The HBR magazine April 2001 issue is about failure - understand, learn, recover. I agree that failure is a teacher, but is it the best teacher?
I also know that there are black swans and we cannot expect the unexpected.
And there are strategies to learn from failure and we should not be programmed thinking that failure is always bad.
But working at a,say, blast furnace, failures let you usually run.
As a coal-faced mathematician, I am socialized in industrial environments of flow processes and highly automated discrete manufacturing - hot and cool businesses, where failures might become disastrous.
Uncertain environments call for experimentation, but a stress test in a real plant is not so easy, you might need to learn from other secure processes in use and transform behavior into computational knowledge.
So, it is vital to understand the processes and its features to the very detail and especially what it means: running-outside-the-secure-properties. This requires modeling and inverse analysis, often in high-dimensional parameter spaces. We called this inline quality assurance and risk control.
Instead of measuring the product in production, control the process in relation to it. Unfortunately you do not always have a theory, so clever machine learning methods may help you to get insight and compute towards a high quality product with a secure process.
Mathematica 8, with its combination of modeling, statistical analysis and our machine learning extension will do a great job to avoid failure or detect them in its stage of coming into being. Our fields of experience are metallurgy, paper making, metal forming, assembling, ...
This might not be so easy in fields, where we have no theory (as in economy, IMO), but even there we shall strive for minimizing the hidden forces that shape our decisions?
