Statistical Analyses

The stereotypical Lean Six Sigma DMAIC project has a nice process that produces data at a rapid enough rate that you can evaluate improvements during the project, but what options do you have if that is not the case? Using Monte-Carlo simulations can be a powerful way to estimate future performance without having to actually pilot test or prototype the improvements.

Qualitative analysis for decision making when conducting attribute analyses can be deceiving. For this situation, an attribute measurement involves a yes or no response. 

What do you do with data that is non-normal and non-transformable data? Read and learn how this quick and simple Minitab resource helps you do just that! 

Important Box Cox transformation considerations that should be addressed before applying this data-transforming technique are described in the PDF article below.

This PDF article provides a predictive modeling example for assessing the stability of a process and then provide a prediction statement for non-normal response data.

The provided process capability study PDF article below describes issues with traditional lean Six Sigma process capability indices reporting and what can be done to resolve the problems.

Described is an enhancement to Acceptable Quality Level testing (i.e., AQL evaluations) which provides better assessments of supplier quality, with less effort.