“We need to improve the line’s OEE by 3.5% this month.”

It’s the kind of statement we often hear during a production meeting. A precise, measurable, seemingly clear target.

But what does it mean to the operator who returns to the packaging line a few minutes later?

What exactly should they do differently from the previous shift?

Probably nothing, at least until someone translates that number into something concrete.

OEE (Overall Equipment Effectiveness) is an important performance indicator. It combines availability, performance, and quality to measure how effectively equipment operates compared to its productive potential.

It’s useful for analyzing losses, evaluating results, and identifying areas for improvement.

But a performance indicator is not an operating instruction.

Telling an operator to improve OEE often means communicating the result we want without explaining which actions could help achieve it.

Let’s look at it differently.

Imagine a packaging line where we’ve identified recurring losses during product changeovers. Packaging materials aren’t always ready, the outfeed conveyor needs to be cleared, and restarting takes longer than necessary.

Instead of simply asking for better OEE, we could say:

“Our target is to produce 100 additional finished cases per shift. To achieve that, we need to reduce the time lost during changeovers. Let’s start by having the packaging film ready at least five minutes before the batch ends and making sure the outfeed conveyor is clear before the changeover.”

Of course, those 100 additional cases must be based on a realistic calculation of production capacity and recoverable losses. Choosing an arbitrary number and calling it a target isn’t enough.

But the difference is significant.

In the first case, we’ve communicated a metric. In the second, we’ve defined an understandable result, identified specific actions, and made execution directly observable on the shop floor.

The operator knows what’s expected. The supervisor can verify whether the required conditions have been met. Most importantly, we can determine whether those actions actually reduced production losses.

If the expected improvement doesn’t happen, we have something concrete to investigate. Perhaps the main causes lie elsewhere. Perhaps the recovered time isn’t sufficient. Or maybe we’ve underestimated a constraint on the line.

And this brings us to another important aspect of operational management.

Not everything that affects a performance indicator is within the operator’s control.

Mechanical failures, defective materials, ineffective production scheduling, or supply problems can undermine performance regardless of how committed the machine operators are.

Demanding results without distinguishing what people can control from what depends on the organization risks turning a performance indicator into a source of pressure rather than a tool for improvement.

This doesn’t mean operators shouldn’t understand OEE. Quite the opposite. Understanding what it measures can help them recognize the value of their work and how individual activities contribute to overall performance.

But understanding a metric and knowing what to do to improve it are two different things.

Connecting them is the responsibility of operational leadership.

A good production leader must be able to interpret the numbers, identify losses, establish priorities, and translate business objectives into clear, achievable actions. They must also ensure that people have the conditions and resources needed to carry them out.

And when something doesn’t work, they must be willing to question their own assumptions, not just the operators’ performance.

Because results don’t improve simply by asking people to achieve a higher percentage.

They improve when we understand which losses can be eliminated, who can address them, and what conditions must be created to make improvement possible.

Numbers help us understand where to act. Operational leadership turns that understanding into concrete action.