10 Keys to Maximize the Benefits of Measurement Systems Assessments

This webinar emphasizes the importance of validating measurement systems for data reliance, risk minimization, and regulatory compliance, highlighting improvements companies can make to their assessment methods.
Thursday, August 14, 2025
Time: 10:00 AM PDT | 01:00 PM EDT
Duration: 90 Minutes
IMG Steven Wachs
Id: 90505
Live
Session
$119.00
Single Attendee
$249.00
Group Attendees
Recorded
Session
$159.00
Single Attendee
$359.00
Group Attendees
Combo
Live+Recorded
$249.00
Single Attendee
$549.00
Group Attendees

Overview:

Important measurement system characteristics include discrimination, accuracy, precision (repeatability and reproducibility), linearity, and stability. Techniques exist to assess measurement systems for each of these important characteristics. Skipping such assessments can lead to the use of measurement systems that are not capable of monitoring process variation or, in extreme cases, even of distinguishing between conforming and non-conforming products.

In short, validating measurement systems is an important pre-requisite to relying on data. Measurement systems must be properly assessed to minimize risk and comply with customer and regulatory requirements. While most companies perform some aspects of MSA, such as Gage Repeatability & Reproducibility studies, we often observe inadequate assessments of measurement systems. In addition to an overview of MSA methods, this webinar identifies many improvements that most companies can make to their measurement systems assessments.  

Why you should Attend: 

  • Develop a solid understanding of the types of Measurement Systems Assessments that may be conducted
  • Improve the planning, conduct, analysis, and interpretation of Gage R&R studies
  • Ensure prerequisites for a measurement system study are satisfied
  • Learn techniques for handling destructive testing or other non-replicable measurement systems

Areas Covered in the Session:

This program will discuss numerous ways in which companies can improve their Measurement Systems Assessments:

  • Understand and Consider All Types of Measurement Error (Repeatability, Reproducibility, Bias, Non-linearity, Instability)
  • Select Specimens Wisely for Gage R&R Studies
  • Ensure Adequate Gage Discrimination  
  • Understand, Calculate, and Interpret Gage R&R Metrics Correctly  
  • Look Beyond the “Pass” or “Fail” Outcomes in a Gage R&R study  
  • Use ANOVA for Gage R&R Studies  
  • Expand Gage R&R Studies to Include Potential Sources of Variation 
  • Apply Methods for Non-Replicable Systems as Necessary 
  • Use Control Charts to Assess the Stability of the Measurement Process  
  • Assess Attribute Gages as Well 

Who Will Benefit:

  • Quality & Process Engineers
  • Quality Technicians
  • SPC Supervisors
  • Production Supervisors
  • Personnel involved in process development and validation
  • Manufacturing/Operations Personnel
  • Process Improvement Personnel
  • Supplier Quality Personnel
  • Product Design/Development Personnel
  • R&D Personnel

Speaker Profile

Steve Wachs has 30 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 estimate and reduce warranty. Steve consults and provides workshops in industrial statistical methods worldwide. He also supports Integral Concepts’ Litigation / Expert Witness practice with data analysis.

Steve possesses an M.A. in Applied Statistics from University of Michigan (Ann Arbor), an M.B.A. from the Katz Graduate School of Business, University of Pittsburgh, and a B.S. in Mechanical Engineering from the University of Michigan (Ann Arbor).