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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

Description:

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

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#Business #Strategic Management #Six Sigma #Management & Leadership #Quality Management #Business Analysis #Process Analysis

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