Six Sigma Quality Methods  3 College credits
Quality improvement techniques will be studied including Six Sigma implementation, experimental design, and CMM. Extensive use of case studies and personalized application of the improvement techniques to product, job, and business. Six Sigma quality improvement methods will be studied and applied to real-life case studies. Six Sigma quality: tools, statistical methods, process mapping, performance goals, metrics, capability analysis, “green and black belt” teams. Personalized application of the improvement techniques to product, job, and business. Taught by an ASQ-certified professional.  3hrs/week.Prerequisites: Math 114 or equivalent Three class hours/week for 15 weeks.

College Contact: Professor Charles Hendrickson, CQE (301) 322-0760. PG College Engineering Technology Faculty member. Email at chendrickson@pgcc.edu

Instructor: Joe Ludford, CQE, CRE, CSQE, CQM (301) 843-3087. Joe is on the executive board of the Washington DC chapter of the American Society for Quality (ASQ), and is the owner and principal consultant of White Hart Associates. Joe has a distinguished teaching background. Email at Jludford@radix.net

SYLLABUS

Week 1            Six Sigma quality overview-  methods and philosophy 

Week 2            Six Sigma teams (green and black belts) 

Week 3            Principles of Six Sigma: Kano method, QFD, data collection 

Week4             Six Sigma organizational performance goals and metrics 

Week 5            Examination 

Week 6            Six Sigma principles of measurement: scales, data reliability, repeatability and reproducibility 

Week 7            Basic Six Sigma methods: problem-solving tools (introduction). Assign major course project that will implement Six Sigma in a medium-sized company. 

Week 8            Basic Six Sigma methods (continued): process mapping, pareto analysis, cause and effect diagrams, flow charts, scatter plots and data analysis 

Week 9            Basic Six Sigma methods (conclusion): 7M tools, knowledge discovery tools. Work through a comprehensive case study and apply the methods learned. 

Week 10          Examination 

Week 11          Intermediate Six Sigma methods (introduction): enumerative statistical methods including expected value, significance, confidence levels, hypothesis testing, errors 

Week 12          Intermediate Six Sigma methods (conclusion): process capability, control chart utilization, EWMA charts 

Week 13          Advanced Six Sigma methods: design of experiments using factorial analysis and intro to ANOVA, reliability analysis methods, risk and safety assessments 

Week 14          Review Six Sigma methods (basic- advanced). Demonstrate when and how each method level should be implemented. Students will present their course projects to the class. 

Week 15          Examination- comprehensive 

EXPECTED COURSE OUTCOMES:

The student will be able to explain:

 1.        The Six Sigma teams (green and black belts)

·         Six Sigma team formation and member responsibilities

·         Process improvement teams

·         Team performance evaluation

2.        Principles of Six Sigma

·         Kano method

·         QFD

·         Data collection

3.        Six Sigma organizational performance goals and metrics

·         Quality costs

·         Attributes of good metrics

4.        Six Sigma principles of measurements and data

·         Scales of measurement

·         Reliability and validity of data

·         Repeatability and reproducibility

  The student will be able to explain and implement:

 5.        Basic Six Sigma methods

·         Problem solving tools

·         Process mapping

·         Flow charts

·         Check sheets

·         Pareto analysis

·         Cause and effect diagrams

·         Scatter plots

·         7M tools: affinity, tree, matrix activity network diagrams; process decision charts

·         Knowledge discovery tools: run charts, histograms, use of descriptive statistics

6.        Intermediate Six Sigma methods

·         Enumerative statistical methods: probability, expected value, distributions, statistical tolerance, significance, and confidence levels, hypothesis testing, analysis of Type I and II errors

·         Analytic statistical methods: control charts, EWMA charts, process capability and control measures

7.        Advanced Six Sigma methods

·         Design of experiments: design characteristics, ANOVA, factorial design, regression analysis, composite design, robust process design, chi-square

·         Reliability analysis and Monte Carlo simulation

·         Risk assessment

·         Safety analysis

·         Process simulation and model development

  INSTRUCTIONAL MATERIALS:  Required:  Textbook

 The Six Sigma handbook

By: Thomas Pyzdek

McGraw Hill Publishing

ISBN 0071372334