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.

Why Manufacturing Data Analytics Initiatives Fail

My professional career has exposed me to a wide range of data-driven initiatives.

Prior to graduating from college, I had the pleasure of working as an engineering co-op student. I spent a summer working at the Naval Research Center in Bethesda, Maryland collecting sonar data on nuclear submarine runs.

This was the summer that Tom Clancy’s “Hunt for Red October” mega-best seller came out. I was thrilled to spend time on the USS Dallas, the submarine in the story that was showcased in the movie with Sean Connery as her captain.

My co-op job was working with a team of engineers to collect sonar data that allowed the Navy to ping the ocean and utilize advanced algorithms to identify sea vessels in the surrounding water based on their unique data “signature.”

The US Department of Defense paid significant amounts of money for teams of engineers and technicians to collect reams of data to create the visibility needed to accurately detect and respond to a range of potential threats at sea.

My first job after college was working as a fiber optics engineer. One project was to collect data from a strain gauge within a rotating shaft via optical transmission. There was valuable mechanical stress data that our customer needed for their product design team to leverage for higher reliability.

Magnetic Bearings Inc. (MBI) was perhaps the most advanced technology company I worked for during my career as an engineer. MBI developed electromagnetic bearings to levitate high-speed (15,000 RPM) rotors in turbines for applications such as pumps for natural gas pipelines.

There was a tremendous amount of detailed vibrational data collected at MBI through microcontrollers. This data was used to “tune” the bearing dynamically using algorithms that enabled machines to reach higher speeds than traditional oil bearings. Without a reliable feedback loop of highly accurate data collection, the magnetic bearing system would fail – sometimes catastrophically.

Close-up of a Flex-Metrics Direct Machine Interface (DMI) module with wired sensor connections for real-time manufacturing data collection and machine monitoring.

I have now spent the past 20+ years collecting data within manufacturing plants using a Direct Machine Interface [DMI] technology, like a Fitbit that attaches to manufacturing equipment. I’ve been deeply involved in developing data collection systems for production equipment ranging from printing presses to bottle fill lines to automotive assembly robots.

As a result of these career experiences, I can’t help but notice automated data collection systems that appear to be spreading at an exponential pace. For example, I was recently at a fast-food restaurant and noticed they had invested in a real-time data collection system for their drive-through lane to count cars and highlight current and average wait times at the window.

Even fast food has data automation and KPIs

Why do businesses spend various degrees of money on these types of data collection systems? As usual, it depends on what they seek to achieve, such as:

• Real-time Visibility: Typically, stand-alone monitoring solutions. Provide a better “line of sight” to build a culture of ownership and accountability. Minimal change-management skills are required.

• Process Improvements: These data systems require organizational change agents and sophisticated data users to generate a payback. Data-driven changes are typically focused on the process, product designs, or capital equipment. Requires highly trained decision-makers and disciplined internal processes to implement and sustain changes. A wide range of potential changes in human behavior may be required to realize a positive return on investment.

• Automated Process Optimization: Historically, such data collection systems are coupled with advanced technology and capital equipment. Improved accuracy and timeliness of feedback loops drive optimization algorithms. Ideally self-optimizing systems based upon well-tuned feedback loops have minimal dependency on human behavior. Examples include automated count control and speed optimization algorithms.

  
  

Given the spectrum of complexity that follows these different levels of manufacturing data analytics systems, it is not surprising that many organizations tend to over-shoot when they realize they have fallen behind the competition when it comes to building a culture of data-driven decisions

Fast-food drive-through monitoring screen displaying real-time service time metrics and operational performance data.

Like the display at the fast-food drive-through, it’s hard to go wrong with a simple, real-time visibility solution. High performers like to keep score. Presenting accurate, real-time data in an intuitive format makes it easier for everyone to “stay in the game”. Real-time visibility enables practically anyone to be more productive immediately when there’s a line of sight on current performance results.

Flex-Metrics Shift Score Card from the Equipment Portal. **queue oohs and ahs**

Collecting data to make design changes to a product or process requires an entirely different level of data users’ skills over a longer time frame. This is the classic example of engineering, which is to apply math and science to make changes to create a Future State that is tangibly better than the Current State.

In the business of software development, there is a concept called a Maturity Model that explains how some software firms reach a Level 4 Maturity by following a proven process to release high-quality code that just works. Unfortunately, there are far more software businesses stuck at Level 0 “Ad-hoc” Maturity that brute force results every day. A key concept in the Maturity Model is that it’s impossible to jump levels.

I’ve seen the same concept play out with manufacturing sites that want to go from no data collection to full-blown Kaizen black belts in a single initiative. Instead, I would strongly advise starting slow and simple, then moving fast based upon “quick wins”. Walk before you run!

If your manufacturing site is not currently utilizing real-time visual factory displays on the shop floor, that is one of the easiest ways to start on the journey toward a data-driven culture of Operational Excellence. Once a team gets a taste of trustworthy data that makes it easier to win, with less stress, going to the next level of data maturity comes much more naturally.