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  1. Introduction to the theory of Monte Carlo simulation
  2. Understanding of the basic concepts of probability
  3. Visualization of the Theory of Constraints on the Kanban Board


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.


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


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Monte Carlo Simulation

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File Type: PowerPoint (pptx)

File Size: 35.8 MB

Number of Slides: 36 (includes cover, transition slides)

Related Topic(s): Kanban Theory of Constraints

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Initial upload date (first version): May 12, 2021
Most recent version published: May 12, 2021

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