Click For Hindi Version

v v 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. 

v 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. 

v 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. 

v The course is designed with a practical orientation and includes cases, industry examples and MINITAB software applications.  

v The course is designed to satisfy the need of both industry professionals and University students.

v The content is beneficial to both manufacturing and service industry


INTENDED AUDIENCE : Mechanical Engineering, MBA, Industrial Engineering
PREREQUISITES : Statistics

INDUSTRY SUPPORT : Manufacturing and Service Industry



Course layout :-

 

Week-1 : QUALITY:FUNDAMENTALS AND KEY CONCEPTS


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 


Books and references :-

1.   Roderick A. Munro and Govindarajan Ramu and Daniel J. Zrymiak, The certified six sigma Green Belt Handbook, ASQ Quality Press and Infotech Standards India Pvt. Ltd. 

2.  T. M. Kubiak and Donald W. Benbow, The Certified Six Sigma Black Belt Handbook, Pearson Publication.

3.   Forrest W. Breyfogle III, Implementing Six Sigma, John Wiley & Sons, INC. 

4.   Evans, J R and W M Lindsay, An Introduction to Six Sigma and Process Improvement, CENGAGE Learning.   

5.   Howard S. Gitlow and David M. Levine, Six Sigma for Green Belts and Champions,  Pearson Education, Inc. 

6.   Montgomery, D C. Design and Analysis of Experiments, Wiley.

7.   Mitra, Amitava. Fundamentals of Quality Control and Improvement, Wiley India Pvt Ltd.

8.   Montgomery, D C. Statistical Quality Control: A modern introduction, Wiley.