Managing OEE Helps Producers Through Difficult Times

Today more than ever, manufacturers are trying to squeeze every drop of productivity out of the assets they already have. Machine builders can help them achieve the OEE they’re looking for.

On the whole, manufacturers understand the importance of measuring overall equipment effectiveness (OEE). It exposes losses within their operations to better drive the decisions for improvement. It’s about finding bottlenecks, improving efficiency, and reducing downtime—and squeezing as much productivity as possible out of existing assets.

The trend toward tighter capital has been in place for a few years. But this year, more than ever, the COVID-19 pandemic is pushing more manufacturers to get serious about their digital transformation to achieve the agility and flexibility needed from their machines. And OEE is a great place to start.

Improved OEE can not only help manufacturers increase productivity, it can help them react to changing demands in the supply chain. When suddenly one product is sold out everywhere (toilet paper, say) while another sees a significant drop in demand (deodorant or, perhaps, pants), manufacturers need to be able to adjust. They need to understand if existing equipment can handle a rise in demand.

“If you’re not collecting the OEE information, or not looking at what’s going on on the shop floor, you don’t have the data to understand where you’re at now, or if you have a change in demand, if you can meet it,” says Matt Giordano, technical evangelist for Information Solutions at Rockwell Automation. You might have the capacity on some machines but not on the line overall. Or you might be constrained by a particular operation on the line. “Until you start looking at that stuff, you’re going to be struggling to adjust.”

This year has been a wakeup call for a lot of businesses, Giordano adds. “We need to understand what’s happening here so we can manage through these difficult times.”

And that’s where OEMs are in a position to help, especially when it comes to incorporating new technology that will move manufacturers toward a more information-driven future. 

Bosch Rexroth’s IoT Gateway software makes machine and process data more transparent. Source: Bosch RexrothBosch Rexroth’s IoT Gateway software makes machine and process data more transparent. Source: Bosch Rexroth

An essential for IIoT
For any manufacturer looking to start an Industrial Internet of Things (IIoT) initiative as part of their digital transformation, OEE is the go-to key performance indicator (KPI) to get the ball rolling. It’s tangible and relatively easy to measure, and can boost productivity right out of the gate, says Luke Durcan, director of IoT Consulting and EcoStruxure for North America at Schneider Electric.

Too many IIoT programs are overzealous from the start, getting everyone excited about big dreams. When they don’t deliver the planned ROI, the project gets canned and people get cynical about what benefits IIoT might actually provide. “But…put in a solution that’s going to drive OEE, and you could deliver $2 million in the first six months,” Durcan says. “It helps move people along the IoT arc. You get the hard cash returns of productivity.”

The data gained from a small-scale OEE project can be used to deliver value further down the line. “We can get 20 points of OEE in the first year,” Durcan says. “Then in year three, we can continue to grind out incremental OEE as we go forward.”

OEE is an essential first step for getting the data that’s needed to start digital transformation, Giordano says. “You can’t really start to do a digital transformation until you understand what’s going on on your shop floor,” he says. “You need data. You need to understand what’s happening.”


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Starting with the basics
Edward Jump, M-Powered IIoT digital analytics leader for Milacron, an OEM that makes technology and equipment for plastics processing, has seen a shift over the past few years where more customers have established ways to efficiently track OEE in some capacity. 

“There’s a varying range of what that means for customers, but it is a metric that most of the industry is starting to utilize and measure themselves,” he says. “But even companies that measure it, they still struggle with writing things down on clipboards, and not having accurate information because it’s relying on somebody saying a machine was down for two hours when it was really down for four hours.”

There are still a large number of plants just beginning to enter the digitalization age, but it doesn’t need to be a big leap, says Jim Hulman, manager of business development at Bosch Rexroth. “The fundamental data to gather is always OEE, and then further analysis comes from there, using data analytics and Industry 4.0 tools to give further and deeper analysis of root causes,” he says. “But you don’t need much data analysis or machine learning or any of that to solve a 14% discrepancy in OEE. It’s just simple analysis—walking it through, identifying the issue, and getting the team together to solve it.”

However you proceed, basic OEE is where you should start, Hulman emphasizes. “You find your big problems first,” he says. “Secondly, you look for simple solutions, like putting a robot in place to fix the process. Or look at ways to predictively determine when the problem’s going to occur.”

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The great equalizer
Once you’ve tackled smaller issues, taking OEE across the operation can bring considerable gains. As Schneider Electric’s Durcan puts it, to understand OEE on a particular machine is interesting; to understand OEE on a particular cell is useful. But you really need to understand OEE across the entire plant to know where your criticality points lie.

It’s the critical bottlenecks in a facility that drive productivity, Durcan contends. It’s a concept that Schneider Electric refers to as production synchronicity—looking across assets to synchronize OEE. “Everyone knows what their bottlenecks are,” he says. “It’s the synchronizations between bottlenecks that drive productivity.”

Production synchronicity integrates OEE and data to study more complicated relationships between OEE and productivity across an entire facility. An OEE issue together with a starvation event upstream, for example, could send an alarm to an engineering or production manager to let them know that an event might be about to occur further down the line. “It’s giving you a window to get in and maintain productivity,” Durcan explains.

Too often, the default measurement for manufacturers is looking at how many pounds they ran on a given machine, says Dan Sileo, chief coach of manufacturing at the FSO Institute, which helps CPGs understand how to adopt improvement programs. But this becomes very difficult at a corporate level because running production of plain pretzels, for example, is very different than running production of peanut butter pretzels. “The dollar delivery is different. The waste numbers are different,” Sileo notes. “OEE becomes the great equalizer.”

Manufacturers with multiple plants can find themselves talking dollars when looking at machine performance. But it always makes sense to look at OEE instead, Sileo argues. “It’s dangerous when you talk dollars because you’re not recognizing the complexities in each plant,” he says. “Then you let the complexity or differences in plants become excuses.”


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The machine builder’s place
With manufacturers looking at OEE across a workcell or even the entire plant rather than just machine by machine, where does the OEM fit into this equation?

Giordano has seen OEE used as a validator, measuring the performance of the OEM’s equipment. “They use OEE in factories as acceptance criteria for machines coming into their plants,” he says. But OEE can also be pushed from the OEM’s side—showing that their equipment is OEE-ready, with the right logic and tags incorporated to help the end customer collect OEE data.

Some OEMs are taking the initiative to help end users be smarter—providing a service element to proactively come in and help them operate their machine at the highest levels, notes Derek Thomas, vice president of Marketing and Strategy for Emerson’s machine automation solutions business.

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Milacron, for example, has taken the initiative to help its manufacturing customers get a better handle on OEE. “Where the machine builder comes in—or where we come in anyway—is by trying to help gather that information, to automate it, to get it into a true system. And then by really listening to the customer about what their problems are and then focusing on those specific areas of OEE and tailoring our solution with our partner ei3 to help them tackle those unique problems,” Jump says.

Milacron uses ei3 technology as the backbone of its M-Powered IIoT suite to make sure it has a secure and reliable connection to remotely troubleshoot customers’ assets. Along with ei3, Milacron combines the data with industry expertise to help its customers understand and improve OEE. “If certain downtimes are continually plaguing our customers or certain components are affecting their downtimes, we study those and we put things in place with ei3 to quickly deliver to our customers what they need to do to improve that score,” Jump says.

Take it to the cloud, or the edge
Higher-level analytics often rely on cloud services to crunch complex data across the enterprise. But OEE is also becoming more powerful with edge technology, Emerson’s Thomas says. You can capture machine parameters at the edge, trend them as they’re operating, and look backwards as well. “It lets you bring intelligence down to the operator,” he says.

Milacron’s team of data scientists typically start with a lot of computation and digestion of the data in the cloud. “We take large swaths of data every day from hundreds of connected machines. And we pull that up, we analyze it, we look at it for common patterns, we look for areas where failures occurred, and we study those patterns as well,” Jump says. “And then as we start to get better at detecting when those patterns are going to occur and start to affect a machine’s performance or eventually cause a downtime, we can package up those algorithms and deliver them to our edge devices, which are also supplied by ei3. And those edge devices can do a lot of this computation for us so that we’re not continually sending all of the data to the cloud.”

A cautionary tale
Providing a machine with a high OEE score is one thing. Making sure your customer understands what can affect the machine’s OEE is another. Bosch Rexroth’s Hulman recommends that machine builders give their customers the tools they need to understand what information is relevant. “It gives them a great starting point for continuous process improvement,” he says.

For example, Bosch Rexroth has an OEM partner that sells its machine at a 95% efficiency rating. Part of why that rating is so high is because the changeover is fully automated, running 200 different product configurations with all electronic setup and automatic configuration. An end user had come to them to say that they were only getting 85% efficiency, but the problem turned out to be upstream, beyond the control of that particular machine.

There are so many variables that are out of the machine builder’s control once the customer has the machine on the plant floor—operator training, proper maintenance, other machines upstream and downstream, etc. “The best thing for an OEM to do is to sit down with the customer and get a common understanding of what OEE means to them,” Hulman advises. “If I’m supplying you a machine that has 95% efficiency, these are the parameters that I’m basing that on. So now when an end user says I’m only getting 85%, let’s take a closer look at it.”

OEE will point you to the problem, but it doesn’t necessarily give you the solution. Maybe the machine is jamming because it hasn’t been set up right. Or the customer isn’t doing proper maintenance. Or they’re using a new material supplier that the machine was not built to handle. “So it’s very important for an OEM to clarify their understanding of OEE and which parameters are outside of their control,” Hulman says.


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Nudging the operator to better OEE
The human-machine interface (HMI) is the ideal place to “nudge the operator to do the right thing from an OEE perspective,” Durcan says. In one case with a premium cosmetics company, Schneider Electric had implemented a manufacturing execution system (MES) at the facility but was also working at the OEE level in terms of root-cause analysis, he recalls. Feeding that information back to the operator as a standard operating procedure (SOP) drove better productivity. “They got more specific in the SOP so they were less likely to get a human error on the machine. It was a virtuous feedback loop into the MES.”

Shifting workforce patterns have been a significant driver in changing HMIs to better help operators be a part of driving OEE. “We’ve been working on this for many years—making it as simple as possible but as configurable as possible,” Durcan says. “It’s taken us a while to develop those tools to be agile and robust enough to deliver value. At the system and enterprise level, they can ingest that complexity of data and do something with it.”

It’s not atypical for manufacturing facilities to have paper-based pie charts and graphs hanging up on bulletin boards for weekly analysis. Instead, Bosch Rexroth shows OEE data in real time on a large monitor on the shop floor. “If you’re running during that shift and your OEE data is not where it should be, you can immediately react upon it and not wait for meetings to take place or new charts to be pinned up,” Hulman says. “It allows the machine operator to view the live data, make changes, and fix things right away.”

Make OEE work for you
Measuring OEE enables manufacturers to not only identify the underlying losses of their equipment, but to benchmark progress and improve productivity. OEE in itself, however, does not hold all the answers. Manufacturers need to dig beneath the surface to find the causes of any given OEE score.

“Tracking is just the beginning,” Rockwell Automation’s Giordano says. “That score can guide you as to where you want to look next.”

Hulman agrees, noting it’s a good chance to open up other opportunities in efficiencies. “Opening up one bottleneck allows you to view the other possibilities.” 

But Giordano emphasizes the need for buy-in from customers—a commitment to use the information OEE provides. “Just because you throw an OEE number up on the TV doesn’t mean it’s automatically going to improve. There has to be a cultural change as well, a commitment to use it,” he says. And keep using it. “If you don’t continue to use it, you’ll start to lose value in it.”

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