Six Sigma

COURSE OUTLINE: The course on Six-Sigma will focus on detailed strategic and operational issues of process improvement and variation reduction. Six-sigma is 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. Six-sigma employs a systematic approach of DMAIC (Define, Measure, Analyze, Improve and Control) for the process improvement. This course will provide a detailed understanding on various issues specific to each phase of DMAIC.

Six Sigma-Introductory Video.
Lecture 1: Brief overview of the course.
Lecture 2: Quality concepts and definition.
Lecture 03: History of continuous improvement.
Lecture 4: Six Sigma Principles and Focus Areas (Part 1).
Lecture 5: Six Sigma Principles and Focus Areas (Part 2).
Lecture 6: Six Sigma Applications.
Lecture 07 : Quality Management: Basics and Key Concepts.
Lecture 8: Fundamentals of Total Quality Management.
Lecture 9: Cost of quality.
Lecture 10: Voice of customer.
Lecture 11: Quality Function Deployment (QFD).
Lecture 12: Management and Planning Tools (Part 1).
Lecture 13: Management and Planning Tools (Part 2).
Lecture 14: Six Sigma Project Identification, Selection and Definition.
Lecture 15: Project Charter and Monitoring.
Lecture 16: Process characteristics and analysis.
Lecture 17: Process Mapping: SIPOC.
Lecture 18: Data Collection and Summarization (Part 1).
Lecture 19: Data Collection and Summarization (Part 2).
Lecture 20: Measurement systems: Fundamentals.
Lecture 21: Measurement systems analysis: Gage R&R study.
Lecture 22: Fundamentals of statistics.
Lecture 23: Probability theory.
Lecture 24: Process capability analysis: Key Concepts.
Lecture 25: Process capability analysis: Measures and Indices.
Lecture 26: Process capability analysis: Minitab Application.
Lecture 27: Non-normal process capability analysis.
Lecture 28: Hypothesis testing: Fundamentals.
Lecture 29: Hypothesis Testing: Single Population Test.
Lecture 30: Hypothesis Testing: Two Population Test.
Lecture 31: Hypothesis Testing: Two Population: Minitab Application.
Lecture 32: Correlation and Regression Analysis.
Lecture 33: Regression Analysis: Model Validation.
Lecture 34: One-Way ANOVA.
Lecture 35: Two-Way ANOVA.
Lecture 36: Multi-vari Analysis.
Lecture 37: Failure Mode Effect Analysis (FMEA).
Lecture 38: Introduction to Design of Experiment.
Lecture 39: Randomized Block Design.
Lecture 40: Randomized Block Design: Minitab Application.
Lecture 41: Factorial Design.
Lecture 42: Factorial Design: Minitab Application.
Lecture 43: Fractional Factorial Design.
Lecture 44: Fractional Factorial Design: Minitab Application.
Lecture 45: Taguchi Method: Key Concepts.
Lecture 46: Taguchi Method: Illustrative Application.
Lecture 47: Seven QC Tools.
Lecture 48: Statistical Process Control: Key Concepts.
Lecture 49: Statistical Process Control: Control Charts for Variables.
Lecture 50: Operating Characteristic ,(OC).
Lecture 51: Statistical Process Control: Control Charts for Attributes.
Lecture 52: OC, Curve for Attribute control chart.
Lecture 53: Statistical Process Control: Minitab Application.
Lecture 54: Acceptance Sampling: Key Concepts.
Lecture 55: Design of Acceptance Sampling Plans for Attributes (Part 1).
Lecture 56: Design of Acceptance Sampling Plans for Attributes (Part 2).
Lecture 57: Design of Acceptance Sampling Plans for Variables.
Lecture 58: Acceptance Sampling: Minitab Application.
Lecture 59: Design for Six Sigma (DFSS): DMADV, DMADOV.
Lecture 60: Design for Six Sigma (DFSS): DFX.
Lecture 61: Team Management.
Lecture 62: Six Sigma: Case study.
Lecture 63: Six Sigma: Summary of key concepts.