Eric Budd asked on The W. Edwards Deming Institute group on LinkedIn
If observed performance/behavior in a system is a result of the interactions between components–and variation exists in those components–the best root cause explanation we might hope for is a description of the interactions and variation at a moment in time. How can we make such an explanation useful?
A single root cause is rare. Normally you can look at the question a bit differently see the scope a bit differently and get a different “root cause.” In my opinion “root cause” is more a decision about what is an effective way to improve the system right now rather than finding a scientifically valid “root cause.”
Sometimes it might be obvious combination which is an issue so must be prevented. In such a case I don’t think interaction root cause is hard – just list out the conditions and then design something to prevent that in the future.
Often I think you may find that the results are not very robust and this time we caught the failure because of u = 11, x = 3, y = 4 and z =1. But those knowledge working on the process can tell the results are not reliable unless x = 5 or 6. And if z is under 3 things are likely to go wrong. and if u is above 8 and x is below 5 and y is below 5 things are in trouble…
To me this often amounts to designing systems to be robust and able to perform with the variation that is likely to happen. And for those areas where the system can’t be made robust for some variation then designing things so that variation doesn’t happen to the system (mistake proofing processes, for example).
In order to deal with interaction, learn about interaction and optimize results possible due to interactions I believe the best method is to use design of experiments (DoE) – factorial experiments.