The Life Cycle Amortisation Module (LCAM)

The Life Cycle Amortisation Module (LCAM) is a decision-making tool.

Our clients want to identify an energy investment solution that best meets their individual requirements. LCAM is a tool to make things comparable. Based on key performance indicators that will be normalized for example to ‘cost / kWh’, one can take better informed decisions. Life cycle costs include primary investment cost, refurbishment cost, regular maintenance and operational cost. In addition we can reflect the cost of finance and tax effects of our clients’ investment.

But LCAM goes even further and also funtions as a “holy grail of investment decisions” by:

  • valuing flexibility,
  • valuing “real options”,
  • applying Monte Carlo scenario modelling

Valuing flexibility

In many cases, a project may open (or close) various paths of action to the company, but this reality will not be captured in a strict NPV approach. Some analysts account for this uncertainty by adjusting the discount rate (e.g. by increasing the cost of capital) or the cash flows (using ‘certainty equivalents’, or applying subjective haircuts to the forecast numbers). Even when employed, however, these methods do not properly account for changes in risk over the project lifecycle. We are therefore frequently employing tools which place an explicit value on these options. So, whereas in a DCF valuation the most likely or average or scenario specific cash flows are discounted, here the “flexible and staged nature” of the investment is modelled, and hence “all” potential payoffs are considered. The difference between the two valuations is the “value of flexibility” inherent in the project, i.e. the value of a real option.

For example, a utility would build/acquire a power plant in a specific country given that demand for power exceeded a certain level and acquire power in the wholesale market otherwise. In turn, given further demand, it would similarly expand the power plant with additional blocks. In a DCF model, by contrast, there is no “branching” – each scenario must be modelled separately. In the decision tree, each management decision in response to an “event” generates a “branch” or “path” which the company could follow. 

Valuing Real Options

We normally apply Real Options Valuation (ROV), when the value of a project is contingent on the value of some other asset or underlying variable. For example, the viability of an oilfield is contingent on the price of Brent; if the price is too low, management will not develop the field. Again, a DCF valuation would capture only one of these outcomes.

The following sequence is advisable: (1) using financial option theory as a framework, the decision to be taken is identified as corresponding to either a call option or a put option; (2) an appropriate valuation technique is then employed – usually a variant on the Binomial options model or a bespoke simulation model. (3) The “true” value of the project is then the NPV of the “most likely” scenario plus the option value.

Given the uncertainty inherent in project forecasting and valuation, analysts will wish to assess the sensitivity of project NPV to the various inputs (i.e. assumptions) to the DCF model.

Applying Monte Carlo scenario modelling

A further advancement overcomes the limitations of sensitivity and scenario analyses. This is done by examining the effects of all possible combinations of variables by constructing stochastic or probabilistic financial models – as opposed to traditional static and deterministic models. For this purpose, the most common method is to use Monte Carlo simulation to analyze the project’s NPV. In contrast to the scenario approach above, the simulation produces several thousand random but possible outcomes. The output is then a histogram of project NPV, and the average NPV of the potential investment – as well as its volatility and other sensitivities – is then observed. This histogram provides information not visible from the static DCF: for example, it allows for an estimate of the probability that a project has a net present value greater than zero (or any other value).

A refined Monte Carlo model includes the possible occurrence of risk events (e.g., a credit crunch, an oil price crisis, etc.) that drive variations in one or more of the DCF model inputs.

Our Life Cycle Amortisation Module LCAM forms the fundament to address the challenges described above.