If Fyodor Dostoyevsky had been a mathematician, he might have written a book called Crime and Statistics. However, since “statistics” doesn’t have quite the same ring as “punishment,” it wouldn’t have sold as well.

This is part three of a three-part series. Read about good metrics in part one and bad metrics in part two.

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 change management. With the increase of digital 3D models in the manufacturing workflow, companies are appreciating their value for speeding product development, improving quality and performance, and allowing for greater automation.

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. Thoughtful selection of the best experiment factors, the right levels, the most efficient design, the best plan for randomization, and creative ways to quantify the response variable consume our thoughts and imagination. The list of considerations and trade-offs is quite impressive.

Walter Shewhart, father of statistical process control and creator of the control chart, put a premium on the time order sequence of data. Since many statistics and graphs are unaffected by this, you might wonder what the fuss is about. Read on to see why.

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.