10 Ways to Improve Measurement Systems Assessments

This webinar identifies many improvements that most companies can make to their measurement systems assessments. In addition, you will learn the techniques for handling destructive testing or other non-replicable measurement systems.

Steven Wachs
Instructor:
Steven Wachs
Date:
Monday, March 30, 2020
Time:
10:00 AM PST | 01:00 PM EST
Duration:
90 Minutes

More Trainings by this Expert   Product Id : 502857

Price Details
$150 Live
$290 Corporate Live
$190 Recorded
$390 Corporate Recorded
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Overview:

The effective use of data to drive decision making requires adequate measurement systems. When interpreting data or the results of data analysis, we assume that data or results represent the process. However, excessive measurement error may result in inappropriate conclusions

Thus, it is critical to properly assess whether measurement systems are adequate for their intended use prior to their use. Only capable measurement systems should be utilized to support quantitative methods such as Statistical Process Control, Inspection activities, Process Capability Assessment, Hypothesis Testing, Data Modeling, etc.

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 product.

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 additional to an overview of MSA methods, this webinar identifies many improvements that most companies can make to their measurement systems assessments.

Why should you 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:
  • 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:
  • Product Development Personnel
  • Quality Personnel
  • Manufacturing Personnel
  • Lab Personnel


Speaker Profile
Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. He 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. Mr. Wachs 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. Mr. Wachs regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

He has an M.A. in Applied Statistics from the University of Michigan, an M.B.A, Katz Graduate School of Business from the University of Pittsburgh, 1992, and a B.S., Mechanical Engineering from the University of Michigan.


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