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

7.3.8.2. Sensitivity Analysis

In the following, a brief sensitivity analysis is provided in order to illustrate the dependence of the NPV on the different input parameters. First of all, the dependence on the traffic growth pattern is analyzed, and Table 7.2 shows three different patterns for traffic growth together with their associated NPV.
Table 7.2 NPV of Self-Planning versus traffic growth

Relative traffic growth pattern (%; years 2-10)	NPV (€ million)
[10, 10, 10, 10, 10, 10, 10, 10, 10]	15.92
[20, 20, 20, 20, 20, 20, 20, 20, 20]	32.35
[30, 30, 30, 30, 30, 30, 30, 30, 30]	51.07

Table 7.3 NPV of Self-Planning versus over-dimensioning rate

Over-dimensioning factor (o)	NPV (€ million)
20%	15.92
30%	25.74
40%	36.87

Table 7.4 NPV of Self-Planning versus initial number of carriers per sector

Initial number of carriers per sector	NPV (€ million)
1	18.64
2	15.92
3	15.13

The main conclusion from Table 7.2 is that more aggressive traffic growth patterns result in more ROI from the Self-Planning functionalities, since more aggressive traffic growth leads to more need for capacity expansions and therefore more room for benefitting from an error-free capacity planning process.
Table 7.3 shows the dependency of the NPV on the value of the over-dimensioning rate (o in Table 7.1). For this calculation, the first traffic growth pattern in Table 7.2 is used: [10, 10, 10, 10, 10, 10, 10, 10, 10]%. As expected, higher over-dimensioning rates for the manual process lead to more ROI from the ideal Self-Planning solution.
In Table 7.4, the dependency of the NPV on the initial number of carriers per sector is analyzed. For this exercise, the first traffic growth pattern in Table 7.2 is used: [10, 10, 10, 10, 10, 10, 10, 10, 10]%, and the over-dimensioning rate (o) is assumed to be 20%. As can be seen, the value of an ideal solution is higher when the network under consideration is initially equipped with fewer carriers, since this leads to more need for capacity expansions and therefore more room for benefitting from an error-free capacity planning process.