Many years ago, in the early self-organizing networks (SON) days, I worked on an intelligent software product for mobile network planning and optimization. By automating certain tasks, this software aimed to free engineers to focus on complex activities which required their expertise.
As a consequence, mobile network engineers could become more efficient and – although not an identical term – more productive.
Interestingly, in a meeting with a mobile network operator, the reaction of a terrified engineer upon hearing about this time-saving software was to ask: “Does this mean that I will no longer have a good excuse to avoid all those boring internal meetings?”
The antidote to pain (and complexity)
On a more serious note, most mobile network engineers welcome the helping hand of automation, especially for recurring tasks. Even though these activities may not be as futile or arduous as the predicament of Sisyphus, their repetitive nature creates the impression of a painful, never-ending cycle.
Furthermore, some of these tasks are complex to perform manually, while others are relatively simple and hardly make the best use of engineers’ expertise.
The interest of mobile network operators in automation has increased as networks have become more challenging to build and operate. Which is why automation has been a major theme in recent years, including its essential role in virtual and hybrid network evolution. Automation has also been discussed as a key enabler for 5G, with artificial intelligence (AI) stealing the show.
Time for productivity gain
But mobile network operators do not need to wait for AI “magic” to enhance operational efficiency.
Intelligent software solutions, with embedded network expertise and advanced features such as customer experience geolocation, can lead to significant improvements through task automation today.
For example, a project with a Tier 1 European operator looked into the productivity gain that a new approach could offer versus legacy optimization/engineering methods. Typically, such methods make limited use of automation and, by relying on drive tests and cell counters only, cannot provide complete insights into network performance and customer experience.
Based on this project, the new approach considered could reduce by 69% the analysis time for recurring network optimization activities in major cities. The substantial productivity gain would be equivalent to a cost saving of US$3.4M per year. The project also comprised other key use cases for this operator, including the analysis of M2M/IoT network issues for its VIP customers.
Although the productivity gain and respective cost saving will differ from project to project, new approaches built on intelligent software solutions can greatly assist stretched engineering teams.
Perhaps, the burden of Sisyphean tasks will not entirely disappear, at least for now. Nevertheless, it is certain that the engineers’ recurring uphill struggle can become much less painful. And, no, dear engineer, you do not have to attend those boring internal meetings!