Better Alternatives to AQL Sampling Plans for Risk Management in Incoming QC webinar at fremont 2017

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.

Product Id : 501099
Instructor : John N. Zorich

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.

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

IMPORTANT DATE:

Webinar Date: Tuesday, January 24, 2017 | 10:00 AM PST | 01:00 PM EST

Duration: 90 Minutes

Others Details

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