Ten Keys for Maximizing the Benefits of your Measurement Systems Assessments

Duration 90 Mins
Level Intermediate
Webinar ID IQW20H0802

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

Overview of the webinar

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.

Who should attend?

The target audience includes personnel involved in process development, manufacturing, quality, program management, and business operations.

  • 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

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

Faculty - Mr.Steven Wachs

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.
 
Education:
M.A., Applied Statistics, University of Michigan, 2002
M.B.A, Katz Graduate School of Business, University of Pittsburgh, 1992
B.S., Mechanical Engineering, University of Michigan, 1986

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