Six Sigma methods have been developed and improved for decades, and historically have relied on acquiring measured data for root cause analyses and solution validation. However, recent increases in computational power and simulation accuracy have made simulation modeling a feasible and trustworthy approach for modeling complex systems. Six Sigma Black Belts can leverage the benefits of combining simulation with measured test data by using model calibration and validation processes in their analytics. However, the use of these processes has added complexity to the statistics required to generate the actionable results.
Uncertainty Quantification (UQ) techniques comprise the next generation of statistical techniques, fully equipped to leverage the potential of combining simulation and physical test data, even for very large and complex systems. This webinar will demonstrate how simulations combined with UQ techniques can enhance Six Sigma statistical modeling processes.
Three case studies will be presented to illustrate the following UQ benefits for Six Sigma statistical analysis:
