Walk into a modern manufacturing plant, and you’ll often find increasingly sophisticated dashboards. Charts, percentages, color-coded indicators, comparisons with previous shifts, daily targets, and real-time performance updates.

We have access to an amount of information that would have been difficult to imagine just a few years ago.

Yet having more data doesn’t necessarily mean having a better understanding of what’s happening on the shop floor.

Sometimes, the opposite is true.

When everything is measured and displayed with equal importance, it becomes difficult to distinguish what requires immediate attention from what simply supports a more detailed analysis.

A dashboard can be technically flawless and still provide little value when it comes to making decisions.

If I wanted to get a quick picture of how a production line was performing, I’d start with two simple questions.

How much product are we getting right the first time?

How much time are we losing while waiting for something that should already be available?

The first question brings us to First Pass Yield (FPY), a metric that measures the percentage of units that successfully pass through a process step on the first attempt, without requiring rework.

It’s important because producing a unit doesn’t necessarily mean producing a conforming product.

A plant may meet its daily production schedule while generating rework, additional inspections, and unnecessary resource consumption that aren’t immediately visible in the total number of units produced.

FPY helps reveal that difference.

The second question concerns Waiting Time.

Materials that don’t arrive, unavailable forklifts, delayed approvals, missing information, or a downstream process that isn’t ready to receive the product.

These may seem like ordinary situations, but they can cause interruptions, reduce effective capacity, and create inefficiencies throughout the production flow.

And there’s something we often overlook.

A stopped machine isn’t necessarily the problem. It may simply be where the problem becomes visible.

If a line stops because materials are missing, asking the operator to work faster won’t solve the issue. We need to understand why those materials weren’t available when they were needed.

Similarly, if a product repeatedly requires rework, increasing machine speed may simply mean producing the same defect faster.

The value of these two metrics goes beyond the numbers displayed on a screen. They help us ask better questions.

Of course, they don’t tell the whole story.

A plant could have high FPY and limited waiting time while still producing less than required because of insufficient capacity, a bottleneck, or inadequate demand.

It could also be operationally efficient while delivering disappointing margins due to costs, pricing, or product mix.

That’s why I wouldn’t eliminate other KPIs or suggest running a manufacturing plant using only two numbers.

The point is different.

Not every metric deserves the same priority at every moment.

A good management system should allow us to start with a few meaningful indicators and dig deeper only when something requires attention.

A dashboard should help us understand where to look, not force us to look at everything at once.

Above all, no metric should replace direct observation of the process and conversations with the people who know it.

Because we can have twenty perfectly updated charts and still fail to understand why production isn’t performing as expected.

Or we can start with two simple questions, walk onto the shop floor, and discover something those charts alone couldn’t tell us.

The value of a metric isn’t defined by how much it measures, but by how much it helps us understand and decide.