Monte Carlo Simulation Provides Advantages in Six Sigma

First of all, what is Monte Carlo simulation?
Monte Carlo simulation is a computerized mathematical technique that allows people to account for variability in their process to enhance quantitative analysis and decision making. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil&gas, transportation, and the environment. (read more)

Where did it come from?
The term Monte Carlo was coined in the 1940s by physicists working on nuclear weapon projects in the Los Alamos National Laboratory.

How Monte Carlo simulation works:
Monte Carlo simulation performs variation analysis by building models of possible results by substituting a range of values—a probability distribution—for any factor that has inherent uncertainty. It then calculates results over and over, each time using a different set of random values from the probability functions. Depending on the number of uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands or tens of thousands of recalculations before it is complete. Monte Carlo simulation produces distributions of possible outcome values.

Monte Carlo simulation provides a number of advantages over deterministic, or “single-point estimate” analysis:

  • Probabilistic Results. Results show not only what could happen, but how likely each outcome is.
  • Graphical Results. Because of the data a Monte Carlo simulation generates, it’s easy to create graphs of different outcomes and their chances of occurrence. This is important for communicating findings to other stakeholders.
  • Sensitivity Analysis. With just a few cases, deterministic analysis makes it difficult to see which variables impact the outcome the most. In Monte Carlo simulation, it’s easy to see which inputs had the biggest effect on bottom-line results.
  • Scenario Analysis. In deterministic models, it’s very difficult to model different combinations of values for different inputs to see the effects of truly different scenarios. Using Monte Carlo simulation, analysts can see exactly which inputs had which values together when certain outcomes occurred. This is invaluable for pursuing further analysis.
  • Correlation of Inputs. In Monte Carlo simulation, it’s possible to model interdependent relationships between input variables. It’s important for accuracy to represent how, in reality, when some factors goes up, others go up or down accordingly.

A few examples of Monte Carlo Simulation for Six Sigma and Design for Six Sigma for you to explore.

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