Modeling in Process Development – Deterministic or Stochastic?

Deterministic" and "stochastic" sound like fancy, academic words, but they are vital for everyone in business to understand. Deterministic refers to single numbers, while stochastic refers to probabilities. When modeling a system, a deterministic model uses numerical input values and calculates numerical output values. But a stochastic model uses a probability distribution for each input and estimates a probability distribution for each output. Monte Carlo simulation (@RISK) is the standard tool for stochastic modeling. During Monte Carlo simulation, random values are generated for inputs, and output values are calculated. By repeating this process many times and saving output values, the simulation generates estimates for output distributions.
Stochastic modeling and Monte Carlo simulation have become accepted tools in financial professions. However, engineers working on the development of new products have been slow to adopt these important tools, opting instead for deterministic approaches such as worst-case analysis. These deterministic tools answer some questions, but they are unable to predict process capability, which is the key to success in a Six Sigma environment. Monte Carlo simulation is now an essential tool for every engineer involved in product and process development. Stochastic tools can predict and prevent performance and capability problems before the first prototype is built. What once required months or years to discover now takes only minutes to prevent.
On April 30th, Andrew Sleeper  of Successful Statistics will be presenting  Accelerating Product Design with Simulation and Stochastic Optimization. Plan to spend hour to learn how to save months in your product and process development projects. It will be time well spent. You can register to attend at

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