JMP® for Visual Six Sigma

Six Sigma can be defined as managing variation in relation to requirements. You can use the wide repertoire of graphical displays in JMP either alone or in combination to dynamically assess the structure of your data and literally see the dominant sources of variation that may be practically important rather than just statistically significant. This dynamic visualization enables you to go far beyond what is possible with static graphs, and although valuable in any situation, is essential as your data becomes more complex because of increasing numbers of columns and rows.

Even with highly dimensional data, the appropriate use of dynamic visualization, coupled with your understanding of the data, will often reveal that subset of Xs that alone or in combination really drive the outcomes, the Ys, of interest to you. But in situations where this approach is not informative, or you have just too many variables to work with, JMP also provides powerful statistical approaches that can effectively reduce dimensionality but preserve information. Platforms like Partition, Cluster and Discriminant, when used in the spirit of uncovering relationships, are often highly successful in isolating the hot Xs that can be then used for more definitive statistical modeling if this is meaningful and useful.

Gathering data and working with it always costs time and money. Unless your findings are actually used to drive subsequent decisions and actions, your work will consume rather than create business value. A key part of this process is the communication of findings to a wider community of stakeholders. And, since most real-world situations are about making trade-offs and compromises, you will usually need to build consensus, not just to communicate. The Profilers and Simulators in JMP are made for this, enabling teams to meaningfully and quickly assess findings and explore what-if scenarios using contextual knowledge to supplement what the data contains – but without getting bogged down in the technical aspects of modeling.