Training Options

  • Recorded (only for one participant)
        US$399.00   US$760.00
    You Save: US$361.00 (47%)*

    Corporate Recorded (Any number of participants)
        ¤699.00   ¤1,560.00
        You Save: $861 (55%)
    Webinar Packs Access recorded version only for one participant; unlimited viewing for 6 months.
    (For Customize Webinar Packs Please Call Customer Care)

  • Refund Policy


Instructor : John N. Zorich
Product Id : 31001PACK

Overview: The pros and cons of the 2 most widely used sampling plans (ANSI Z1.4, and Squeglia's C=0) are examined in detail, focusing especially on the weaknesses of such plans in regards to meeting regulatory requirements. Real-world examples are provided for how using such sampling plans leads to production of non-conforming product.

The advantages of "confidence/reliability" calculations are explained. Such calculations are demonstrated for Attribute data (pass/fail, yes/no data) as well as for variables data (i.e., measurements). If variables data is "Normally distributed" the calculations are extremely simple. The webinar explains how to handle "non-Normal" data, and provides the methods, formulas, and tools to handle non-normality.

The webinar includes a discussion of how one OEM manufacturer has implemented "confidence/reliability" calculations instead of AQL sampling plans for all of its clients. And suggestions are given for how to use "confidence/reliability" QC specifications instead of "AQL" QC specifications. The use of "reliability plotting" for assessing product reliability during R&D is also discussed. The webinar also includes an examination of ISO and FDA regulations and guidelines regarding the use of statistics, especially in regards to Sampling Plans.

Why should you Attend: Almost all manufacturing companies spend time and money to inspect purchased parts upon receipt, in order to evaluate part quality before the parts Supplier is paid. "AQL" sampling plans are used almost universally for such inspections. However, AQL plans actually provide very little information about part quality.

A better way to assess the quality of purchased parts is to use "confidence/reliability" calculations. Such calculations are very easy to perform using tables and/or an electronic spreadsheet. ISO 9001 and ISO 13485 requirements include establishing "processes needed to demonstrate [product] conformity"; FDA's GMP (21CFR820) requires that "sampling methods are ad-equate for their use". An AQL sampling plan does not provide what is needed to meet either of those requirements. FDA guidelines state that "A manufacturer shall be prepared to demonstrate the statistical rationale for any sampling plan used" --- it is not possible to "demonstrate" that an AQL sampling plan ensures product quality.

On the other hand, confidence/reliability calculations can be easily shown to provide evidence of product quality, and the statistical rationale for such calculations is easy to explain and demonstrate.

Areas Covered in the Session:
  • AQL and LQL sampling plans
  • OC Curves
  • AOQL
  • ANSI Z1.4
  • Squeglia's C=0
  • Confidence/Reliability calculations for
    • Attribute data
    • Normally-distributed variables data
    • Non-Normal data
  • Transformations to Normality
  • K-tables
  • Normal Probability Plot
  • Reliability Plotting

Who Will Benefit:
  • QA/QC Supervisor
  • Process Engineer
  • Manufacturing Engineer
  • QC/QC Technician
  • Manufacturing Technician
  • R&D Engineer


Instructor : Steven Wachs
Product Id : 31001PACK

Overview: The webinar will provide important considerations when selecting sample sizes for specific applications. The knowledge gained by attending the webinar will allow practitioners to consider the implications of sample size selection prior to conducting the study and ensure that the information obtained can be useful for decision making.

Areas Covered in the Session:
  • Population and Samples
  • Basic Statistics
  • Common Applications requiring sample size determination (e.g. estimation, hypothesis testing, demonstration of conformance to specification)
  • Sample Size Determination (Examples)

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
  • Business Analysts
  • Process Improvement Personnel
  • Management


Instructor : Steven Walfish
Product Id : 31001PACK

Overview: This webinar covers the statistical methods used to calculate sample sizes for both attribute and variables data. Methods for collecting the sample will be covered. Every sampling plan has risks. This webinar covers how to calculate Type I and Type II errors. A discussion of how the FDA views sampling plans, especially for validation and acceptance activities. Sample size to ensure a certain level of process capability will be covered.

Areas Covered in the Session:
  • How to collect a sample
  • What are the two types of error (Type I and Type II)
  • How to calculate sample sizes for variables data
  • How to calculate sample sizes for attribute data
  • Using confidence intervals on Cpk to calculate a sample size
  • Using binomial confidence intervals

Who Will Benefit:
  • Management
  • Research and Development
  • Regulatory Affairs Personnel
  • Quality Assurance/Quality Control Personnel
  • Auditors and Inspectors


Instructor : John N. Zorich
Product Id : 31001PACK

Overview: This webinar explains the logic behind sample-size choice for several statistical methods that are commonly used in verification or validation efforts, and how to express a valid statistical justification for a chosen sample size.

The statistical methods discussed during the webinar include the following:
  • Confidence intervals
  • Process Control Charts
  • Process Capability Indices
  • Confidence / Reliability Calculations
  • MTBF Studies ("Mean Time Between Failures" of electronic equipment)
  • QC Sampling Plans

Why should you Attend: Almost all manufacturing and development companies perform at least some verification testings or validation studies of design-outputs and/or manufacturing processes, but it is sometimes difficult to explain the rationale for the sample sizes used in such efforts. This webinar provides guidance on how to justify such sample sizes, and thereby indirectly provides guidance on how to choose sample sizes. Those justifications can then be documented in Protocols or regulatory submissions, or can be given to regulatory auditors who may ask for them during onsite audits at your company. Thus, this webinar is designed to help you avoid regulatory delays in product approvals and to prevent an auditor from issuing you a nonconformity.

NOTE: This webinar does not address rationales for sample sizes used in clinical trials.

Areas Covered in the Session:
  • Introduction
    • Examples of regulatory requirements related to sample size rationale
    • Sample versus Population
    • Statistic versus Parameter
  • Rationales for sample size choices when using
    • Confidence Intervals
      • Attribute data
      • Variables data
    • Statistical Process Control C harts (e.g., XbarR)
    • Process Capability Indices (e.g., Cpk )
    • Confidence/Reliability Calculation
      • Attribute data
      • Variables data (e.g., K-tables)
    • Significance Tests ( using t-Tests as an example )
      • When the "significance" is the desired outcome
      • When "non-significance" is the desired outcome (i.e., "Power" analysis)
    • AQL sampling plans
  • Examples of statistically valid "Sample-Size Rationale" statements

Who Will Benefit:
  • QA/QC Supervisor
  • Process Engineer
  • Manufacturing Engineer
  • QC/QC Technician
  • Manufacturing Technician
  • R&D Engineer


Payment Methods

Contact Us

NetZealous LLC,
161 Mission Falls Lane, Suite 216,
Fremont, CA 94539, USA.

Information

  Refund Policy
  +1-800-447-9407
  support@compliance4All.com