on Tools & Techniques

Statistical Process Control (SPC) charts allow timely detection of assignable causes of process changes (e.g. shifts, trends, variation) so that root causes may be determined and corrective actions taken before product performance is adversely impacted.  Proper use of SPC identifies and eliminates “special cause” sources of variation.  To achieve desired process capability, sources of “common cause” variation may need to be identified as well, using tools such as Design of Experiments to develop process understanding and predictive models that explain the source of the unwanted variability.   [Read more…]

We are ready to learn how to analyze the data collected during the experiment.  This is the most exciting part of DOE!  We will cover the analysis in this article as well as the next several articles.

The following are the main steps to perform during the analysis:

• Calculate the Main Effects and Interaction Effects
• Test the Effects for Statistical Significance
• Interpret the Significant Effects (Often with Plots)
• Develop Predictive Model(s)
• Perform Model Validation (Residual Analysis)
• Find Solutions and Perform Optimization to find Best Solution(s)
• Validate the Solutions
• Determine if Follow-up Experimentation is Necessary

[Read more…]

When applied properly, SPC provides manufacturers a proven method to increase profitability and achieve a deeper understanding of their processes..  Additionally, SPC can prevent problems—saving companies money that would have been lost in scrap, rework, warranty, litigation, and market share decline.  A key factor in obtaining these SPC benefits is the proper deployment of control charts.  Correctly designed control charts identify significant changes to a process.  These can be changes that are still within specification, but are statistically different than where the process was previously running.  By identifying the changes, personnel can determine what caused the change and potentially improve the process or prevent the production of inferior products.   [Read more…]

In this week’s article entry, we discuss some guidelines for conducting an experiment.  As we discussed in an earlier post, planning the study is critical for a successful outcome.  A good plan makes the conduct of the study straightforward.

Below are some key aspects of conducting the study: [Read more…]

In this article post, we discuss several important techniques to consider when conducting and analyzing an experiment.  They are summarized in the table below and next we discuss each one in a bit more detail.  Some of these techniques are bit more advanced (e.g. Blocking, Covariates), but they are introduced here.    [Read more…]

An earlier article focused on the conceptual application of appropriate sample sizes for X-bar charts.  As we discussed, the purpose of control charts is to detect significant process changes when they occur.  When the proper sample size is selected, X-bar charts will detect process shifts (that have practical significance) in a timely manner. [Read more…]

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…]