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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Monitoring & Improving Quality via Consumer Complaints - A Statistical Engineering Case Study
Procter & Gamble (P&G) receives millions of consumer comments each year and requires an efficient data-mining algorithm to identify products with unexpectedly high complaint counts, as these suggest potential quality or safety issues (aka “signals”). This talk will present an overview of P&G’s adopted signal detection method, the Multi-Item Gamma Poisson Shrinker (MGPS), an empirical Bayesian method for disproportionality analysis. It will also discuss the application of text mining on consumer comments to find misclassified adverse events and identify fraudulent complaints. I will finish the talk by relating certain aspects of this case study to the statistical engineering framework proposed by Roger Hoerl and Ron Snee in their 2017 American Statistician paper, “Statistical Engineering: An Idea Whose Time Has Come?”
William Brenneman is a Research Fellow and Global Statistics Discipline Leader at Procter & Gamble in the Data and Modeling Sciences Department and an Adjunct Professor of Practice at Georgia Tech in the Stewart School of Industrial and Systems Engineering. Since joining P&G, he has worked on a wide range of projects that deal with statistics applications in his areas of expertise: design and analysis of experiments, robust parameter design, reliability engineering, statistical process control, computer experiments, data science, machine learning and general statistical thinking. He was also instrumental in the development of an in-house statistics curriculum.
He received a Ph.D. degree in Statistics from the University of Michigan, an MS in Mathematics from the University of Iowa and a BA in Mathematics and Secondary Education from Tabor College. William is a Fellow of the American Statistical Association (ASA), a Fellow of the American Society for Quality (ASQ), and a member of the Institute of Mathematical Statistics and the Institute for Operations Research and Management Sciences. He has served as ASQ Statistics Division Chair, ASA Quality and Productivity Section Chair and is currently serving as an Associate Editor for Technometrics. William also has seven years of experience as an educator at the high school and college level.
