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Online Course

Six Sigma

Indian Institute of Technology, Kharagpur and NPTEL via Swayam


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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. 
The course is designed with a practical orientation and includes cases, industry examples and MINITAB software applications.  
The course is designed to satisfy the need of both industry professionals and University students.
The content is beneficial to both manufacturing and service industry.


Lecture 1: Brief overview of the course
Lecture 2: Quality concepts and definition
Lecture 3: 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 7: 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)
Week 3  : DEFINE
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
Week 5  : MEASURE 
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
Week 6  :  ANALYZE 
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
Week 7  :   ANALYZE 
Lecture 34: One-Way ANOVA
Lecture 35: Two-Way ANOVA
Lecture 36: Multi-vari Analysis
Lecture 37: Failure Mode Effect Analysis (FMEA)
Week 8  :  IMPROVE
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
Week 9  :  IMPROVE
Lecture 43: Fractional Factorial Design
Lecture 44: Fractional Factorial Design: Minitab Application
Lecture 45: Taguchi Method: Key Concepts
Lecture 46: Taguchi Method: Illustrative Application
Week 10  :  CONTROL 
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) Curve for Variable Control charts
Lecture 51: Statistical Process Control: Control Charts for Attributes
Lecture 52: Operating Characteristic (OC) Curve for Attribute Control charts
Lecture 53: Statistical Process Control: Minitab Application
Week 11  :  CONTROL 
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 

Taught by

Prof. Jitesh J Thakkar

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