The capex challenge facing operators is increasing. Network
expenditure is forecast to rise significantly into the next
decade as operators deploy 5G and edge-cloud networks,
recasting their consumer and enterprise value propositions in
the process.
Capex planning in the 5G era
Capex planning in the 5G era needs to operate at unprecedented pace, scale and precision with seamless workflow across the entire organization on a continuous basis. Shared ownership and accountabilities are vital, as is a wider range of KPIs, including metrics relating to the quality of the customer experience and financial performance. Linear tools and scenario-based rules should give way to machine-learning capabilities supported by dynamic processes.
Planning considerations | Traditional | Fit to 5G |
Ownership | Fragmented | Integrated |
Operation | Manual | Automated |
Accountability | Network | Business |
Input variables | 5 to 10 | No practical limit |
Tools | Linear | Machine learning-based |
Planning rules and process | Static and scenario-based | Dynamic |
Planning horizon | Over 12 to 18 months | Continuous |
Return on investment (ROI) | Not modeled | Prioritized |
Workflow | High-touch | Seamless |
Budgeting | Network-driven | Business-driven |
Time to market | Best effort | Accelerated |
KPIs | Network | Network, customer and financial |
Key success factors for smart capex planning
Standardized AI technology can deliver a step change in agility and precision, by bringing together deep learning and dynamic modeling capabilities. There is no practical limit on the amount or type of data that can be modeled, paving the way for industrialized processes that are automated, transparent and self-governing. At the same time, vendor-agnostic platforms enable more flexible ecosystem relationships. With AI at the heart of capex planning, network planners can become better network investors.
Harnessing automation with AI-led planning tools can drive a better balance between people and processes that delivers shorter planning cycles, greater procedural transparency and more reliable decision-making. At the same time, operators can free up resources to upskill for higher-value functions.
Establish continuous planning
This is vital if network planners are to optimize their use of resources. Agile planning outputs — whether in the form of varied network scenarios or investment recommendations — demand ongoing oversight. In this way, operators will be able to make better decisions as they need them, as transformation road maps or competitor actions evolve in new ways.
Case study: US tier-1 mobile network operator
A US tier-1 mobile network operator wanted to improve its capital allocation for 5G rollout. It had been using rules-based models but was realizing the limits of using such tools for 5G networks. In addition, there was a mandate from business to improve customer experience.
B-Yond collaborated with the carrier, comparing the performance of traditional in-house capex planning tools with an AI-based planning tool for a fixed set of sites over a fixed planning period. Benchmark test results clearly demonstrated the advantage of AI-based smart capex planning over the current in-house tools.
Cell planning | In-house tool | Smart capex | Improvement |
Method | Linear | Dynamic | Machine learning |
Historical data | 18 months | 36 months | Pattern-based |
Validation period | 4 months | 4 months | Baseline |
Correct triggers | 478 | 550 | 72 |
Missed triggers | 121 | 49 | 72 |
Excess triggers | 213 | 76 | 137 |
Capex accuracy | 59% | 81% | 37% |
Underinvestment | 25% | 7% | 72% |
Over-investment | 45% | 11% | 76% |
The results were indisputable: a 37% accuracy improvement in capacity prioritization, 72% reduction in capacity degradation, and 76% reduction in unnecessary capex and opex (over-investment). But this is just the beginning; with AI, planning rules are dynamic and continuously evolving toward zero-error tolerance. As the AI-based tool continues to learn and evolve on the basis of network and traffic patterns, significant capex accuracy can be achieved.
Conclusion
Greater capex efficiency is no longer a desirable attribute, but a business-critical competitive differentiator in the 5G era. For this to happen, a major modernization of network planning activities is required on multiple fronts.
AI-driven tools can help provide improved customer experience and financial benefits while also streamlining workflows and upskilling resources. In this way, smart capex planning can act as a foundation for broader strategic goals.
Greater levels of automation will be the lifeblood of a new capex planning paradigm, one where business outcomes inform overhaul of tools, processes and roles. Legacy systems and processes will not disappear overnight, but AI-driven planning tools will provide a path to graceful attrition and retirement of aging systems without business or operational disruption.
Ultimately, network planning driven by AI and automation has the potential to deliver upside in three important ways by:
- Optimizing capex on the basis of value to customers
- Reducing customer churn
- Streamlining business processes and increasing ROIC
Taken together, these benefits will determine which operators are able to thrive as opposed to surviving in the 5G era.
Summary
Greater levels of capex efficiency are vital for communications providers, particularly as they embark on the new wave of 5G investment. Network planning driven by AI and automation can deliver a better customer experience and drive significant ROI improvements especially given the current challenges faced across the world today. With a smarter approach to capex in place, communications providers can thrive as opposed to simply survive in the 5G era.