Applying Statistical Process Control Effectively
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Many companies have failed to achieve the potential benefits from the application of statistical process control due to widespread misunderstanding and misapplication of the methods. When misapplied, great improvements in quality and productivity are not achieved. The consequences of the improper application of SPC include: devastating inefficiencies, poor purchasing decisions, a false sense of reality, and processes controlling human behavior rather than the humans controlling process behavior.
Why should you Attend: Learning objectives
Areas Covered in the Session:
- Understand SPC terminology and basic concepts
- Understand the most common mistakes in applying SPC
- Know the difference between process stability and process capability along with the appropriate methods for identifying both aspects (including an understanding of non-Normal distributions)
- Be able to make immediate improvements in the implementation of SPC in your organization
Who Will Benefit:
- A brief overview of the purpose of Statistical Process Control
- SPC Fundamentals
- Common misunderstandings and misapplications of SPC.
- They include:
- the belief that control charts provide an indication of process capability
- the belief that specification limits are related to control limits
- a misunderstanding of chart sensitivity to detect process changes (proper sample size selection)
- the limitations of traditional control charts (e.g. Xbar-R) for many modern production processes
- charting the wrong characteristics
- when the normality of data matters
- the shortcomings of process capability indices
- when control limits should be updated
- how best to apply SPC to short production runs
- Operations / Production Managers
- Quality Assurance Managers
- Process or Manufacturing Engineers or Managers
- Product Design Personnel
- Research & Development personnel
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.