Why Run Uptime May Matter More Than OEE

In manufacturing, few metrics are more widely discussed than OEE. And while OEE can be valuable, we’ve found that many plants struggle to turn it into daily action on the floor.

Why? Because OEE is often too complex, too debated, and too disconnected from the moment-to-moment reality operators and supervisors face during a shift. Plants end up arguing about calculation rules, ideal rates, planned downtime, and data accuracy instead of focusing on the most important operational question:

Is the line running and producing sellable product?

That’s why at Flex-Metrics, we put so much emphasis on Run Uptime.

After decades in manufacturing leadership roles, we’ve found that one of the most powerful metrics is also one of the simplest. Run Uptime cuts through the noise and gives everyone — from operators to executives — a clear picture of what’s actually happening on the floor.

And more importantly, it drives action.

The Beauty of Simplicity

Prior to joining Flex, I was the Senior Director of Manufacturing at Spectrum Brands.  We implemented Flex across their entire 5-site manufacturing platform. What made the difference wasn't complicated analytics – it was giving everyone clear visibility into a simple question: is the machine running or not?

Run Uptime: The percentage of available run time (i.e. excluding changeovers and planned downtime) during which the equipment is actually producing sellable product.

Visual explanation of run uptime equation

This simplicity makes it immediately clear to everyone – from operators to executives – what's happening on the floor. No PhD in data science required.

Finding Hidden Capacity

One of the most universal pieces of low-hanging fruit we find is shift ramp-up and ramp-down. When we show people their run time data, they quickly see that the first and last hour of every shift are consistently the lowest two hours. This is easy to fix – it's purely behavioral.

Another common discovery: how much time is lost simply waiting for something – materials, quality approvals, or other departments. It's not a mechanical problem or a process problem. They're just waiting. Again, low-hanging fruit.

For companies early in their Flex journey, we start with these obvious, easy wins. The low-hanging fruit is, by definition, high impact and low effort – it doesn't cost anything to fix.

Run Uptime + Leadership = Boosted Performance

The data itself is only part of the equation. Through years of implementation, we've discovered that the "wild card" in Flex's success is leadership engagement. The customers that don't maximize the value of Flex almost always lack this component.

When you plug Flex into an organization that is well-led, the results are transformative. The leadership component – earning trust, setting expectations, celebrating wins, giving people visibility into how their work affects metrics, showing them they're part of something bigger – is what turns data into action.

We recently visited a plant running at just 20% run uptime when similar operations typically achieve 70-75%. The difference wasn't technology or equipment – it was leadership's willingness to engage with the data to drive improvement.

The Bottom Line on Run Uptime vs OEE

Run Uptime isn’t meant to replace every manufacturing KPI or eliminate the value of OEE. In mature operations, OEE can be a powerful tool. But we’ve found that many plants jump straight to complex composite metrics before they’ve built visibility and discipline around the fundamentals.

Run Uptime brings the focus back to what matters most: are we maximizing the time our equipment is actually producing sellable product?

That simplicity is what makes it effective. Everyone understands it. Operators can influence it. Supervisors can coach around it. Leaders can align teams around it. And when organizations consistently focus on improving Run Uptime, many of the components that drive stronger OEE performance improve naturally along the way.

The beauty is in the balance: simple enough to drive action, powerful enough to expose hidden capacity, and flexible enough to grow with an organization’s operational maturity.

At Flex-Metrics, we believe manufacturing improvement starts by cutting through the noise. Because in most plants, the challenge isn’t a lack of data — it’s a lack of focus. Our job is to help teams focus on what matters most: getting equipment in run, keeping it in run, and running at target speed.

Downtime Tracking That Drives Real Operational Improvement

Want to drive improvements to virtually every operational KPI? Start by focusing on our simple maxim: “Get it in run, keep it in run, at target speed.” Effective use of downtime reason codes can help you achieve this goal. Let's dive into some key concepts about your reasons for downtime that you might not have considered.

Understanding D1 vs. D2 Downtime Tracking

D1 (Unplanned Downtime): This state occurs when the crew is on the line, but the line isn't running. This is where most of your headaches will manifest.

D2 (Planned Downtime): This state is for downtimes when the line is not crewed, or the crew is in an indirect labor state (e.g., lunch break, clean-up, maintenance). Although this article focuses on D1, tracking D2 is also important to ensure scheduled downtime events are properly managed. We’ll cover this in another post.

The Three Types of D1 Downtimes

We believe there are 3 distinct types of D1s, each needing different analysis and corrective action:

1. Internal D1s

These are downtime events that are inherent in the process and cannot be avoided.  Roll changes are a classic example of an internal D1. 

 D1s require well-defined standard work and training. The goal is to measure and improve the process capability, i.e., everyone does it the same way and in, roughly, the same amount of time. High variability in the downtime durations signals an “out of control” process.

 The corrective action: reduce the variability by assessing your standard work, evaluating your training, and working directly with struggling employees.

MTBF [Mean Time Between Failures] (or average Run duration) is an excellent metric. It will never be longer than the intervals between internal D1 events. You know that going in so set your target accordingly.

2. External D1s

These are downtime events that are unrelated to the equipment, job, or crew.  The classic external D1 is the machine is down waiting for something, materials being the most common culprit. 

External D1s are typically avoidable and tend to be ‘low hanging fruit’.  They need a deep dive into the conditions that cause them. 

3. Break-fix D1s

As the name suggests, these are equipment breakdown events that often result in the need for Maintenance support. 

There are two critical metrics to consider here:

• How long: When they happen, how long does it take for the required resources to respond and fix the issue?  If you don’t measure it, you can’t manage it.

• How often: What frequency are you experiencing the same breakdown for any given piece of equipment? 

If you repeatedly experience the same break-fix D1 on a given piece of equipment, FIX IT!  These reason codes are an excellent source of ROI justification for capital investment. And here’s a hard saying that is worth emphasizing: if you are not using Flex data to find and fix your problems, what’s the point?

Effectively managing downtime in manufacturing is essential for maximizing profitability and operational excellence. Thinking about your downtime using this framework and selecting the reason codes that make sense for your operation will help you get the most out of your data.

Manufacturing Data Analytics from the Plant Floor

Below, watch the dynamic duo of manufacturing data analytics, Don and our CTO/Owner Jay Foster, as they playfully riff on each other while delving deep into their shared passion for data analytics in manufacturing. Listen to them share insights, anecdotes, and a dash of humor in this entertaining and enlightening discussion.

https://www.youtube.com/watch?v=7t4RAff5-CU