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Indian Institute of Technology, Kharagpur

Six Sigma

Indian Institute of Technology, Kharagpur and NPTEL via Swayam


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 Week 2 :  QUALITY: FUNDAMENTALS AND KEY CONCEPTS
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 Week 4 MEASURE 
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 Week 12  : SIX SIGMA IMPLEMENTATION CHALLENGES
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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