this isn't Gran Turismo, you need to drive properly.
But a great many situations -- including almost all of the examples above -- have been successfully handled with simulation models created in a spreadsheet using Microsoft Excel.
This minimizes the learning curve, since you can apply your spreadsheet skills to create the model.
Monte Carlo simulation -- named after the city in Monaco famed for its casinos and games of chance -- is a powerful method for studying the behavior of a system, as expressed in a mathematical model on a computer.
As the name implies, Monte Carlo methods rely on random sampling of values for uncertain variables, that are "plugged into" the simulation model and used to calculate outcomes of interest.
A simulation run includes many hundreds or thousands of trials.
Our simulation model -- often called a risk model -- will calculate the impact of the uncertain variables and the decisions we make on outcomes that we care about, such as profit and loss, investment returns, environmental consequences, and the like.
Simple steps or stages, such as inventory levels in different periods, are easy to represent in columns of a spreadsheet model.
You can solve a wide range of problems with Monte Carlo simulation of models created in Excel, or in a programming language such as Visual Basic, C or C#.
As part of our model design, we must choose how numerical values for the uncertain variables will be sampled on each trial.
Complex manufacturing and logistics systems often call for discrete event simulation, where there are "flows" of materials or parts, people, etc.
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