This means that the model assumes that all the jackets that

were purchased will be sold and there will be no surplus.

The total revenue is calculated in cell B14.

The cost is in B15, and the profit in B16.

Since the model doesn't consider any of the uncertainty,

the average profit in cell B17 is the same as the profit in cell B16.

The estimated profit is $7,280.

Now, let's take a look at the simulation model.

We're going to assume the store would purchase the historical

average of 80 jackets.

The demand value in cell E11 is in green,

the color that we have been using to indicate that this is a uncertain value.

If we double click on this cell, we can see that we are making an assumption that

the demand follows a Poisson distribution with an average of 80 jackets.

The rest of the formulas to calculate the profit

are the same as in the average value model.

The profit is the simulation output.

So cell E16 includes the psi output function.

The average profit is calculated using the PsiMean function.

The difference between the average model and

the simulation model is that we have added uncertainty in the demand.

The uncertainty was modeled using a Poisson distribution.

If the assumption is correct that the demand follows a Poisson distribution,

which could be verified by analyzing historical data, then the simulation

model shows that the average value model is over-estimating the expected profit.

This tells us that using an average value for

an uncertain input, the demand in this case,

does not necessarily result in the correct estimate for the average output value.

What is happening here is that every time the demand is larger than 80,

the profit it stays at $7,280.

However, when the demand is less than 80, the profit drops because there

will be some loss associated with selling jackets at their salvage value.

The average value model is not able to capture this.

To conclude, I hope that I have been able to convince you

of the benefits of simulation as a predictive analytics tool

that enables you to incorporate uncertainty in decision making models.

In this example,

we show how average values might result in misleading information.

But this is just one of several important insights produced by various types

of analysis that we have discussed throughout this module.