and are investing in more sophisticated tools to provide improved operational intelligence. New methods of working and new use cases are now emerging, in which operational decisions are being made based on impact, not just performance statistics alone.


Taking a localised view to network planning and optimisation


One such example is taking a localised approach to network planning and optimisation to fully understand the entire situation in specific areas across the country. Using SysMech’s Zen Operational Intelligence Software, network engineers are creating a ‘dynamic grid’ view of the country, from which they can see key performance metrics, drill down to a ‘zoomed in’ local grid and identify the subscribers in the area alongside new business opportunities and profitability. With this information, they can then make better informed decisions based upon the impact on subscribers and potential new business revenue.


Firstly, the country is split into 10km grids, displaying a RAG status for a key metric, such as call set-up success rate, along with sizing based on a secondary metric, for example combined data consumption volumes. These metrics are completely dynamic allowing network operators to compare various elements of the networks performance and usage.


Areas of poor performance with a high user base can then be zoomed in on to provide more detail on that specific locality. Network engineers can see the same metrics for a 1km grid square, along with the location of the cell towers and key users such as enterprise customers. With the click of a button, network engineers can also bring up information on the opportunities within the locality, including private and public businesses, how many subscribers have used the cell towers within the grid, and spots with the potential for new cell tower deployment. They can also see the profitability of existing cells in the locality to get a better understanding of how the area currently performs.


With all this information in one view, network engineers can rapidly identify the local regions with the greatest potential for new investment or optimisation. Key decisions can be made, knowing how subscribers will be impacted. In one example, a feed of handset application data identified a dense area of total service loss. With localised investigation, the operator could easily identify the cause of the problem; a large hospital with poor indoor coverage. They could then make the decision to deploy a small cell in that area to improve indoor coverage. Small cells have a much lower cost than new cell towers, and deliver significant impact on indoor coverage.



This type of network planning and optimisation is now being implemented by many network operators, and numerous use cases are beginning to emerge, demonstrating real, measurable impact on both customer satisfaction and return on investment. Head over to the SysMech website to see more impact led  use cases.