
Training materials: Texts: Basic Statistics: Tools for Continuous Improvement, Understanding Industrial Designed Experiments, DFSS: The Tool Guide for Practitioners; Software: SPC XL, DOE PRO, DFSS Master
COURSE AGENDA:
Introduction to Design for Six Sigma (DFSS)
The DFSS Scorecard
Identify: The DFSS Project and Team
Identify: The Voice of the Customer (VOC)
Design: Concept Generation and Selection; Product Design and Requirements Flowdown
Design and Optimize: Transfer Functions and Expected Value Analysis (EVA)
Optimize: Robust Design
Optimize: Tolerance Allocation
Design for X-ability and Scorecard Updates
Validate: Sensitivity Analysis
Validate: The Achievement of Breakthrough Performance
DFSS Case Studies and Examples
The Static - Applying the IDOV Methodology
References and Course Evaluation
COURSE GOALS:
- Understand what DFSS is all about and how it differs from traditional Six Sigma
- To understand and apply Six Sigma/DFSS tools and concepts for the purpose of gaining knowledge and improving the bottom line.
- Be able to construct a DFSS Scorecard to predict yield and identify problem areas for resource allocation and attention
- Become familiar with methods for identifying the voice of the customer and understand how to construct a House of Quality
- Understand methods for evaluating risk including failure mode and effect analysis (FMEA)
- Be able to complete a Pugh analysis to select the best concept or help narrow the focus
- Understand the use and importance of transfer functions in DFSS and methods for generating them
- Be able to complete an expected value analysis (EVA) for predicting product performance using transfer functions
- Understand how to apply robust design analysis for reducing variation and minimizing sensitivity to noise
- Be able to complete a tolerance allocation analysis to understand the impact of design variables on performance
- Understand the importance and role of designing for manufacturability and other key aspects (DFX) in addition to designing for performance (DFP)
- Understand the importance of testing and validating prediction models and strategies for gap analysis when results do not match
- Practice applying the DFSS IDOV process using an in-class design exercise
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