In this article post, we discuss the suggested steps for planning a Designed Experiment.

Step #1 – Clearly Define the Problem and Objectives

It is critical to clearly define the problem before beginning experimentation.  When the problem is not clearly defined and described, there will be confusion in designing and executing effective studies.  To define appropriate responses to measure requires that the problem be understood and agreed on.  Also, it is key to define the objectives of the actual experiment.  If the problem is to reduce scrap rate, how much of a reduction is targeted? [Read more…]

The purpose of control charts is to detect significant process changes when they occur.  In general, charts that display averages of data/measurements (X-bar charts) are more useful than charts of individual data points or measurements.  Charts of individuals are not nearly as sensitive as charts of averages at detecting process changes quickly.  X-bar charts are far superior at detecting process shifts in a timely manner, and the sample size is a crucial element in ensuring that appropriate chart signals are produced. [Read more…]

While the construction of control charts is relatively straight-forward, often a more difficult question is “how do I know what process characteristic to control in the first place?”  Clearly, controlling “everything” is not feasible or a smart use of limited resources.   [Read more…]

In this article post, we formally define or describe the basic terminology that is commonly used in Design of Experiments.  Some of the terms we have already been using in prior posts, but they will also be presented here for completeness.  This is Part I of a two part article covering DOE Terminology.  [Read more…]

Statistically based DOE provides several advantages over more simplistic approaches such “one-factor-at-a-time” experimentation.  These advantages include:

• The use of statistical methodology to determine which factors are actually (statistically) significant
• Balanced experimental designs to allow stronger conclusions with respect to cause and effect relationships (as opposed to just finding correlations)
• The ability to understand and estimate interactions between factors
• The development of predictive models that are used to find optimal solutions for one or more responses

[Read more…]

Proper control chart selection is critical to realizing the benefits of Statistical Process Control.  Many factors should be considered when choosing a control chart for a given application.  These include:

• The type of data being charted (continuous or attribute)
• The required sensitivity (size of the change to be detected) of the chart
• Whether the chart includes data from multiple locations or not
• The ease and cost of sampling
• Production volumes

[Read more…]

Statistically based DOE provides several advantages over more simplistic approaches such “one-factor-at-a-time” experimentation.  These advantages include:

• The use of statistical methodology to determine which factors are actually (statistically) significant
• Balanced experimental designs to allow stronger conclusions with respect to cause and effect relationships (as opposed to just finding correlations)
• The ability to understand and estimate interactions between factors
• The development of predictive models that are used to find optimal solutions for one or more responses

This article will explore the first two advantages in a bit more detail.  The second two advantages will be discussed in the next article post.  [Read more…]