Optimization With Data Analytics
If your business model is not well understood, simulation allows you to change the parameters and see how the business behaves under those parameters. Through simulation, you can change the parameters of your business model to see how it behaves under a variety of situations. With this experience, you can then move on to optimization – getting the greatest possible reward for the least possible cost.
In any set of decisions, the optimal decision can be difficult to discern, but simulation and modeling helps evaluate possible options and identify the one that provides the best result.
By creating a simulated model of your system, you can then adjust the simulation to see first-hand the implications of proposed business decisions. These decisions can be as varied as whether or not to add HR staff, which delivery route to choose, where to build a physical site, whether a manufacturing robot can be sped up, or any of an infinite number of business decisions that need to be made.
Or, alternatively, you can optimize a system in a model given a set of known restraints, combining limited resources and relationship constraints. This can be used to calculate the point of declining returns, overutilization, the supply/demand curve, and other relationship models.
Our simulation models use computer science to iterate over all of the environment’s parameters and tune the distinct values to optimize the outcome. (Or, in non-geek-speak, we run the simulation a bunch of times and pick the best, most robust result.)
For example, if your business was retail sales, it is possible to use computer simulation to optimize what inventory should be in a particular store to maximize sales, if you know the turnover time, discount and sales promotions, and consumer behaviors and preferences.