I took a few business courses at Stanford. They were pretty entertaining and my favorite was a course on Supply-Chain Management. It was a series of case-studies that all started or ended with: “Rob looked out of his office window overlooking the hills in Palo Alto and wondered how he was going to …”
Invariably, the case-studies had consultants who would come in and save the day. More often than not they would either move the supply-chain from an existing centralized system to a more distributed process or vice versa. In both cases there would be very rational sounding reasons for doing so, and indeed the case study would cite much increased growth and productivity at the host company after the consultants has done their thing.
I never quite understood how one would make the decision before-hand on whether to go with a centralized or distributed process. In any case, it was always presented as a binary decision - it was never framed as a tradeoff.
Because that’s what it really is. Anytime somebody moves the slider on the efficiency scale, the system will become more or less robust. The higher the efficiency, the less robust the system. A highly efficient centralized system is much more vulnerable to single-point-of-failure situations. A more distributed approach, while being more resistant to single-point failures, is less efficient. An efficient system will also fail very efficiently.
Unfortunately, competition pushes systems to move towards higher and higher efficiency, all the while ignoring the massive drop in robustness for each player. Robustness is a difficult quality to measure compared to efficiency, and is thus rarely included in cost/benefit analyses. Efficiency gains can be quoted in percentage terms, but robustness measures only rely on “messy” scenario analysis that are difficult to enumerate and ask people to imagine the unknown.
While efficiency has been reified, robustness remains an elusive measure that few take into account until a failure actually occurs.
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