How Local Optimization May Result in Failure
Sometimes when you remove a rusty bolt from a system of nuts and bolts and clean it up, it may not fit the original context anymore. In the course of time the rust has become part of the system and should be also taken into account.
In a complex software legacy system this phenomenon may manifest when a certain subsystem is refactored or rewritten. Technically the new part may be superior and fulfill the original specifications, but the changes may still result in nasty regression issues. During the past years the layer of application logic has been built on top of the faulty behavior (the rust).
Same applies in organizational context. High-performance team may outrun the system around it. It may produce results faster than the other parts can consume. This can create queues and waste. Results can be even worse when the sub optimization happens on the unit level. For example when R&D is able to produce results faster than can be delivered or specified.
Usually increasing performance is desirable, but some gotchas should be avoided. If you want to remedy the possible problems, I’d suggest a system view. Try to improve the overall flow of value through the system. Tweak the parts, but don’t fall for the local optimization. Aim to decrease the lead time and measure things from the customers’ perspective.
