INTENDED AUDIENCE :
- 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.
Mechanical Engineering, MBA, Industrial EngineeringPREREQUISITES :
StatisticsINDUSTRY SUPPORT :
Manufacturing and Service Industry
COURSE LAYOUT Week 1:QUALITY: FUNDAMENTALS AND KEY CONCEPTS
Lecture 1: Brief overview of the courseLecture 2: Quality concepts and definitionLecture 3: History of continuous improvementLecture 4: Six Sigma Principles and Focus Areas (Part 1)Lecture 5: Six Sigma Principles and Focus Areas (Part 2)Lecture 6: Six Sigma ApplicationsWeek 2:QUALITY: FUNDAMENTALS AND KEY CONCEPTS
Lecture 7: Quality Management: Basics and Key ConceptsLecture 8: Fundamentals of Total Quality ManagementLecture 9: Cost of qualityLecture 10: Voice of customerLecture 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 DefinitionLecture 15: Project Charter and MonitoringLecture 16: Process characteristics and analysisLecture 17: Process Mapping: SIPOCWeek 4:MEASURE
Lecture 18: Data Collection and Summarization (Part 1)Lecture 19: Data Collection and Summarization (Part 2)Lecture 20: Measurement systems: FundamentalsLecture 21: Measurement systems analysis: Gage R&R studyLecture 22: Fundamentals of statisticsLecture 23: Probability theoryWeek 5:MEASURE
Lecture 24: Process capability analysis: Key ConceptsLecture 25: Process capability analysis: Measures and IndicesLecture 26: Process capability analysis: Minitab ApplicationLecture 27: Non-normal process capability analysisWeek 6:ANALYZE
Lecture 28: Hypothesis testing: FundamentalsLecture 29: Hypothesis Testing: Single Population TestLecture 30: Hypothesis Testing: Two Population TestLecture 31: Hypothesis Testing: Two Population: Minitab ApplicationLecture 32: Correlation and Regression AnalysisLecture 33: Regression Analysis: Model ValidationWeek 7: ANALYZE
Lecture 34: One-Way ANOVALecture 35: Two-Way ANOVALecture 36: Multi-vari AnalysisLecture 37: Failure Mode Effect Analysis (FMEA)Week 8:IMPROVE
Lecture 38: Introduction to Design of ExperimentLecture 39: Randomized Block DesignLecture 40: Randomized Block Design: Minitab ApplicationLecture 41: Factorial DesignLecture 42: Factorial Design: Minitab ApplicationWeek 9:IMPROVE
Lecture 43: Fractional Factorial DesignLecture 44: Fractional Factorial Design: Minitab ApplicationLecture 45: Taguchi Method: Key ConceptsLecture 46: Taguchi Method: Illustrative ApplicationWeek 10:CONTROL
Lecture 47: Seven QC ToolsLecture 48: Statistical Process Control: Key ConceptsLecture 49: Statistical Process Control: Control Charts for VariablesLecture 50: Operating Characteristic (OC) Curve for Variable Control chartsLecture 51: Statistical Process Control: Control Charts for AttributesLecture 52: Operating Characteristic (OC) Curve for Attribute Control chartsLecture 53: Statistical Process Control: Minitab ApplicationWeek 11:CONTROL
Lecture 54: Acceptance Sampling: Key ConceptsLecture 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 VariablesLecture 58: Acceptance Sampling: Minitab ApplicationWeek 12:SIX SIGMA IMPLEMENTATION CHALLENGES
Lecture 59: Design for Six Sigma (DFSS): DMADV, DMADOVLecture 60: Design for Six Sigma (DFSS): DFXLecture 61: Team ManagementLecture 62: Six Sigma: Case studyLecture 63: Six Sigma: Summary of key concepts