Overview
Scientists, Design Engineers, and Manufacturing/Process Engineers must develop product and process specifications that ensure that products delivered to customers perform their intended functions over time. If specifications are too wide, the risks of inadequate product performance and product failures increase. If specifications are too tight, the costs to ensure conformance increase. Scientific and engineering theory, knowledge, and principles play an important role in developing specifications, but usually this must be combined with testing and data analysis to verify appropriate specifications.
This webinar covers useful and important statistical methods that assist scientists and engineers in the development of appropriate product and process specifications. Appropriate product specifications are critical to achieving adequate and reliable product performance.
Webinar Takeaway
The webinar topics include:
Introduction
- What are Specifications?
- Why Are Specs Important
- Risks of Inappropriate Specifications
Characterizing Process Data
- Normal Distribution
- Characterizing Process Data
- Reference Intervals
- Min - Max Interval
- Tolerance Intervals
- Coverage Probability and Confidence Levels
Using Predictive Models to develop specifications
- Review of Predictive Models (Regression/DOE)
- Confidence and Prediction Intervals
- Using Models Examples (Contour Plots)
- Factor Specifications to Optimize a Response
- Factor Specifications to Jointly Optimize Multiple Responses
- Introduction to Monte Carlo Simulation for further Optimization
Who Will Benefit
- Quality Personnel
- Product Design Engineer
- Scientists
- Process Engineer
- Manufacturing Engineer
- Product / Program Manager
- Operations / Production Manager
Faculty Steven Wachs
Statistician Specializing in Product Reliability and Quality
Dexter, Michigan, United States
Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.
Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as to estimate and reduce warranty. In addition to providing consulting services, Steve regularly conducts workshops in industrial statistical methods for companies worldwide.
