Process Capability Analysis by means of Confidence Reliability Calculations
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| Description: | Overview: The webinar begins with a discussion of relevant regulatory requirements, as motivation for calculating "confidence/reliability". Then, some vocabulary and basic concepts are discussed. Next, detailed descriptions are given for 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. Spreadsheets are shown as examples of how to implement the methods described in the webinar. A final discussion is provided on how to introduce the methods into a company. Why should you Attend: All manufacturing and development companies perform testing and/or inspections that involve concluding whether or not a product or lot is acceptable vs. design or QC specifications. Such test/inspections may occur 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"). Such a method produces information that is more valuable than simply that 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: 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 Will Benefit: QA/QC Supervisor Process Engineer Manufacturing Engineer QC/QC Technician Manufacturing Technician R&D Engineer Speaker Profile John N. Zorich has spent 35 years in the medical device manufacturing industry; the first 20 years were as a "regular" employee in the areas of R&D, Manufacturing, QA/QC, and Regulatory; the last 15 years were as consultant in the areas of QA/QC and Statistics. His consulting clients in the area of statistics have included numerous start-ups as well as large corporations such as Boston Scientific, Novellus, and Siemens Medical. |
| URL: | http://www.compliance4all.com/control/w_product/~product_id=501050LIVE |
| Status: | Deleted |
| Date: | Tuesday, December 6, 2016 |
| Time: | 3:00pm-4:30pm UTC |
| Duration: | 1 hour 30 minutes |
| Access: | Public |
| Category: | Business*, Education*, Environment*, Food and Beverage*, Health*, Webinar* |
| Created by: | compliance4all |
| Updated: | Friday, October 28, 2016 10:45am UTC |
| : | One Dial-in One Attendee Price: $150.00 |
| : | 18004479407 |
| : | support@compliance4All.com |
| : | Event Manager |
| : | 18004479407 |
| Comments: | None |