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.
INTENDED AUDIENCE : Mechanical Engineering, MBA, Industrial Engineering PREREQUISITES : StatisticsINDUSTRY SUPPORT : Manufacturing and Service Industry
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