DoE helps in optimizing processes and output in a number of ways

Design of Experiments (DoE) is defined as a systematized means with which the factors that go into a process and its intended output can be determined. DoE makes extensive use of statistical and experimentation means. DoE helps to optimize the processes of production in a number of environments in a huge range of industries.

The varied uses of DoE

DoE is useful in a number of applications. For instance, it:

o  Helps determine root cause, leading to quick and efficient ways of solving problems

o  Reduces R&D work

o  Optimizes Product Designs

o  Augments Manufacturing Processes

o  Hastens the development of Product or Process Specifications

o  Improves Quality, as well as reliability, of products.

DoE is thus a specialized discipline that needs to be exercised with training. Professionals who work in a number of industries can use the DoE process to optimize a number of processes and results, and thus enhance their bottom line.

Learn the elements of DoE

In order to help professionals derive the best out of DoE; Compliance4All, a leading provider of professional trainings for all the areas of regulatory compliance, is organizing a webinar. This webinar, which will be about the ways by which process behavior can be built and optimized using DoE, will have the well-known and highly experienced expert on statistics, Steven Wachs, as speaker.

Steven, with over 25 years as a statistician, has had extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control. In his current position as a Principal Statistician at Integral Concepts, Inc., he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity.

To enroll for this webinar, just log on to

Get a clear understanding of key concepts behind DoE

At this webinar, Steven will review the key concepts behind Design of Experiments and present a strategy that participants can use to utilize sequential experiments that helps to understand and model a process efficiently and effectively. He will present the various kinds of experiments and their applications.

The appropriate means to screen and optimize experiments, as well as an understanding of mixtures/formulations will be covered at this session. Other important techniques in experimental design, such as replication, blocking, and randomization, will also be covered at this session. Stave will also present a case study that involves optimizing a manufacturing process using multiple responses.


By attending this webinar, participants will be able to achieve the following objectives concerning DoE:

o   Learn a methodology to perform experiments in an optimal fashion

o  Review the common types of experimental designs and important techniques

o  Develop predictive models to describe the effects that variables have on one or more responses

o  Utilize predictive models to develop optimal solutions


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