Sampling Plans for Process Validation and Production Lot Acceptance

Instructor: Steven Wachs
Product ID: 704315
  • 3
  • April 2017
  • 10:00 AM PDT | 01:00 PM EDT
    Duration: 60 Min

Live Online Training
April 03, Monday 10:00 AM PDT | 01:00 PM EDT
Duration: 60 Min

One Dial-in One Attendee
Group-Max. 10 Attendees/Location
(For multiple locations contact Customer Care)

recorded version

1x Person - Unlimited viewing for 6 Months
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Recorded Link and Ref. material will be available in My CO Section 48 hrs after completion of Live training

Training CD

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CD and Ref. material will be shipped within 15 business days after completion of Live training

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Read Frequently Asked Questions

This webinar will cover acceptance sampling plans for process validation and production lot acceptance. 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 will be discussed.

Why Should You Attend:

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 will provide details regarding the generation of sampling plans that meet the desired statistical properties. Participants will be able to understand the key inputs and issues involved in determining acceptance sampling plans. Although software is generally used to generate sampling plans, participants will gain useful insight into the methodology and its use in typical applications.

The course will detail:

  • Key inputs to determining sampling plans (AQL, RQL, consumer's and producer's risks).
  • Key characteristics of the generated sampling plans (such as average outgoing quality).
  • Double sampling plans.
  • Several example applications of acceptance sampling.
  • The use of statistical process control and process capability methods as an alternative to variable acceptance sampling plans.

Learning Objectives:

  • 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
  • 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 Webinar:

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:

  • Quality Personnel
  • Manufacturing Personnel
  • Plant Operations
  • Supplier Quality Personnel
  • Quality Engineering
Instructor Profile:
Steven Wachs

Steven Wachs
Principal Statistician, Integral Concepts, Inc

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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