This course will teach a focused managerial strategy of process improvement and variation reduction called Six Sigma, a measure of quality that strives for near perfection.
It is a disciplined, data-driven approach for eliminating defects (driving towards six standard deviations between the mean and the nearest specification limit) in any process-from manufacturing to transactional and from product to service.
A Six Sigma defect is anything outside of customer specifications. To be tagged Six Sigma, a process must not produce more than 3.4 defects per million opportunities.
To this end this course will span established methods of quality assurance and management, and advanced statistical methods including design of experiments.
Positioned at the frontier of modern quality strategies, Six Sigma comprises two frameworks-DMAIC (define, measure, analyze, improve, control) and DMADV (define, measure, analyze, design, verify). This course will cover both.
Course centents:
Basic concepts in quality management, TQM, Cost of quality, quality engineering and Six Sigma, review of Probability and Statistics, Test of Hypothesis.
DMAIC process for process and design improvement, Acceptance Sampling, SPC (Statistical Process Control), Process Capability, Gage Reproducibility and Repeatability, Quality Function Deployment.
Design of Experiments, ANOVA, EVOP; Fractional, Full and Orthogonal Experiments, Regression model building, Taguchi methods for robust design, Six Sigma sustainability; Case studies.