The importance of Design of Experiments (DoE)

[Adam Fleming]

Design of Experiments (DoE) is an important component in many industries. It is a series of tests or runs that is carried out repeatedly and consistently over a period of time, and its outputs or responses, observed. Design of Experiments is very important in industry to help arrive at an understanding of the predictability and reproducibility of an experiment.

Design of Experiments is a very important aspect of the important elements of a product, such as quality, reliability and performance. What Design of Experiments does is that it helps to examine and investigate the inputs that lead to poor quality. This insight leads the entity carrying out the Design of Experiments to use these to improve their quality standards.

 

Ruling out chance

Design of Experiments does not rely on chance or providence to bring about the quality that is required of an experiment. It arrives at the optimal set of procedures that are needed to get the required quality standards after a series of tests and experiments, so that the final result shows in the process that goes into the product.

Fundamentally, Design of Experiments helps to put in place a system of control for a product. All the ingredients that go into the inputs needed for obtaining a product of a defined standard or quality are scientific and precise. This precision and accuracy is arrived at after carrying out as many runs or series of Design of Experiments as needed to finally arrive at it.

An introduction to Design of Experiments

The ways of understanding Design of Experiments and applying their standards into production will be the topic of a webinar that is being organized by Compliance4All, a leading provider of professional trainings for all areas of regulatory compliance. At this webinar, the speaker, William Levinson, an ASQ Fellow, Certified Quality Engineer, Quality Auditor, Quality Manager, Reliability Engineer, and Six Sigma Black Belt, who is the principal of Levinson Productivity Systems, P.C., will explain the fundamentals of Design of Experiments.

To gain a proper understanding of the principles of Design of Experiments and to get a grasp of how to implement this concept into your systems, please register for this webinar by logging on to http://www.compliance4all.com/control/w_product/~product_id=501202?...

An understanding of the significance level in hypothesis testing

William will make participants understand how to use Design of Experiments to identify and rule out the particular item or input that affects quality. The concept of significance level in hypothesis testing, which will serve as a basis for not only DoE, but also Statistical Process Control and acceptance sampling, will be explained.

A description of the other uses of DoE, such as supporting Corrective and Preventive Action (CAPA) and in process improvement, where it helps to identify and optimize the factors influenced by Critical to Quality (CTQ) characteristic, will be part of the learning that is on offer at this webinar.

Levinson will cover the following areas at this webinar:

·        Economic benefits of DOE

·        Hypothesis testing: the foundation of DOE, SPC, and acceptance sampling

o  Null and alternate hypothesis

o  Type I or alpha risk of concluding wrongly that the experiment differs from the control (or that a process is out of control, or that an acceptable production lot should be rejected)

o  Type II or beta risk of not detecting a difference between the control and the experiment, not detecting an out of control condition, and accepting a production lot that should be rejected

·        Factors, levels, and interactions

o  Interaction = “the whole is greater or less than the sum of its parts”. One variable at a time experiments cannot detect interactions.

·        Randomization and blocking exclude extraneous variation sources from the experiment.

·        Replication means taking multiple measurements to increase the experiment’s power.

·        Interpret the experiment’s results in terms of the significance level, or quantifiable “reasonable doubt” that the experiment differs from the control.

http://support.minitab.com/en-us/minitab/17/topic-library/modeling-...