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The course on Six Sigma will focus on detailed strategic and operational issues 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.

The course will provide an exposure to well-established methods of quality assurance and management and advanced statistical methods including design of experiments.

Six Sigma is recognized as modern quality strategy to compete and sustain in the global markets. The philosophy of Six Sigma is built on two frameworks-DMAIC (define, measure, analyze, improve, control) and DMADV (define, measure, analyze, design, verify). This course will provide a detailed understanding on both the methodologies to the students.

 The course intends to cover basic concepts in quality management, TQM, Cost of quality, quality engineering and Six Sigma, review of Probability and Statistics, Test of Hypothesis.

Subsequently, the course will focus on DMAIC process for process and design improvement, Acceptance Sampling, SPC (Statistical Process Control), Process Capability, Gage Reproducibility and Repeatability, Quality Function Deployment.

This will be followed by advanced quality control tools like Design of Experiments, ANOVA, EVOP, Fractional, Full and Orthogonal Experiments, Regression model building, Taguchi methods for robust design, and Six Sigma sustainability.

The course is designed with a practical orientation and includes cases and industry applications of the concepts.


Week 1: Lecture 1: Quality concepts and definition

Lecture 2: Key concepts in quality management

Lecture 3: Fundamentals of Total Quality Management (TQM)


Week 2: Lecture 4: Cost of quality and Six Sigma

Lecture 5: Fundamentals of statistics

Lecture 6: Probability theory and concepts


Week 3: Lecture 7: Probability rules and events

Lecture 8: Sampling distribution and test of hypothesis


Week 4: Lecture 9: Quality philosophies and standards

Lecture 10: Tools for TQM and continuous improvement

Lecture 11: Quality Function Deployment (QFD) and Design failure mode effects analysis  (DFMEA)


Week 5: Lecture 12: Quality awards, benchmarking and service quality

Lecture 13: Service quality and process control


Week 6: Lecture 14: Project management: Complexities and examples

Lecture 15: Project management: Key decisions, Work breakdown structure, schedule development and cost estimation

Lecture 16: Project planning and scheduling: Network, critical path method, PERT, crashing


Week 7: Lecture 17: Measurement accuracy and process variations

Lecture 18: Acceptance sampling

Lecture 19: Operating characteristic curve


Week 8: Lecture 20: Design of sampling plan

Lecture 21: Basics of Statistical Process Control

Lecture 22: Statistical Process Control for services


Week 9: Lecture 23: Control charts for variables and attributes

Lecture 24: Process capability: Fundamentals and measures

Lecture 25: Quality Function Deployment (QFD) and Kano Model


Week 10: Lecture 26: Design of experiment (DOE)

Lecture 27: Experimental analysis in product realization

Lecture 28: Experimental setups and strategies


Week 11: Lecture 29: Factorial experiment, ANOVA and Response surface

Lecture 30: Benchmarking: Customer-service and Product-service performance

Lecture 31: Benchmarks and performance measurement: Critical success factors and case study


Week 12: Lecture 32: Supply Chain Management, TQM and quality chain

Lecture 33: Taguchi Product Design Approach

Lecture 34: Taguchi’s Robust Design


Week 13: Lecture 35: DMAIC, Zero defect and Six Sigma

Lecture 36: Six Sigma: Case study and Tools

Lecture 37: Design for Manufacturing (DFM),  Design for Assemble (DFA) and Reliability Analysis

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