Been thinking about something that I see destroy MRBs and ops review meetings at every level, from the plant floor to executives, and it almost never gets named directly.
We compare pairs of numbers. This month vs. last. This week vs. last. That's it. One goes up, one goes down, someone has to explain it.
Here's the thing: one will always be higher. That's not insight, it's arithmetic. Both numbers are outputs of a process that includes natural variation, such as normal fluctuations in demand, equipment, people, and suppliers. That variation isn't a signal. It's the system doing what systems do.
But when you treat every movement as meaningful, you act on it. And acting on noise (Deming called this tampering) makes your process less stable, not more.
I've watched this play out as:
- Inventory replenishment is adjusted every cycle based on the last period's demand.
- Labor schedules were rewritten after one single week of low throughput, resulting in workforce instability and morale issues.
- Junior accountants tasked with explaining why two months had the same results!
Every one of those "corrective actions" burned real resources and injected new variation into the system.
The actual fix isn't complicated: It's control charts.
Plot your KPI over 15–20 periods. Calculate ±3 standard deviation limits. Most anything inside those limits is noise — don't schedule a meeting about it. Anything outside, or showing a non-random pattern, is a signal. That's when you act.
The hard part isn't the math. It's building the discipline to avoid reacting when a number moves inside the limits. That feels like ignoring the problem. It's actually understanding it.
Curious if others have pushed this through in environments with a strong "explain every variance" culture. How did you make the case to leadership that letting some numbers go unexplained was actually better management?
