Since the introduction of mass production, the concept of “quality” has evolved from simple assembly line inspections to a broad approach to production and management involving an entire corporation. Quality has become a critical driver for long-term success through continuous process improvement and customer satisfaction. Quality Management today concerns the entire value chain, encompassing multi-tiered supplier networks and customer service and returns.
This course balances the quantitative elements of quality engineering with a managerial approach to using quality in an organization to effect change. We cover the statistical basics needed for each of the well-known process-improvement cycle steps: Define, Measure, Analyze, Improve, and Control, covering the most important quality methods and techniques including sampling, statistical process control, process capability, regression analysis, and design of experiments. Quality assurance is examined, from the viewpoint of quality incorporated into product design, measuring and controlling quality in production and improving quality using quantitative problem-solving and interactive, guided exercises.
The contents of this course are essentially the same as those of the corresponding TUM class (Quality Engineering and Management) and will enable you to immediately understand and apply quality concepts in your work and research.
What you'll learn:
- The fundamentals for quality engineering and management
- The statistical basics to apply the DMAIC process-improvement cycle to your work and research
This course is part of the Lean Six Sigma Yellow Belt: Quantitative Tools for Quality and Productivity Professional Certificate and Lean Six Sigma Green Belt Certification Professional Certificate
What you'll learn
- To understand the background and meaning of the Six Sigma methodology and the role of the DMAIC process improvement cycle.
- To identify the Voice of the Customer and translate into Critical-to-Quality parameters.
- To understand the concept of random variables, probability mass functions, and probability density functions.
- To calculate probabilities using the Normal distribution.
- To understand how the Central Limit Theorem applies to sampling and how to set up sampling plans.
- To understand the importance of a Measurement System Analysis in a Six Sigma project.
- To calculate Process Yield and Process Capability.
- To perform a risk assessment using a Failure Modes and Effects Analysis.
- How to apply the Define and Measure phases of the DMAIC cycle in your work or research, in order to identify problems and quantitatively assess the impact of process changes using statistical analysis.
Syllabus
Week 1: Six Sigma Introduction
Introduction to the Six Sigma Methodology and the DMAIC process improvement cycle. Understand the contributors to the cost of quality. Discuss the difference between defects and defectives in a process and how to calculate process yield, including a comparison of processes of different complexity using the metric DPMO.
Week 2: DEFINE - Defining the Problem
Discuss how to understand customer expectations, using the Kano Model to categorize quality characteristics. Start the first and difficult task of a Six Sigma project, Defining the Problem, and review the key content in a Project Charter.
Week 3: MEASURE - Statistics Review
Review of random variables and probability distributions used commonly in quality engineering, such as Binomial, Poisson, and Exponential. Cover descriptive statistics, emphasizing the importance of clearly communicating the results of your project.
Week 4: MEASURE - Normal Distribution
Learn the characteristics of the Normal Distribution and how to use the Standard Normal to calculate probabilities related to normally distributed variables. Cover the Central Limit Theorem, and how it relates to sampling theory.
Week 5: MEASURE - Process Mapping
Introduce Process Mapping, including SIPOC and Value Stream Mapping. We identify the Critical-to-Quality characteristic for a Six Sigma project
Week 6: MEASURE - Measurement System Analysis
Learn the basics of Measurement Theory and Sampling Plans, including
Precision, Accuracy, Linearity, Bias, Stability, Gage Repeatability & Reproducibility
Week 7: MEASURE - Process Capability
Introduction to Process Capability and the metrics CP/CPK for establishing our baseline process performance.
Week 8: Quality Topics and Course Summary
Cover the basics of Tolerance Design and the risk assessment tool failure Mode and Effects Analysis (FMEA).
Review the complete Six Sigma Roadmap before summarizing and closing the course.