6.9. Conclusion

Using this case study, let us review how the Visual Six Sigma Data Analysis Process aligns with the DMAIC framework, and how the Visual Six Sigma Roadmap was used to make progress quickly:

• Frame the Problem occurred in the Define phase.

• Collect Data began in the Measure phase, where the team collected data for its MSA studies and for the baseline control chart. Also, the team collected a set of historical data relating Color Rating, the team's primary, but nominal, Y, to four continuous Ys, namely Thickness, L*, a*, and b*, that were thought to provide more detailed information than Color Rating itself.

• Uncover Relationships was the goal of the Analyze phase. The team members first visualized the five Ys one at a time using Distribution, also using dynamic linking to start to explore conditional distributions. Then, they dynamically visualized the variables two at a time with a Scatterplot Matrix. Finally, they dynamically visualized the variables more than two at a time using Scatterplot 3D. From the relationships that they uncovered, they were able to define specification limits for Thickness, L*, a*, and b* that corresponded to nondefective Normal Black parts.

• Model Relationships occurred in the Analyze and Improve phases. Here, the team studied five potential Hot Xs for the four continuous Ys. A customized experiment that allowed the team to identify which Hot Xs to include in each of the four signal functions was designed and conducted. The resulting models were visualized using the Prediction Profiler.

• Revise Knowledge also occurred as part of the Improve phase. New settings for the Hot Xs were identified that would simultaneously optimize all four Ys. Confirmation runs were obtained to provide some assurance that operating at the new optimal settings was likely to deliver the expected results. Finally, the JMP simulator was used to visualize the impact that variation about these optimal settings would have on the Ys.

• Utilize Knowledge was the goal of both the Improve and Control phases. Here, the knowledge developed by the team was institutionalized as the new way of running the process.

This case study shows how a Six Sigma team used visualization and confirmatory methods to solve a challenging industrial problem. The team's efforts resulted in a multimillion-dollar cost reduction for Components Inc. In addition, the elimination of rework resulted in significantly increased capacity in the anodize process. Components Inc. was able to use this newfound capacity to accommodate the increased demand for the parts that resulted from the dramatic improvements in quality and on-time delivery.

Our case study demonstrates how the dynamic visualization, analytic, and simulation capabilities of JMP played a prominent role in uncovering information and in modeling relationships that led to the resolution of a tough problem. Without these capabilities, and the Visual Six Sigma Roadmap to guide them, the team would have faced a much longer and more difficult path trying to find a workable solution.