All investment problems are economically assessed before they are started. In general, this analysis consists of obtaining a clear view on the underlying technology, predicting the future incoming and outgoing cash flows of the investment project for each discrete time period, discounting them with an appropriate discount factor and then adding them to come to the Net Present Value (NPV) (eq. 2.1) [2.3]. When this NPV is positive, the project is assessed as profitable and the project is executed. This is the approach typically followed by network planners. We refer to Section 3.5.1 for an in-depth discussion of NPV. (2.1)
2.2.1 Techno-economic methodology
In [2.4], Verbrugge et al. introduced their techno-economic methodology (Figure 2-1). The approach consists of four steps, covering all required aspects in the analysis, going from identifying and delineating the problem, over modelling of the different sub-problems to economic evaluation with possible extensions. We refer to their work for a detailed description. This methodology will be applied to analyse the case study in Section 2.3.
Figure 2-1: Techno-economic analysis methodology [2.4].
The first step is scoping the problem and it consists of three phases. To start an economic evaluation, all necessary data needs to be collected. This includes information about the targeted area, the market situation and the technologies that can be used. In the second phase, the problem as a whole is divided into smaller, more manageable problems. The targeted area is divided in smaller areas, and users are put in target groups. In addition to that, the services offered are discussed, and cost and revenue impact factors are summed up. In the last phase of planning, the input data collected from the previous steps is processed. Business modelling, technical design and user adoption are covered. Already in this first step, the impact of uncertainty, flexibility and competition comes into play. How correct is the gathered input data on user adoption and technical parameters like data rates? In addition, if two technical designs are compared, they will differ in the amount of flexibility they offer towards future migration. A Fibre to the Cabinet (FTTC) network using Very high speed digital Subscriber Line (VDSL) can offer comparable data rates as a Hybrid Fibre Coax (HFC) network using DOCSIS 3.0 or a point-to-multipoint FTTH network, but the upgrade possibilities of each physical infrastructure and network technology differ.
In the modelling step, using all the input data from the planning step, the costs and revenues structures are developed. Both capital expenditures (CapEx) and operational expenditures (OpEx) are considered. Top-down and bottom-up approaches are possible. The modelling performed here will be impacted by the additions proposed to the first step. Uncertainty in customer adoption impacts the modelling of the required equipment through the bill of material. Is spare
capacity provided to host all households in an area or is only a partial rollout performed. In the first case, sufficient capacity is present in all cases, but this obviously comes at a higher initial cost compared to the second option. In the second case, capacity could run out, requiring the operator to provision for flexible extensions of the initial installation.
In the evaluation step, the economic evaluation is carried out. The standard method to evaluate financial feasibility is the NPV analysis. This figure gives a first indication of the financial feasibility of the project. When multiple actors are present in the value network, a NPV calculation needs to be performed for each one of them. The total NPV of the project is then equal to the sum of the individual NPVs.
In the fourth step, the initial analysis is refined. This refinement is driven by the several shortcomings of the NPV analysis to reflect the underlying challenges and opportunities the technical design and the environment offers. The main focus of this dissertation is on the various refinements possible in techno- economic analysis. The topics investigated in this dissertation and their relation with the techno-economic analysis are indicated in Figure 2-2. We discuss the driving factors for these refinements and their solutions in the next section.
Figure 2-2: Investigated topics and their relation with the techno-economic analysis methodology
2.2.2 Shortcomings of NPV methodology
Conducting a standard NPV analysis can yield unintuitive results, both in light of uncertainty and competition. These unintuitive results are linked to the two main drawbacks of the NPV analysis.
2.2.2.a Measuring uncertainty and flexibility
In an NPV analysis, the future cash flows of the investment are projected. In this cash flow projection, several assumptions on future evolutions are made, but they are assumed certain. In reality, this assumption does not hold. When the required equipment is dimensioned for the rollout of a Fibre to the Cabinet network, several aspects are driven by the number of customers. A street cabinet can only host x cards, each card can only host y users, etc. However, this number of customers comes with a certain degree of uncertainty and is impacted by the level of competition with other installed infrastructures. In addition, it will differ between several regions. Business areas could for example require more connections compared to rural ones.
Secondly, the project path used in the NPV analysis is considered fixed. Once the decision to invest is made, no deviation from the initial project path is possible. Again, in any business environment, such an assumption does not reflect reality. In case of the FTTC network, the initial street cabinet could reach full capacity, e.g. due to an underestimate of the customer uptake. Once this happens, the operator can install extra capacity, or even upgrade its network. As such, it can be stated that the NPV analysis does not indicate the impact of uncertainty and flexibility on the analysis. However, it still remains the most widely used tool to assess investment viability.
Although the standard NPV analysis suffers from these serious drawbacks, extensions to this method have been proposed to tackle these problems. In order to include the impact of uncertainty on the analysis, two extensions have been proposed, namely the scenario analysis and the sensitivity analysis. In a scenario analysis, the investment project is assessed in a small number of possible scenarios. While NPV analysis offers only one view on the future, scenario analysis compares several alternative futures. For instance, an application provider could compare a scenario of low, normal and high customer uptake and how this impacts the required equipment. A scenario analysis approach can also consist of comparing different investment projects to assess them based on economic feasibility. Scenario analysis has been applied to different cases in telecommunication research [2.5], [2.6].
The second extension is the sensitivity analysis [2.7]. While a scenario analysis only studies a few possible scenarios, the sensitivity analysis studies the impact of uncertainty in the input factors on the output of the analysis. In a scenario analysis, the input values only take some discrete scenario-dependent values, like low and high market potential. In the sensitivity analysis, this input is extended with a statistical uncertainty distribution. It allows one to systematically change
variables in the model to determine the effects on the final result. In techno- economic research within telecoms, the sensitivity analysis has been used in different papers [2.6], [2.8], [2.9].
While incorporating uncertainty in the analysis might still be a straightforward exercise, flexibility cannot be handled as intuitively [2.10], [2.11]. In NPV analysis, the project is seen as a now or never decision, with no possibilities for the decision makers to alter the project during its lifetime. In a realistic business case, this condition is not fulfilled. Real Option (RO) Theory has been formulated to capture the value of managerial flexibility in practical cases. In addition, the concepts offered by this theory make it also of great value for non- financial specialists, as it helps to identify and catalogue intuitive notions of flexible design. The concepts of RO are introduced in Chapter 4.
2.2.2.b Measuring competitive and cooperative interaction
The second drawback of the NPV analysis is the lack of possibilities to incorporate competitive and cooperative interaction. When conducting the analysis, the decision maker has to define the future cash flows. However, once the project is conducted, there might be an impact on the market equilibrium, resulting in counteractions by competitors. Two aspects come into play here. Take the example of the introduction of a new fixed broadband service offer in an already saturated market. The only way this offer can gain market share is by churning on the market share of the other existing offers. When conducting the techno-economic analysis, it is thus required to be able to model the future adoption evolution under competition. More information on market modelling can be found in Chapter 5.
Secondly, competitors might counteract, and these counteractions can impact the viability of the initial strategy, and it could be that another strategy was better chosen at the beginning. Note that such counteractions can also consist of cooperative actions. In order to model such behaviour, it is required to estimate the viability of different strategies under different competitive or cooperative counter strategies. In this case, the strategy resulting in the highest payoff might never be reached, as competitors will not choose the strategy maximising your payoff. Here, more advanced tools are required to find the strategy and the resulting payoff. Game Theory (GT) methodology provides such a toolset, and is described in detail in Chapter 6.
2.2.3 Other issues in techno-economic analysis
Up to now, it was assumed that decision makers had one objective, value creation, which is measured in financial terms, typically through NPV. However, value maximisation is not always the sole goal. Private players have other objectives next to the financial ones. Market share, customer satisfaction or operational excellence are just a few of these objectives. In case of public
players, financial objectives can be absent altogether. In broadband networks, the goal of regulators is sufficient competition on the market, large penetration, high bandwidths, etc.
In a multi-objective environment, these – sometimes – conflicting goals increase the complexity of the analysis. A correct quantification of these objectives is therefore important for a more realistic techno-economic analysis. This issue will be tackled in the next chapter.