Six Sigma Data Measurement, Analysis and Tools
This course provides both the theoretical background and practical skills necessary to effectively apply the Six Sigma methodology in your business. It uses the powerful and proven concepts developed in statistical process control and provides you with the knowledge, tools and guidelines to apply them quickly and effectively within the DMAIC model. The focus is less on theory and more on how to apply these influential tools to conduct projects in your business. Understanding proper data collection methods and using the proper analysis tool from the myriad of possibilities will be emphasized. Data analysis techniques to uncover the root causes for process failure will be investigated, as well as how to implement lasting controls for sustained process improvement success.
You’ll learn to:
- Differentiate between discrete and continuous data and how it affects your analysis
- Use and apply Gage R&R
- Calculate sample size and use it in determining the scope of data collection activities
- Select the proper analysis tool for a specific situation: Pareto charts, histograms, scatter plots, normality tests, ANOVA, HOV, Mood’s median, correlation and regression analysis, t-test, f-test and chi-square
- Validate and measure the effect of process improvements
- Choose and apply the proper control tool: I-MR; Xbar; u-chart; p-chart; and line, run, bar and pie charts
Outline
Statistics
- Populations, samples and processes
- Characteristics of the normal curve
- Measuring center and variability of a process
- Introduction to basic graphing techniques
- Use of Pareto, histogram and scatter plots
Define Phase
- Project prioritization using a simple decision matrix or QFO chart
- Defining objectives in the context of the organization
Measure Phase
- Discrete versus continuous data and the effect on analysis
- Sample size and its effect on data collection
- Using and applying Gage R&R
Analyze Phase
- Determining baseline process capability
- Selection of proper analysis tools
- Normality testing
- Hypothesis testing and tools, including ANOVA (Analysis of Variance), HOV (Homogeneity of Variance), t-test and more
- Types of process errors
Improve Phase
- PM/CE/CNX/SOP (process map, cause & effect, control/noise/X-variable, standard operating procedure)
- Design of experiments (DOE)
- Validating and measuring the effects of process improvements
Control Phase
- Types of controls
- Choosing and applying the proper controls, such as I-MR (individual and moving range), Xbar, u-charts and more
- Defining the sustain solution
- Process monitoring
Schedule
Evening before course — Optional dinner for those arriving early
Day 1 — 8:00 a.m – 4:30 p.m. Registration/Session
Day 2 — 8:00 – 4:30 p.m. Session
Day 3 — 8:00 – 3:30 p.m. Session
Day 3 — 3:30 p.m. Exam
Program Faculty
Scott B. Converse
Scott Converse is the Director of Technology Programs for the University of Wisconsin-Madison School of Business. Working within the Executive Education department, he oversees courses in Six Sigma, purchasing and supply, and information technology. Scott developed and oversees the Technical Leadership Certificate series, a collaborative effort between the College of Engineering and the School of Business. He is also a lecturer in the University of Wisconsin-Madison School of Business’ MBA program.
Scott has developed technology-based training courses for a variety of audiences ranging from the computer novice to the IT professional. Clients have included Fortune 500 firms, the U.S. military, state and local government and not-for-profit agencies. He is engaging, interactive and clear in the presentation of technical material. His ability to break down complex topics, use analogies to develop understanding and include visual and group activities in traditionally lecture-based subjects helps to make learning fun.
Areas of expertise include internetworking concepts and information technologies, predicting and managing emerging technologies, technology project implementation, the role technology will play on the future of business, data analysis and data mining techniques. He also has over a decade of applied experiences in the field as a former IT director for the University of Wisconsin, and technologist for an Internet working software developer.
Scott received his MBA from the University of Wisconsin-Madison, and he holds a B.S. degree in Physics from UW-Eau Claire.
Allen B. Gates
Allen B. Gates' work experience spans industry, government, and academia. He is currently President and founder of Renaissance Consulting. Concurrently, he holds adjunct faculty positions at the University of Nevada, Reno, University of Minnesota, and University of Wisconsin. His work experience includes: System Engineer, Project Manager and Line Manager at the Naval Weapons Center, China Lake, CA; Long Range Planning Manager, Project Manager, Advanced Technology Director, Electro-Optics Vice-President, Vice-President Technical Affairs, Senior Vice President Engineering at Ford Aerospace; President, Kaiser Electronics; President, Gradient Technology; Vice President, Programs, Vice-President, Manufacturing at General Dynamics Information Systems; and Partner, Dannemiller Tyson Associates.
Allen’s educational credentials include: BSME and MSME, University of Nevada, Reno, SM Mgt, MIT, and Ph.D. System Engineering, Case-Western Reserve University. He teaches courses in: project management, systems engineering, manufacturing system design and simulation, management of innovation and technology, and management of information technology.
Allen was a Naval Fellow while studying for his Ph.D. and a Sloan Fellow while studying for his SM in Mgt. He is a co-author of two books: Whole Scale Change: Unleashing the Magic in Organizations and the Whole Scale Change Toolkit.
For information, call 1-800-292-8964
The University of Wisconsin-Madison, as a member of the University Continuing Education Association (UCEA), authorizes this course for 2.1 Continuing Education Units (CEUs) or 21 hours.