Acceptance Sampling Plans for Process Validation and Production Lot Monitoring

The information gained in the webinar will allow you develop statistically sound sampling plans that manage the risks inherent when making decisions based on sample data.

Steven Wachs
Instructor:
Steven Wachs
Date:
Wednesday, January 22, 2020
Time:
10:00 AM PST | 01:00 PM EST
Duration:
90 Minutes

More Trainings by this Expert   Product Id : 502853

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

Personnel involved in process validation and production control often rely on sampling methods to determine the suitability of a process before moving to production (process validation) or for checking production lots for acceptance.

This webinar provides details regarding the generation of sampling plans that meet the desired statistical properties. By attending this webinar, participants will be able understand the key inputs and issues involved in determining acceptance sampling plans.

Although software is generally used to generate sampling plans, the participants will gain useful insight into the methodology and its use in typical applications.

Sampling plans for attribute data are the primary focus although variable acceptance sampling plans are presented as well. The binomial distribution and its use in developing Operating Characteristic (OC) Curves is discussed. The key inputs to determining sampling plans (AQL, RQL, Consumer's and Producer's Risks) are described in detail. Key characteristics of the generated sampling plans (such as average outgoing quality) are presented. Double sampling plans are briefly introduced.

Several example applications of acceptance sampling are presented. The use of Statistical Process Control and Process Capability methods are presented as an alternative to variable acceptance sampling plans.

Why should you Attend: The information gained in the webinar will allow you develop statistically sound sampling plans that manage the risks inherent when making decisions based on sample data.

  • Understand the Acceptance Sampling Problem and Objectives
  • Understand the necessary inputs and how to specify them for generating a sampling plan
  • Learn how to quantify the risks of making mistakes that are inherent in any acceptance sampling plan
  • Establish Guidelines for establishing quality levels (for Process Validation vs. Production)
  • Understand key characteristics of a generated sampling plan
  • Compare alternate sampling plans
  • Understand alternatives to Acceptance Sampling for controlling quality

Areas Covered in the Session:
  • Acceptance Sampling Plans for Attribute Data
    • Sampling Plans and Applications
    • Binomial Distribution
    • OC Curves
    • Acceptable Quality Level (AQL)
    • Rejectable Quality Level (RQL)
    • Consumer's and Producer's Risks
    • Generating and Comparing alternative plans
    • Accounting for risk severity when specifying AQL and RQL
    • Average Outgoing Quality
    • Average Total Inspection
    • Double Sampling Plans
  • Acceptance Sampling Plans for Variable Data
    • Sampling Plans and Applications
    • Limitations of Variable Sampling Plans
    • Alternatives (Statistical Process Control, Process Capability)

Who Will Benefit:
  • R&D Personnel
  • Product Development Personnel
  • Quality Personnel
  • Lab Testing Personnel
  • Operations / Production Managers
  • Quality Assurance Managers, Engineers
  • Process or Manufacturing Engineers or Managers
  • Program or Product Managers


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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