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 design 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. The use of accelerating variables to shorten the study time and the models required are presented. Finally approaches for including the affect of other variables on shelf life (such as packaging type, storage conditions) are presented. The webinar includes several examples and case studies to illustrate the methods discussed. Datasets will be provided to the class participants so they can follow along with the instructor, as desired. The datasets will be provided in Minitab and Excel formats (The analyses will be performed in this webinar using Minitab).
Agenda
Speakers
Steven Wachs,
Principal Statistician ,
Integral Concepts, IncSteven 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 estimate and reduce warranty.
Who Should Attend
The target audience includes personnel involved in product/process development and manufacturing
- R&D Personnel
- Product Development Personnel
- Quality Personnel
- Lab Testing Personnel
- Operations / Production Managers
- Quality Assurance Managers, Engineers
- Process or Manufacturing Engineers or Managers
- Program or Product Managers
