Dealing With Count Data and Variation

A non-statistical common cause strategy from Joseph Juran

In my last column, I showed the power of process-oriented thinking with a safety scenario. A simple run chart demonstrated that, despite meeting an aggressive 25-percent reduction goal (i.e., 45 accidents during the first year, and 32 the following year), the process that produced the 32 was no different from the process that produced the 45. It was common cause. Now what?

One advantage to the common-cause nature of the problem is that all 77 incidents were produced by the same process. Therefore, they can be aggregated, then stratified by process inputs to reveal hidden special causes.

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Comments

Great Idea

A simple, yet effective way to look at the data. Well worth sharing with others.

What if we compare different sized units: normalize?

Great article!

While looking at how I could use this I came accross a question: what if the work activity in the different departments is very different?

For instance if one of the units has twice the number of employees and/or produces twice the output of any other unit, we would certainly expect a higher number of incidents.

This basically means that the areas of opportunity are different in the different units. Should we in this case use the two entry table with "normalized" data to make it relevant (i.e., nbr of accidents/nbr of employees or hrs worked...)?

Thank you for your insight.

Best regards, Pierre

Count data analysis needs a lot of help

Those of us lucky enough to be "data rich" with continuous data are spoiled.

Count data is always messy in some way, it seems to me.

And yet reliability and safety people have to work with count data in many high risk scenarios.

Thanks for your focus on "small data" when "big data" is so hyped now days.