Monte Carlo Simulation
A Monte Carlo simulation relies on probability modeling to determine the level of risk. It’s used to determine how an unpredictable variable might affect a certain factor.
For example, let’s say that you want to increase your profit margin and you think the best way to do this is to expand your sales team by five people, for example.
Currently, you pay your sales team a fixed wage, plus commissions. So, you have an idea of the fixed costs. You also have historical data on how much each sales rep generates in earnings. This means you can work out approximately how much each sales agent would be worth to the company.
However, you have to consider the uncertainty involved. For example, what region is that sales agent selling in?
Instead of using just a few figures to determine the outcomes, with a Monte Carlo simulation, you can test hundreds or thousands of options.
This approach means that instead of just seeing averages, you can see a range of potential results. You will essentially be able to see the best- and worst-case scenarios and everything in-between.
Coming back to our example, if the worst-case scenario where all the new sales reps perform far below average means the profit margin remains the same, then it’s likely a good course of action.
So, you can use a Monte Carlo simulation to determine how uncertain factors will affect an outcome. Instead of just resorting to averages, you will get a far more complete picture with this form of analysis.
With linear programming, you can determine the best result based on a set of specific variables. It can be used for things like discovering the most effective way to maximize sales while minimizing customer acquisition costs.
Linear programming involves taking complex relationships, simplifying them so they become linear, and then finding the optimum points.
For example, a pizza delivery person has seven pies to deliver. To save time, he will want to use the shortest possible route. So, he will look at all the possible routes and work out which one is shortest. This strategy is, basically, linear programming.
Linear programming can be used to develop optimal solutions for a wide range of issues. You can determine, for example, how much of each product you should stock to maximize profitability during the holidays. Or you can work out what the optimal price point is to maximize your profit margin while also growing sales.
This is an extremely valuable form of analysis and can be used to streamline operations and significantly improve your bottom line.