Six Sigma Quality Management: Prof. H. Moskowitz
Overview
The data-driven business strategy known as Six Sigma is one of today’s hot-button issues. By training
managers and professionals to use specialized measurement and statistical tools, organizations can reduce
defects and errors in products, processes, and services. In turn, costs that are usually passed on to the
customer are cut, also reducing cycle time and variation.
This short course is focused on the concepts, tools, and applications of Six Sigma Quality to manage and
improve processes and systems. It is partitioned into two one-week modules: (a) Module 1 (Week 1):
Overview and Fundamentals, and (b) Module 2 (Week 2): Advanced Concepts and Applications.
Module 1 will focus on:
1. Quality management and the business strategy behind Six Sigma as a rigorous problem
prevention and solution methodology. A key aspect of the Six Sigma strategy is DMAIC
(Define, Measure, Analyze, Improve, and Control), a scientific approach to problem prevention
and problem solving
2. Fundamentals of statistical process control (SPC), process capability, and measurement capability
for variable (quantitative) and attribute (qualitative) data to measure, analyze, and control
processes / systems.
Module 2 will focus on:
1. Concepts and applications of Design of Experiments (DOE) and Response Surface Design to
improve/optimize processes / systems
2. A team-based competition, where the tools learned in Modules 1 and 2 will be applied to improve
an animated, simulated manufacturing system.
Learning Approach
1. Using various exercises and databases, participants will actively learn and apply key Six Sigma
principles and tools to measure, analyze, improve, and control processes
2. Course will be computer intensive using Excel as a data platform, Minitab 15 for statistical
analysis and optimization, and an animated manufacturing system simulator application using
ExtendSim
3. Short readings and computer exercises will be assigned in preparation for each class session.
A preliminary, abridged syllabus (listing topics) for Modules 1 & 2 is attached along with some of the
tools used in the course.
Six Sigma Quality Management
Tentative Abbreviated Syllabus*
Week 1
Session
1
Topics
Overview of Six Sigma as a Business Strategy
Macroeconomics and Microeconomics of Quality
2
Fundamentals of Statistical Process Control, Process Capability, and Measurement
Capability… Variable (Quantitative) Data… Printed Circuit Board (PCB) Exercise
3
Fundamentals of Statistical Process Control, Process Capability, and Measurement
Capability… Attribute (Qualitative) Data… Simulation of Chassis Assembly Exercise
4
Six Sigma Simulator of a Manufacturing Process: Introduction and Demonstration of
Animated Simulation
Week 2
1
Introduction to Design of Experiments (DOE)
Full Factorial Designs
2
Sequential Experimentation
Fractional Factorial Designs and Multiple Response Optimization (MRO)
3
Response Surface Design and MRO
4
Six Sigma Process Improvement Team Competition Using Six Sigma Animated
Simulator
Software

Excel… Data Platform

Minitab 15… Statistical Analysis and Optimization Tools

ExtendSim… Animated Simulation Application
* Each session is based on 180 minutes (or four, 45-minute modules per session)
Tools Taught
Six Sigma as a Business Strategy
 Macroeconomics of Quality
 Microeconomics of Quality
 Where Can Six Sigma be Deployed?
DMAIC Steps
Tools Used
 Identify Customers and Requirements
 Define Problem: QFD & House of Quality
 Create Process Map
Define Phase
 Flowcharting
 QFD & House of Quality
 CTQ & CTC Definitions




Measure Phase
 Process Flowchart
 Pareto Analysis w/ and w/out Data
 Cause-Effect Analysis
 Failure Modes and Effects Analysis (FMEA)
Define Defect, Defectives, & Opportunity
Validate the Measurement System
Collect the Data
Determine Process Capability and Sigma
Baseline
 Define Performance Objectives
 Identify Sources of Variation
 Perform Design of Experiments
 Validate Potential Improvement Using
Simulation
 Correct/Re-evaluate Potential Solution
 Detecting Out-of-Control Conditions
 Implement Statistical Process Control
 Determine Process Capability
 Measurement Capability Analysis
 Process Sigma Calculation
Analyze Phase
 Histogram
 SPC Charts
 Statistical Analysis (Univariate/Mulitvariate)
 Hypotheses Testing (Continuous and
Discrete)
 Non-normal Data Analysis
Improve Phase
 Screening Experiments
 Full & Fractional Factorial Designs
 Response Surface Design & Optimization
Control Phase
 Calculate Process Sigma
 Variable and Attribute Control Charts