The most dangerous KPI isn't the one that's wrong. It's the one that's technically correct but tells you the wrong story.
We all love KPIs.
On-time delivery: 96%.
Forecast accuracy: 91%.
Inventory: down 12%.
Manufacturing efficiency: 94%.
Sounds like we're doing a pretty good job, right?
Well… maybe.
One of the things I've learned working with supply chains is that a KPI can be perfectly correct and still give you the wrong answer.
Imagine a company with a 95% On-Time Delivery rate.
At first glance, that's great.
But then you ask a few more questions:
- On time according to the original customer request date, or the date we changed it to?
- How many orders were delivered on time because the customer accepted a later date?
- How many required an expensive expedited shipment to make the deadline?
- And how many orders were already late before someone realized there was a problem?
Suddenly, that 95% doesn't look quite as impressive.
The same happens with lead time.
Suppose we reduce our average end-to-end lead time from 30 days to 25.
Success!
But what if the average improved because a few large orders moved much faster, while smaller orders became significantly more unpredictable?
The average tells us we improved.
The customer experience might tell us something completely different.
And that's the problem with averages and isolated KPIs.
They measure what happened. They don't necessarily explain why it happened.
This is why I think the next step in supply chain performance management isn't simply adding more KPIs.
It's connecting the KPIs and understanding the story behind them.
Instead of asking “What's our lead time?” we should ask “Where is the time actually being spent?”, is it manufacturing? Transportation? Waiting for an approval? Quality release? Planning? Customs? Or simply waiting for someone to make a decision?
And instead of asking “Are we on time?” maybe we should ask “What did it take to be on time?”
Because there's a huge difference between delivering on time through a stable, predictable process and delivering on time after three escalations, an emergency production slot and an expensive air shipment.
Same KPI. Completely different supply chain.
That's why I believe good supply chain analytics should work a bit like detective work.
The KPI tells you where to look, the process tells you what happened and root-cause analysis tells you what needs to change.
So the next time your dashboard is full of green numbers, don't just celebrate.
Ask a few uncomfortable questions, what is this KPI really telling us? What is it hiding? and perhaps most importantly: Are we measuring performance… or just measuring the symptoms?
Because sometimes the biggest supply chain problem isn't a bad KPI, it's a KPI that makes us believe everything is fine.
