Return on Investment (roi) for Multi-Technology son juan Ramiro, Mark Austin and Khalid Hamied


Figure 7.1 Benefits from multi-technology SON. Figure 7.2



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Figure 7.1 Benefits from multi-technology SON.

Figure 7.2 From technical to financial benefits.
networks are extremely complex systems, and it is well known that capacity, coverage and quality are many times conflicting targets in wireless network design and operation. Therefore, the capacity enhancing techniques under discussion will be only beneficial from an operational viewpoint if they can provide capacity enhancements while at the same time maintaining satisfactory coverage and quality.
Moreover, another remarkable benefit of the application of SON techniques comes from improved Quality of Service (QoS), which is mainly due to two reasons. First, the availability of Self-Planning and Self-Optimization techniques ensures that the network is always delivering the desired QoS while maintaining reasonable operational costs, which would otherwise be extremely challenging to achieve by means of manual network maintenance activities. Secondly, the deployment of Self-Healing routines minimizes the time to solve, or at least compensate for, the disturbing consequences of network incidences. Ultimately, quality improvements result in increased customer loyalty and satisfaction, thereby reducing churn and directly impacting the operators’ Earnings Before Interest, Taxes, Depreciation and Amortization (EBITDA) positively (see Figure 7.2).

7.2. General Model for ROI Calculation


In order to estimate the financial benefits for an operator deploying a set of SON functions in its different network layers, a faithful method is to look at their impact on the company’s cash flow, ignoring accounting concepts such as profit/loss and solely focusing on cash calculations. Additionally, there are taxation implications involved in this discussion. However, since these are country-specific, they are not considered in this generic analysis.
When introducing a set of SON functions, it is likely that the impact on the operator’s cash flow is observed over several years. In some cases, e.g. when a process is simply automated and there are obvious and recurrent OPEX savings associated with that process, the description of the benefits can be simplified by just reporting annual savings. However, in other cases, such simplification is not straightforward and a more comprehensive methodology needs to be adopted. For example, let us assume that a SON function is activated and, as a consequence, the need for new CAPEX expansions is reduced by 40% during the first year, 30% during the second year and 20% during the third and fourth years. In this case, it becomes clear that a mere annual calculation of the savings is no longer an illustrative tool. In these more complex cases, a more suitable methodology is the computation of the Net Present Value (NPV) of the Cash Flow (CF) that can be d ifferentially a ttributed to SON over a meaningful time horizon. This calculation, which is often utilized to assess long term projects taking into account the time value of money, is e xecuted as follows:
NPV = ∑∞ CFn 1 n (7.1)
n=1 (1+ r)
where CFn is the differential cash flow (i.e. incoming cash flow minus outgoing cash flow, only for those contributions that are directly attributable to SON) associated with year n (taking the entire year as evaluation period and measuring magnitudes at the end of the year) and r is the discount rate, which is the rate of return that investors would typically expect from a project with a risk profile similar to that of the process under evaluation. In principle, the summation should be carried out for an infinite time horizon. However, in practice, this is neither feasible nor important since the contribution of CFn for large values of n (i.e. many years into the future) does not have a strong influence on the final result due to the negative exponential nature of the factor that it is multiplied with (see Equation 7.1).
For each year, the differential cash flow is calculated as the net amount of extra cash (positive or negative) that the operator is going to have at the end of the year due to the deployment of a certain set of SON functions. Typical components of this magnitude are: (i) additional revenue due to extra quality and therefore reduced churn; (ii) OPEX reduction through automation of tasks that used to be executed manually before the introduction of SON; (iii) OPEX
Year 1 Year 2 Year 3 Year 4 Year 5

CAPEX Forecast without SON (€ million)
CAPEX Forecast with SON (€ million)

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Differential Cash Flow due to SON (€ million)


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