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Take control of your digital transformation to balance your cost and complexity

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From 5G complexity to COVID-19 disruptions, it can seem like the forces shaping the future are out of your hands as a communications service provider (CSP). What you can control is how to respond — using automation, virtualization, artificial intelligence and machine learning (AI/ML) to tackle emerging challenges and seize new opportunities. With a structured approach to setting priorities, you can overcome some of the cost and complexity challenges that come with digital transformation, and make real progress toward becoming a digital service provider (DSP).

 

The road to becoming a DSP is paved with automation and intelligence

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It’s important for CSPs to define their own digital transformation priorities because they each have their own business goals and strategies. One CSP we worked with recently was a “market pragmatist”. They had merged with another CSP to cut costs and gain economies of scale, and while they had some success automating operational processes, scaling that automation was a challenge. We helped them take a step-by-step, AI-based approach, starting with the “low-hanging fruit” of fault management, alarm handling, troubleshooting and issue resolution in the radio access network. We then built and rolled out new proof-of-concept solutions, with re-validation of their strategy and confirmation of their expected return on investment (ROI) every step of the way.

In contrast, a different CSP wanted a “big bang”: everything that could be automated would be automated to manage 5G complexity and respond to shifting market demands. They adopted a unified and integrated automation platform that gives them a “single pane of glass” view into their operations. They’re now applying automation across their radio, transport and core domains. In a relatively short time, they’ve automated a wide range of operational processes including fault, configuration and performance management using 100 digitized methods of procedure (“DigiMoPs”).

While the “pragmatist” CSP was focused on cost, their automation initiatives helped keep a grip on network complexity. The “big bang” CSP had complexity in the foreground but their leadership still wanted to bring down their total cost of ownership (TCO) wherever possible. Those two factors — complexity and cost —define almost every CSP’s digital transformation efforts in some way or other.

Mounting complexity, rising costs

Complexity increased steadily with the migrations from 2G to 3G to 4G, and is set to explode with 5G. While today a CSP might operate 35,000 base stations and support eight different bands, by 2025 they could have 50 percent or more of each. For example, our analysis of a medium-sized CSP in Europe shows a 73% increase in network growth in the next 5 years due to a combination of 5G rollouts, re-farming of spectrum assets, and LTE refresh.

But where 5G complexity will really ramp up is in the services delivered — with different performance parameters and service-level agreements (SLAs) for distinct network slices tailored to different use cases, industry segments, and mix of enterprises and consumers. The essence of the 5G opportunity lies in that complexity, so CSPs who want to claim a piece of the market need to embrace digital transformation and work to become DSPs.

Clearly, there’s a cost implication to that, one that extends to every corner of the business, including areas that are already top cost centers: site rental, field maintenance and power consumption — the last of which is growing at an eight percent compound annual growth rate (CAGR). These are costs over which CSPs haven’t traditionally had a lot of control.

How digital transformation can help

A lot of CSPs are focused on three key cost performance indicators: earnings before interest, taxes, depreciation and amortization (EBIDTA); capital efficiency (ROI); and revenues. Digital transformation can have an impact on each. Smart CAPEX (capital expense) and digital design technologies, for example, can help cut TCO by three to five percent. Automation and digitalization can speed up time to market by 25 percent. Cognitive applications that control off-peak power consumption can deliver up to 20 percent cost savings on energy. Predictive algorithms for field maintenance can cut OPEX (operating expenses) by five to 20 percent.

Some CSPs have used drone footage and data from geo-spatial, network and environmental systems to build digital twins of their base stations (digital models that can be manipulated virtually), allowing them to plan upgrades without having to re-survey the same site, saving further money — and time.

On the complexity front, CSPs can use a wealth of tools. End-to-end systems integration can give a “single pane of glass” view of the entire network and the lifecycles of all its services. Operational automation using AI/ML can handle massive numbers of parameters faster than humans possibly can — and identify patterns and potential issues before they affect the customer experience.

With no shortage of choices, the question for many CSPs is: “Where do we start?”

Taking a structured approach

Analyzing the relative cost/complexity trade-offs in a structured way — and in light of your business priorities — gives a clear and reliable digital transformation pathway. Cognitive use cases powered by AI/ML can provide that analysis and expose where digital transformation efforts will deliver the best possible returns. Network expansion, operational streamlining and performance optimization are all promising areas to target.

Data-driven network planning, for example, can identify the best opportunities to reduce site CAPEX and OPEX related to network equipment deployments and spectrum assets. Involved in a joint venture with another CSP and want to know which parts of whose grids to use for new 5G services? Analytics can determine the answer, clarifying the business case and avoiding error-prone assumptions.

AI and automation can boost the efficiency of Network and Service Operations Centers (NOCs and SOCs) — for example, by using intelligent alarm clearing to reduce the number of alarms engineers need to manage from several million to a few hundred or thousand each month. By applying AI and automation across their NOCs and SOCs, some CSPs have improved their overall operational efficiency by up to 40 percent.

Automated cognitive applications can give detailed insights into network coverage, capacity and quality without requiring manual drive tests, which tend only to give “snapshots” of system performance. Automated applications instead can reveal subscribers’ experiences inside buildings, and contrast network performance in the same location at different times of day — insights that make it possible to predict and resolve issues before they become major problems.

Taking a structured approach to setting digital transformation priorities and basing decisions on cognitive analytics helps manage complexity and, by “getting it right the first time”, avoids the cost overruns that often sabotage digital transformation efforts. At Nokia, we’ve developed nearly 40 cognitive use cases to determine where CSPs can gain most from predictive operations based on their unique pain points.

The road to digital transformation is different for each CSP. But each journey will involve the use of a common set of new technologies. Through automation, virtualization and AI/ML, each CSP can manage the increasing costs and complexity of the coming 5G networks and position themselves for ongoing success.

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