Annalise Suzuki, director of technology and engagement at software provider Elysium Inc., spoke to Quality Digest about the importance of model-based definitions (MBD) for data quality, validation, and engineering
Everybody wants to design and conduct a great experiment! To find enlightenment by the discovery of the big red X and perhaps a few smaller pink x’s along the way.
Walter Shewhart, father of statistical process control and creator of the control chart, put a premium on the time order sequence of data.
Machine learning as a tool in your analytical toolkit can help accelerate the discovery of insights in data that can create a more efficient manufacturing process and drive innovation.
Process validation is vital to the success of companies that manufacture pharmaceutical drugs, vaccines, test kits, and a variety of other biological products for people and animals.
This is the second article in a three-part series to help readers distinguish good metrics from bad. In part one we discussed good metrics.
Perhaps the reader recognizes d2 as slang for “designated driver,” but quality professionals will recognize it as a control chart constant used to estimate short-term variation of a process.
Choosing the correct linear regression model can be difficult. Trying to model it with only a sample doesn’t make it any easier.
In this episode we look at a history of quality, how you serve your customer in the housing industry, and what makes a good review.
Metrics are an important part of an effective quality management system (QMS). They are necessary to understand, validate, and course-correct the QMS. They should be used to verify that it is achieving the goals and objectives defined by management.
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