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Event :Statistical Process Control -Verification, Validation, Risk Management, Manufacturing, and QA/QC 2017

Dates :Thursday July 13th, 2017 - Friday July 14th, 2017

Location :By correspondence

Type :Conference & Seminar - International audience

Accreditation :--


 

Further information

Course "Applied Statistics, with Emphasis on Verification, Validation, and Risk Management, in R&D, Manufacturing, and QA/QC" has been pre-approved by RAPS as eligible for up to 12 credits towards a participant's RAC recertification upon full completion.

Overview:
The 2-day seminar explains how to apply statistics to manage risks and verify/validate processes in R&D, QA/QC, and Manufacturing, with examples derived mainly from the medical device design/manufacturing industry. The flow of topics over the 2 days is as follows:
•    ISO standards and FDA/MDD regulations regarding the use of statistics.
•    Basic vocabulary and concepts, including distributions such as binomial, hypergeometric, and Normal, and transformations into Normality.
•    Statistical Process Control
•    Statistical methods for Design Verification
•    Statistical methods for Product/Process Qualification
•    Metrology: the statistical analysis of measurement uncertainty, and how it is used to establish QC specifications
•    How to craft "statistically valid conclusion statements" (e.g., for reports)
•    Summary, from a risk management perspective

Why should you attend :
Almost all design and/or manufacturing companies evaluate product and processes either to manage risks, to validate processes, to establish product/process specifications, to QC to such specifications, and/or to monitor compliance to such specifications.
The various statistical methods used to support such activities can be intimidating. If used incorrectly or inappropriately, statistical methods can result in new products being launched that should have been kept in R&D; or, conversely, new products not being launched that, if analyzed correctly, would have met all requirements. In QC, mistakenly chosen sample sizes and inappropriate statistical methods may result in purchased product being rejected that should have passed, and vice-versa.
This seminar provides a practical approach to understanding how to interpret and use more than just a standard tool-box of statistical methods; topics include: Confidence intervals, t-tests, Normal K-tables, Normality tests, Confidence/reliability calculations, Reliability plotting (for extremely non-normal data), AQL sampling plans, Metrology (i.e., statistical analysis of measurement uncertainty ), and Statistical Process Control. Without a clear understanding and correct implementation of such methods, a company risks not only significantly increasing its complaint rates, scrap rates, and time-to-market, but also risks significantly reducing its product and service quality, its customer satisfaction levels, and its profit margins.

Areas Covered in the Session:
•    FDA, ISO 9001/13485, and MDD requirements related to statistical methods
•    How to apply statistical methods to manage product-related risks to patient, doctor, and the designing/manufacturing company
•    Design Control processes (verification, validation, risk management, design input)
•    QA/QC processes (sampling plans, monitoring of validated processes, setting of QC specifications, evaluation of measurement equipment)
•    Manufacturing processes (process validation, equipment qualification)
Who will benefit:
•    QA/QC Supervisor
•    Process Engineer
•    Manufacturing Engineer
•    QC/QC Technician
•    Manufacturing Technician
•    R&D Engineer

Agenda:
Day 1 Schedule
Lecture 1:
Regulatory Requirements
Lecture 2:
Vocabulary and Concepts
Lecture 3:
Confidence Intervals (attribute and variables data)
Lecture 4:
Normality Tests and Normality Transformations
Lecture 5:
Statistical Process Control (with focus on XbarR charts)
Lecture 6:
Confidence/Reliability calculations for Proportions
Lecture 7:
Confidence/Reliability calculations for Normally distributed data (K-tables)
Lecture 8:
Process Capability Indices calculations(Cp, Cpk, Pp, Ppk)

Day 2 Schedule

Lecture 1:
Confidence/Reliability calculations using Reliability Plotting (e.g., for non-normal data and/or censored studies)
Lecture 2:
Confidence/Reliability calculations for MTTF and MTBF (this typically applies only to electronic equipment)
Lecture 3:
Statistical Significance: t-Tests and related "power" estimations
Lecture 4:
Metrology (Gage R&R, Correlation, Linearity, Bias , and Uncertainty Budgets)
Lecture 5:
QC Sampling Plans (C=0 and Z1.4 attribute AQL plans, and alternatives to such plans), including OC curves, AQL vs. LQL/LTPD, AOQL, and calculation of acceptance rates.
Lecture 6:
Statistically valid statements for use in reports
Lecture 7:
Summary and Implementation Recommendations

Speaker

John N. Zorich
Statistical Consultant & Trainer, Ohlone College & SV Polytechnic


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. His experience as an instructor in statistics includes having given 3-day workshop/seminars for the past several years at Ohlone College (San Jose CA), 1-day training workshops in SPC for Silicon Valley Polytechnic Institute (San Jose CA) for several years, several 3-day courses for ASQ Biomedical, numerous seminars at ASQ meetings and conferences, and half-day seminars for numerous private clients. He creates and sells formally-validated statistical application spreadsheets that have been purchased by more than 75 companies, world-wide.
    
Location:  SFO, CA Date: July 13th & 14th, 2017 and Time: 9:00 AM to 6:00 PM    

Venue:  Hilton San Francisco Airport Bayfront
Address:  600 Airport Blvd, Burlingame, CA 94010
 Price:

Register now and save $200. (Early Bird)
Price: $1,295.00 (Seminar Fee for One Delegate)
Until June 10, Early Bird Price: $1,295.00 From June 11 to July 11, Regular Price: $1,495.00
Register for 5 attendees   Price: $3,885.00 $6,475.00 You Save: $2,590.00 (40%)*

Sponsorship Program benefits for “Risk Management, in R&D, Manufacturing, and QA/QC” seminar
At this seminar, world-renowned Risk Management subject matter experts interact with CXO’s of various designations. Executives who carry vast experience about Risk Management and Experts get down to discussing industry-related best practices, regulatory updates, changes in technologies, and much more relating to Risk Management.
As a sponsor of these seminars, you get the opportunity to have your product and company reach out to C-Level executives in Risk Management industries and become known among these elite executives and subject matter experts. Apart from being seen prominently at these globally held seminars, you also get talked about frequently in our correspondences with our experts and these participants.
For More Information- https://www.globalcompliancepanel.com/control/sponsorship  
 
Contact us today!
NetZealous LLC DBA GlobalCompliancePanel
john.robinson@globalcompliancepanel.com   
support@globalcompliancepanel.com  
Toll free: +1-800-447-9407
Phone: +1-510-584-9661
 Website: http://www.globalcompliancepanel.com

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GlobalCompliancePanel (GCP) is a specialized offering from NetZealous LLC, a Technology and Business Process Solutions and Services Company registered in Fremont, CA. It is a fountainhead for Continuous Professional Education, compliance training and consulting. GCP offers a broad range of high quality regulatory and [...]

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Statistical Process Control -Verification, Validation, Risk Management, Manufacturing, and QA/QC 2017 Medicine - Pharmacy
applied statistics course, applied statistics and management, statistical methods course, statistical methods and machine learning, statistical analysis methods, statistics and data analysis, statistical methods risk management, statistical analysis for risk management
Everyone, QA/QC Supervisor Process Engineer Manufacturing Engineer QC/QC Technician Manufacturing Technician R&D Engineer
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