Statistical Process and Quality Control


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This 2-day seminar includes the steps and techniques used to quantify variability in manufacturing processes, and to assure quality products.

The concepts and information presented will be mainly concerned with statistical quality control: obtaining information (data) that is objective, unbiased, and useful for decision making. An emphasis will be placed on the set-up and use of control charts and acceptance sampling systems and procedures.

The objective of the seminar is to provide information that can be used immediately by personnel involved in production operations, and by supervisors and management in decision making. Although the presentation involves use of statistical techniques, presentation of statistical theory will be limited to only what is needed by the attendees to understand and implement processes and testing within the statistical framework.

Presented examples will include an emphasis on the manufacturing processes and quality assurance needs of personnel in the medical device and pharmaceutical industries.

Process and quality control are constantly evolving. Therefore, historical concepts, current trends and regulatory requirements will be discussed. The presentation of statistical charts and analyses, graphical techniques for planning, trouble-shooting and problem solving will also be presented.

Instructions and Complementary Handouts:

  • Minitab statistical software will be used to demonstrate data collection and input, and how to build and interpret various process control charts for both attributes and variables data.
  • The seminar will also include the use of Minitab to develop attributes and variables sampling plans for quality assurance and acceptance.
  • A handout and dataset will be provided to attendees, so they may work hands-on with the information presented in the seminar.

Learning Objectives:

  • Understanding of the need and reasons for statistical process control
  • Knowledge of regulations and requirements of sound process control and acceptance sampling plans
  • Understanding and application of statistical concepts in the development and use of process control and sampling plans
  • Step-by-step instructions on how to design and interpret control charts and sampling plans using Minitab software

Areas Covered :

  • FDA QSR, ISO 13485:2016, ISO 9001:2015 regulations and harmonization
  • Overview of statistical theory and nomenclature for processes and quality control
  • Variability and risk assessment in practice
  • Control before testing!
  • Which chart for which process?
  • Presentation of control charts for discrete and continuous data
  • Setting statistical specifications, control limits, and signals
  • Attribute and Variables sampling plans
  • C = 0/Zero acceptance sampling plans

Who will Benefit:

  • Quality assurance (QA) engineers
  • Quality control (QC) engineers
  • R&D engineers
  • Process control personnel
  • Manufacturing/Industrial personnel
  • Manufacturing/Industrial personnel
  • Production supervisors
  • Management personnel of processing facilities

Topic Background:

In pharma and medical device companies, all processes exhibit intrinsic variation. However, sometimes the variation is excessive and this hinders the ability to achieve reliable measurements and desired results. Statistical process control (SPC) and statistical quality control (SQC) allow us to control the functions of our processes (input) and the quality of our product (output) by providing tangible tools for monitoring and testing.

Process and quality control is important for a company's reputation. A good system of processing and quality checks reduce costs associated with production waste and re-work due to defects, and allows a company to deliver products that are high in quality. Many industries are also required to have a good quality management system in place to achieve compliance with regulatory authorities.

DAY 01(8:30 AM - 4:30 PM)
  • 8:30-8:59 Registration Meet & Greet.
  • 9:00 AM -10:30 AM Introductions: Seminar objectives review, expectations and scope. Session 1(90 Mins): It’s a System! Elements of Quality Management
    • Deming 14 points for total quality management
    • Dr. Ishikawa, seven quality control tools (7-QC) and supplementals (7-SUPP)
    • Pareto principle (80/20 rule)
    • Shewhart (Plan, Do, Study, Act)
  • 10:30 AM -12:00 Noon Session 2(90 Mins): Regulatory Requirements in Quality Management
    • FDA Quality System Regulation (QSR)
    • ISO 13485:2016
    • ISO 9001:2015
  • 12:00 Noon-1:00 PM Lunch
  • 1:00 PM-2:30 PM Session (90 Mins): Statistical basics
    • Check sheets/tally sheets
    • Descriptive statistics
    • Binomial Distribution (discrete data)
    • Hypergeometric Distribution (sampling without replacement)
    • Normal Distribution (continuous data)
  • 2:30 PM-4:00 PM Session (90 Mins): Statistical Basics (cont’d)
    • Graphical Techniques:
      • Histograms
      • Scatterplots
      • Pareto charts
      • Cause and effect (fishbone) diagrams
      • Defect concentration diagrams
  • 4:00 PM-4:30 PM Wrap-up for Day 1, Q & A
DAY 02(9:00 AM - 4:30 PM)
  • 9:00 AM-10:30 AM(90 Mins): Statistical Process Control: The ABC’s of Control Charts
    • Elements of a control chart
    • Control Charts for Discrete Data
      • c chart
      • u chart
      • p chart
      • np chart
    • Control Charts for Continuous Data
      • X-bar chart
      • R chart
      • I chart
      • MR chart
      • Combined charts (Xbar-R, I-MR)
  • 10:30 AM-12:00 Noon Session 2(90 Mins): Statistical Process Control (cont’d)
    • More Control Charts
      • Classical Shewhart control charts
      • Cumulative Sum (CUSUM) charts
      • Exponentially Weighted Moving Average (EWMA) charts
      • Hotelling (multivariate) control charts
  • 12:00 Noon-1:00 PM Lunch
  • 1:00 PM-2:30 PM Session 3(90 Mins): Statistical Quality Control – Attribute Sampling Plans
    • C= 0 /Zero Acceptance
    • Single sample plan
    • Double-sampling plan
    • Multiple sampling plan
    • Sequential sampling plan
    • Skip-lot sampling plan
    • Using ANSI Z1.4
  • 2:30 PM-4:00 PM Session 4(90 Mins): Statistical Quality Control – Variables Sampling Plans
    • Sampling size and critical distance
    • Known vs. unknown standard deviation
    • One or two specification limits
    • Using ANSI Z1.9
  • 4:00 PM - 4:30 PM: Final Thoughts, Wrap-up for Day 2, Q & A
Seminar (Price/Register)
Seminar Fee Includes:
Lunch
AM-PM Tea/Coffee
Seminar Material
Attendance Certificate
$100 Gift Cert for next seminar

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Speaker: Elaine Eisenbeisz, Owner, Omega Statistics

Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine has over 30 years of experience in creating data and information solutions for industries ranging from governmental agencies and corporations, to start-up companies and individual researchers.

Elaine’s love of numbers began in elementary school where she placed in regional and statewide mathematics competitions. She attended University of California, Riverside, as a National Science Foundation scholar, where she earned a B.S. in Statistics with a minor in Quantitative Management, Accounting. Elaine received her Master’s Certification in Applied Statistcs from Texas A&M, and is currently finishing her graduate studies at Rochester Institute of Technology. Elaine is a member in good standing with the American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.

Elaine has designed the methodology for numerous studies in the clinical, biotech, and health care fields. She currently is an investigator on approximately 10 proton therapy clinical trials for Proton Collaborative Group, based in Illinois. She also designs and analyzes studies as a contract statistician for nutriceutical and fitness studies with QPS, a CRO based in Delaware. Elaine has also worked as a contract statistician with numerous private researchers and biotech start-ups as well as with larger companies such as Allergan and Rio Tinto Minerals. Not only is Elaine well versed in statistical methodology and analysis, she works well with project teams. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals. Please visit the Omega Statistics website at www.OmegaStatistics.com to learn more about Elaine and Omega Statistics.


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