Six Sigma for Students: A Problem-Solving MethodologySpringer Nature, 2020 M12 21 - 492 páginas This textbook covers the fundamental mechanisms of the Six Sigma philosophy, while showing how this approach is used in solving problems that affect the variability and quality of processes and outcomes in business settings. Further, it teaches readers how to integrate a statistical perspective into problem solving and decision-making processes. Part I provides foundational background and introduces the Six Sigma methodology while Part II focuses on the details of DMAIC process and tools used in each phase of DMAIC. The student-centered approach based on learning objectives, solved examples, practice and discussion questions is ideal for those studying Six Sigma. |
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analyze ANOVA Author's creation based average based on Minitab batch black belts calculated cess coefficient Collect data computed confidence interval control charts control limits correlation CTQ characteristic culture customer needs data collection data set decision-makers diagram DMAIC process employees error example factors FMEA function Gage R&R goals hypothesis testing identify Image implemented Inferential Statistics Interval Estimation Juran lean manufacturing matrix measurement system ment methods needs and expectations nonconforming normal distribution null hypothesis number of defects observations operation organization organizational outputs p-value parameter performance potential problem quality control quality costs quality improvement regression analysis response variable sample mean simulation SIPOC Six Sigma projects Six Sigma team Solution Source specification stakeholders standard deviation Step Table temperature tensile strength test statistic tion tolerance interval Type I error UCL and LCL variance variation