JMP For: Analytical Application Development • Business Visualization • Design of Experiments • Exploratory Data Analysis • Interactive Data Mining • Modeling • Quality Improvement • Reliability • Statistics • Visual Six Sigma
JMP® for Visual Six Sigma
Traditional statistical tools for problem-solving and process improvement can be unhelpful and inefficient. Visual Six Sigma is the next step in the evolution of data-driven process and product improvement programs. Using JMP, you can identify problems and improvement opportunities, uncover solutions and communicate results – visually.
Visual Six Sigma empowers analysts and practitioners to leverage their contextual knowledge to pose relevant, new questions and reliably find answers that reflect real-world ambiguity, uncertainty and compromise. Stakeholders can more easily review and challenge recommendations, exploring alternatives without the distractions of unnecessary or spurious statistical detail.
- Sources of Variation
- Identify Key Drivers
- Build Consensus
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.
You can explore patterns of variation in different ways to set specification limits for parameters that are congruent with a subjective quality assessment.
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.
Once you understand the key drivers, even chronically incapable processes can be operated well while you look for the root cause.
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.
Interact with and link Profilers together to reach consensus within technical teams, then communicate key findings to stakeholders in a way they can understand.
More resources for Visual Six Sigma
Demos
On-Demand Webcasts
More on Visual Six Sigma
Visual Six Sigma on the JMP Blog
Contact JMP Sales
877.594.6567 (US)
International Sales via Worldwide SAS Offices
