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Monte Caro simulation ties together sensitivities and probability distributions of came out of the work of first nuclear bomb and was so named because it was based on mathematics of Casino gambling. Fundamental appeal of this analysis is that it provides decision makers with a probability distribution of NPVs rather than a single point estimates of the expected NPV. This analysis starts with carrying out a simulation exercise to model the investment project. It involves identifying the key factors affecting the project and their inter relationships. It involves modelling of case flows to reveal the key factors influencing both cash receipt and payments and their inter relationship. In this analysis specify a range for a probability distribution of potential outcomes for each of model’s assumptions.
Steps For Simulation Analysis:
1. Modelling the project. The model shows the relationship of N.P.V. with parameters and exogenous variables.(Parameters are input variables specified by decision maker and held constant over all simulation runs. Exogenous variables are input variables, which are stochastic in nature and outside
the control of the decision maker.

2. Specify values of parameters and probability distributions of exogenous variables.
3. Select a value at random from probability distribution of each of the exogenous variables.
4. Determine NPV corresponding to the randomly generated value of exogenous variables and prespecified parameter variables.
5. Repeat steps (3) & (4) a large number of times to get a large number of simulated NPVs.
6. Plot frequency distribution of NPV

1. Strength lies in Variability. It handle problems characterised by

(a) numerous exogenous variables
following any kind of distribution,

(b) complex inter-relationships among parameters, exogenous
variables. Such problems defy capabilities of analytical methods.
2. Compels decision maker to explicitly consider the inter-dependencies and uncertainties featuring the
project.
Shortcomings Of Simulation Analysis:
1. Difficult to model the project and specify probability distribution of exogenous variables.
2. Simulation is inherently imprecise. Provides rough approximation of probability distribution of NPV.
Due to its imprecision, simulation probability distribution may be misleading when a tail of distribution is critical.

3. Realistic simulation model being likely to be complex would probably be constructed by management expert and not by the decision maker. Decision maker lacking understanding of the model may not use it.
4. Determine NPV in simulation run, with risk free discount rate. 