The Six Sigma methodology is a systematic application, focused on achieving significant financial results and increasing customer satisfaction. When properly deployed on carefully selected business projects, this methodology can lead to a significant reduction, and in many cases elimination, of defects, process waste and out-of-control processes which translate into dramatic business gains.
Various exercises and a full case study will be used to direct and illustrate the implementation of a project. Learned skills are practiced and applied through individual and team exercises as well as individual projects. Participants will be able to apply the concepts learned in the course to a business improvement project assigned to them by their management sponsor. Games and discussions will be used to enrich course delivery.
Develop the Six Sigma methodology and apply its roadmap.
Apply the principles of the Six Sigma 'DMAIC' performance improvement model.
Examine in detail the various elements of building a complete project for improvement.
Develop statistical hypotheses using simple statistical tools.
Use quantifiable tools to solve problems related to an improvement project.
Eliminate waste and defects by applying Lean and Six Sigma.
Collect, analyze, and quantify data that enable process improvements.
Employ statistical analysis using statistical tools and software.
Individuals from all organization departments including finance, quality and business operations staff functions as well as those who have direct intervention as process owners or stakeholders
History and origins.
Why Six Sigma?.
Cost of poor quality.
Project details.
Charter the improvement project.
Define the scope.
Six Sigma project. definition.
Project selection process.
Voice of Customer (VOC) and Critical to Quality (CTQ).
Kano model analysis.
Team development phases.
Communication plan.
Project planning and management
Types of data.
Computing 'DPMO' and sigma levels.
Process mapping..
'FMEAs' and cause and effect.
Graphical analysis.
Analysis of Variance (ANOVA) and multi-plot diagrams.
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