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Seminar on Risk Management in R&D, QA/QC, and Manufacturing at Seattle, Washington
- August 18, 2016
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Course
"Applied Statistics, with Emphasis on Risk Management in R&D, QA/QC,
and Manufacturing" 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 risk 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.
- 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 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, deciding to not launch a
new product because of incorrectly calculated product reliability or process
capability. In QC, mistakenly chosen sample sizes and inappropriate statistical
methods may result in product being rejected that should have passed, and
vice-versa.
This seminar provides a practical
approach to understanding how to interpret and use a standard tool-box of
statistical methods, including confidence intervals, t-tests, Normal K-tables,
Normality tests, confidence/reliability calculations, AQL sampling plans,
measurement equipment analysis, 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: Statistical Significance: ANOVA calculations
Lecture 5: Metrology (Gage R&R, Correlation, Linearity, Bias, and
Uncertainty Budgets)
Lecture 6: QC Sampling Plans (C=0 and Z1.4 attribute AQL plans, and
alternatives to such plans)
Lecture 7: Statistically valid statements for use in reports
Lecture 8: Summary and Implementation Recommendations
Exercise and Recap of Day 2
- Exercise
- Quiz
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: Seattle,
Washington Date: September 22nd & 23rd, 2016 and Time: 09:00 AM to 06:00 PM
Venue: Courtyard Seattle Sea-Tac Area
Address: 16038 West Valley Highway Tukwila
Washington 98188 USA
Price:
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