Testing and/or inspections to determine whether or not a product or a lot is acceptable vs. design or QC specifications are performed by all manufacturing and development companies. These test/inspections may happen during design verification/validation or during incoming or final QC.
The most informative method for analyzing the data that results from such activities is the calculation of the product’s or lot’s “reliability” at a chosen “confidence” level (where “reliability” means “in-specification”). Confidence/Reliability Calculations method produces information that is far more valuable than simply if the given product or lot “passed” (as is the case when “AQL Attribute Sampling Plans” are used) or a % in-specification statement without any corresponding confidence statement (as is the case with AQL Variables Sampling Plans and with Process Capability calculations).
The output of a “Confidence/Reliability” calculation is a definitive statement that the given product or lot has a specific % in-specification, which conclusion we can state with a specific level of confidence (e.g., 95% confidence of 99% reliability, or 90% confident of 93% reliability”).
Areas Covered in the Session :
The webinar will begin with a discussion of the relevant regulatory requirements currently in place as incentive for calculating “confidence/reliability”. This will be followed by some vocabulary and basic concepts.
Next, detailed overview on how to calculate confidence/reliability for data that is either pass/fail (i.e., “attribute” data), normally-distributed measurement data, non-normally distributed measurement data that can be transformed into normality, or non-normally distributed measurement data that cannot be transformed into normality. Examples using spreadsheets will be shown during the session as of how to implement the methods. Finally the session will close with a discussion on how to introduce the methods into a company.
All the above is captured in these bullet points:
- Regulatory Requirements
- Vocabulary and Concepts
- Attribute Data
- Normal Data
- Normal Probability Plotting
- Non-Normal Data that can be normalized
- Reliability Plotting (for data that cannot be normalized)
- Implementation Recommendations
Who Should Attend:
A must attend webinar for all:
- QA / QC Supervisors
- Process Engineers
- Manufacturing Engineers
- QA / QC Technicians
- Manufacturing Technicians
- R&D Engineers