We have been building some product forecasting models using Monte Carlo methods. Sales distributions are often skewed right. Using normal approximations tends to over inflate forecast estimates, since the distribution is not centered around the mean. Further more the standard deviation of skewed distributions tends to produce estimates with very wide variances – by definition.
To overcome this, we use a Monte Carlo simulator – that draws from the sales distribution at random. Creating a sample of many estimates not only gives a more accurate estimate, it is also helps us calculate more realistic margins of error.