What I’ve seen in the wild is that this is entirely a veneer. It’s important to have numbers. It doesn’t matter if they mean anything. In fact, management is full of numbers that a slightly-clever high school sophomore who’d paid attention in science classes could tell you are totally useless, because they were gathered all wrong. They mean nothing whatsoever. They’re just noise.

But nobody wants to hear stuff like “well first we’re going to need a baseline, and if you want it to be any good we’ll probably need two years or so before we can start trying to measure the effects of changes”. They just want something convincing enough that everyone can nod along to a story in a PowerPoint in four months. Two years out? Lol you’ll be measuring something totally different by then anyway. Your boss may be in a different role. You’ve asked something the company is literally incapable of.

Meanwhile, last I checked, measuring management effectiveness isn’t something we can do in practice for most roles, except bad ways that only pretend to tell us something useful (see above). Good scientists, excellent and large dataset, just the right sector, just one layer of management under scrutiny, maybe you get lucky and can draw some conclusions, but that’s about it, and it’s rare to see it happen in an actual company. Any companies that do achieve it aren’t sharing their datasets.

This kind of thing has been consistent everywhere my wife or I have worked. Similar things reported by many friends. Companies want to pretend to be “scientific” and “data-driven” but instead of applying it to only a couple things where they might do it well (enough data, cheap to gather metrics, clear relevant business outcome) they try it everywhere, but don’t want to spend what it would take to be serious about it, with the result that most of their figures are garbage.

This trend has become just another “soft”, as you put it, tool.

> In fact, management is full of numbers that a slightly-clever high school sophomore who’d paid attention in science classes could tell you are totally useless, because they were gathered all wrong. They mean nothing whatsoever. They’re just noise.

The whole point of SPC is to separate signal from noise. Pointing out that some change that everyone is obsessing over is well within the expected range is useful, it can head-off knee jerk reactions to phantom issues.

...assuming people want to know that the change is in the expected range. That's often not the case. People's careers are built on phantom improvements and being able to say that regular process issues were one-time occurrences.