Speaker Profile
STEVEN WACHS
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. Education 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
Steven Wachs
Recorded Webinar
90 Minutes
Stability Studies And Estimating Shelf Life With Regression Models
Participants will be able to plan/conduct a stability study and then analyze the results to predict shelf life. They will also be able to explain the results from a statistical perspective. They will learn different approaches for estimating shelf life and will be in a position to select an appropriate method given the situation and constraints. Participants will learn how to appropriately model the data en..
Steven Wachs
Recorded Webinar
90 Minutes
Ten Keys for Maximizing the Benefits of your SPC Program
Statistical Process Control charts have been called the Voice of the Process. Progressive manufacturers utilize control charts to “listen” to their processes so that potentially harmful changes will be quickly detected and rectified. However, not all SPC programs deliver to their highest capability as there are many elements to get right to achieve maximum utility. Highly effective SPC programs combin..
Modeling and Optimizing Process/Product Behavior using Design of Experiments
Experimentation is frequently performed using trial and error approaches which are extremely inefficient and rarely lead to optimal solutions. Furthermore, when it’s desired to understand the effect of multiple variables on an outcome (response), “one-factor-at-a-time” trials are often performed. Not only is this approach inefficient, but it also inhibits the ability to understand and model how multip..