Monte Carlo Simulation (PowerPoint PPTX)

Monte Carlo simulation or Monte Carlo study, also MC simulation, is a method from stochastics, in which a very large number of similar random experiments forms the basis. An attempt is made to solve problems that cannot be solved analytically, or can only be solved at great expense, numerically with the help of probability theory.

The law of large numbers is to be seen above all as the basis. The random experiments can either be carried out in real terms – for example by rolling dice – or determined by means of Monte Carlo algorithms. In the latter, seemingly random numbers are calculated to simulate random events, which are also called pseudo-random numbers.

In the context of process optimization and Kanban, Monte Carlo simulation is often used to identify bottlenecks and make predictions in terms of throughput and implementation time, and to agree on service level agreements.

Contents

•  Historical Introduction
•  How Monte Carlo Simulation Works
•  Use of Monte Carlo Simulation in a context of Kanban

Application

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