Manufacturers of foods, drugs, consumer goods, and other products must determine the shelf life of their products so that customers know when the product can be expected to perform as intended. Many approaches are available to quantify the "shelf life" and the method(s) chosen often depend on the testing time available.
This webinar discusses the steps to set-up a stability study and analyze the results to estimate the product's shelf life. The use of regression models to model the relationship between the response variable(s) and time are presented. Models useful for describing non-linear degradation over time are also presented. Additionally, methods for handling non-normal response data are also discussed. Finally, the use of accelerating variables to shorten the study time and the models required are introduced. The webinar includes several examples to illustrate the methods discussed.
Companies must ensure that the advertised shelf life on their products is accurate and supported by data. Failure to do so may result in fraudulent claims, customer dissatisfaction, or even safety concerns. Many industries must comply with government or industry guidelines for determining shelf life.
This webinar provides an overview of statistical methods that are appropriate for shelf life determination. Both regression modeling of stability data and life data analysis are presented as valid methods for quantifying shelf life at a specified level of confidence.
Examples are included with illustrations to clarify what are typically confusing points. Also, the interpretation and communication of results will be stressed.
The target audience includes personnel involved in product/process development and manufacturing
Product Development Personnel
Lab Testing Personnel
Operations / Production Managers
Quality Assurance Managers, Engineers
Process or Manufacturing Engineers or Managers
Program or Product Managers
Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.
Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as to estimate and reduce warranty. In addition to providing consulting services, Steve regularly conducts workshops in industrial statistical methods for companies worldwide.
M.A., Applied Statistics, University of Michigan, 2002
M.B.A, Katz Graduate School of Business, University of Pittsburgh, 1992
B.S., Mechanical Engineering, University of Michigan, 1986