SAP HANA Predictive Analysis Library (PAL)

The projected estimates are single point estimates of each data point and the analysis provides a single point
value of project net present value (NPV). This is referred to as deterministic modeling, which is in contrast to
probabilistic modeling whereby we examine the probability of outcomes, for example, what is the probability of
a NPV greater than zero. Probabilistic modeling is also called Monte Carlo Simulation.
Monte Carlo Simulation is used in our example to estimate the net present value (NPV) of the investment. The
equations used in the simulation are:
For each year i=0, 1, ..., k
Product margin(i) = product revenue(i) – product cost(i)
Total profit(i) = product margin(i) – overhead(i)
Cash flow(i) = total profit(i) – capital investment(i)
Suppose the simulation covers k years’ time periods and the discount rate is r, the net present value of the
investment is defined as:
Technology Background
Monte Carlo Simulation is a computational algorithm that repeatedly generates random samples to compute
numerical results based on a formula or model in order to obtain the unknown probability distribution of an
event or outcome.
In PAL, the Random Distribution Sampling, Distribution Fitting, and Cumulative Distribution algorithms may be
used for Monte Carlo Simulation.
Implementation Steps
Assume that:
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DM_PAL is a schema belonging to USER1; and
USER1 has been assigned the AFLPM_CREATOR_ERASER_EXECUTE role; and
USER1 has been assigned the AFL__SYS_AFL_AFLPAL_EXECUTE or
AFL__SYS_AFL_AFLPAL_EXECUTE_WITH_GRANT_OPTION role.
Step 1
Input the given estimates (single point deterministic values) for product revenue, product costs, overheads,
and capital investment. In this example, the time periods are 5 (from year 1 to year 5).
The probability distribution for each variable is assumed as follows:
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Product Revenue:
Normal distribution and the mean and standard deviation are listed in the following table.
Product Costs:
Normal distribution and the mean and standard deviation are listed in the following table.
SAP HANA Predictive Analysis Library (PAL)
End-to-End Scenarios P U B L I C 557