Acceptance Sampling for Process Validation and Production Lot Monitoring

On-Demand Schedule Fri, September 29, 2023 - Fri, October 06, 2023
Duration 90 Mins
Level Intermediate
Webinar ID IQW20A0110

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)

Overview of the webinar

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 to understand the key inputs and issues involved in determining acceptance sampling plans. Although the 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.

Who should attend?

  • 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

Why should you attend?

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

  • 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 the quality

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.
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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