A primer for quality professionals
Identifying common-cause and special-cause variation in processes is key to process improvement
Two engineers use fluid dynamics to study stock ‘flows’
Can they help to do a better job?
Understanding the changes
Vilfredo Pareto, Joseph Juran, and Kaoru Ishikawa are all contenders
What you don’t know can confuse you
Reduce the amount of attribute inspection that must be done
Four points of consideration
Why is it important to keep the process stable?
Taking advantage of ‘natural experiments,’ researchers analyze data to look at what works
Part three: the ugly
Interoperability is key to avoiding the manual steps and hand-offs that Industry 4.0 hopes to eliminate
Be deliberate about the sample size you use
Avoid the unnecessary waste of being misled
As a tool, machine learning can accelerate insights in data for more efficient manufacturing and drive innovation
Passing the three FDA stage goals
Part two: the bad
We owe a debt of gratitude to Tippett and other pioneers who put ‘engineering’ into quality engineering.
There’s a Goldilocks balance with the number of predictors to include