Mid-Atlantic JMP® Users Group (MAJUG) Fall Meeting
Location: SAS Office, 1530 Wilson Blvd. Suite 800, Arlington-Rosslyn, VA
Thursday, September 12, 2019 from 8:45 a.m. - 12:30 p.m.
Abstracts and biographies for Rob Lievense and Ronald Snee are listed below.
AGENDA*:
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8:45 a.m.
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Registration/Sign-in
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9:00-9:10 a.m.
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Welcome and Introduction (Mel Alexander/Josh Klick)
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9:10-10:00 a.m.
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Applying Statistical Engineering to Big Data Problems (Ronald D Snee, Snee Associates LLC)
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10:00-10:15 a.m.
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Break
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10:15-10:50 a.m.
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Extracting Valuable Practical Information From Experimental Models Created for Quality by Design (Rob Lievense, Senior Systems Engineer, JMP)
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10:50-11:00 a.m.
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Break
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11:00 a.m. -12:00 p.m.
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JMP® Presentation (To be announced)
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12:00-12:15 p.m.
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Review SESUG, Discovery Summit, Upcoming events, Q&A, Discussion, Feedback, and Prizes
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12:30 p.m.
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Lunch, Networking, and Adjourn
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* This schedule is subject to change without notice
MAJUG meeting will be located at the SAS Office, 1530 Wilson Blvd. (Suite 800), Arlington-Rosslyn, VA
We encourage you to attend in person to get full advantage of the meeting and learning from other JMP users. If you are unable to attend in person, you can Register to attend using WebEx.
Please reply to Joshua Klick at klick.joshua@bls.gov by 12:00 p.m. Thursday September 5th indicating whether you plan to attend in person or using WebEx. Instructions how to access the WebEx session will be sent to WebEx registrants.
Bios
Ronald D. Snee, PhD, is founder and president of Snee Associates, LLC, an authority on designing and implementing improvement and cost-reduction solutions for a variety of organizational environments. He has a proven track record in process and organizational improvement in a variety of industries, including pharmaceutical, biotech, clinical diagnostics, and telecommunications. He is credited with developing the formulation development system strategy and leading the design of the first company-wide continuous improvement curriculum for DuPont. He has coauthored four books, published more than 300 articles on product and process improvement, quality, management, and statistics, and received numerous honors and awards for his work.
He received a bachelor’s degree in mathematics from Washington and Jefferson College and a master’s degree and PhD from Rutgers University in applied and mathematical statistics. He is a Fellow of the American Statistical Association, American Society of Quality (ASQ) and American Association for the Advancement of Science. His work has been recognized with 20 major awards and honors, including ASA’s Deming Lecture Award, ASQ’s Shewhart and Grant Medals, an Academician in the International Academy for Quality, and Honorary Member of ASQ.
Rob Lievense is a Senior Systems Engineer at JMP. He works with federal government customers showing them how to utilize JMP to extract useful information from data. He is also an active professor of statistics at Grand Valley State University (GVSU), located in Allendale, Michigan. Prior to joining JMP, Lievense served as Research Fellow of Global Statistics at Perrigo where he led a group that supported the consumer health care research and development department with statistical analysis, data visualization, advanced modeling, date-driven Quality by Design for product development and structured experimental design planning. Lievense has more than 20 years of experience in the applied statistics industry and 10 years of experience using JMP. He has presented at major conferences including JMP Discovery Summit, where he served on the Steering Committee, and the annual conference of the American Association of Pharmaceutical Scientists. Lievense holds a BS in Applied Statistics and an MS in Biostatistics from GVSU. He currently serves as Member of the Biostatistics Curriculum Development Committee for GVSU and has his Six Sigma Black Belt Certification.
Abstracts
Applying Statistical Engineering to Big Data Problems
Several trends are underway that have the promise of taking process improvement and problem solving to the next level; namely, Big Data, Statistical Engineering and improved statistical software. When these trends are combined with Lean Six Sigma the impact of analytical studies can be significantly increased. Executives and leaders of organizations of all types are asking more of analytical studies and those engaged in the conduct of such work. Increasing organizational impact typically results in addressing large, complex unstructured problems. Indeed any Big Data problem is large, complex and unstructured. We increase the impact of Lean Six Sigma improvement initiatives when the problems addressed are mission critical issues; problems that are typically large, complex and unstructured. Statistical Engineering is uniquely suited to successfully solving major problems. This presentation focuses on methodologies including case studies that show how these approaches and opportunities are integrated to increase the impact and success of data-based problem solving and process improvement work.
Extracting Valuable Practical Information From Experimental Models Created for Quality by Design
JMP DOE models can be used dynamically to provide stakeholders with reliable estimates of the quality performance for new products. The design and execution of structured, multivariate experiments allow scientists and engineers to efficiently define a robust design space for pharmaceutical and medical device manufacturing. Models created through the DOE platform in JMP are used to determine settings of the critical process parameters (CPPs) that ensure a robust process to make products that meet the requirements for Critical Quality Attributes (CQAs). Regulatory submissions that include such QbD elements demonstrate that risks have been mitigated; however, a great deal of practical information can be extracted with simulations of the model. This presentation utilizes the experimental model with historical information and subject matter expertise to project the likely operational performance of a product. The DOE model prediction profiler is used with the simulator to dynamically predict a population of future results with patterns of real-world variation included in the inputs. This dynamic modeling is an excellent tool for setting manufacturing card limits determining a manufacturing control space defined with estimates of the percentage defects. The analyses allow for the inclusion of the measurement uncertainty as an added noise factor for the response.
