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Until recently, there hasn’t been enough data to apply statistical process control in a lot of fields. Chemical process companies (like Kodak used to be) have u
by mch82 5y ago
Until recently, there hasn’t been enough data to apply statistical process control in a lot of fields. Chemical process companies (like Kodak used to be) have used SPC. “Web scale” companies commonly talk about running experiments now. Lots of people talk about A/B testing, which is an extremely simplistic example of SPC. I’ve been anticipating more advanced SPC tools to replace A/B testing for years, but am not aware of any. Maybe I should have created one.
Now I wonder if Machine Learning might pass up SPC as the primary method by which people identify and control key process variables.
- neverartful 5y agoActually, it does not take a huge amount of data to make use of statistical process control. In fact, it's so little that someone should be able to do it with paper, pencil, and calculator. The statistical technique I think that's better able to displace A/B testing is design of experiments (DOE). This approach also does not require that much data to implement. There was an excellent youtube series on it where the presenter used popping popcorn for his experiments. I can't seem to find it now.
- analog31 5y agoI've observed DOE in use. It's a statistical method at its core, and as such potentially suffers from the replication crisis.