How to Apply OEE for Maximum Effect

Feb. 20, 2024
Jim Toman of system integrator Grantek explains the overall equipment effectiveness formula, how to apply the results and why the metric remains relevant.



David Greenfield
Welcome to the Automation World Gets Your Questions Answered podcast where we connect with industry experts to get the answers you need about industrial automation technologies. I'm David Greenfield, editor in chief at automation World and the question will be answering in this episode is: How do you apply OEE for maximum effect?

Joining me to answer this question is Jim Toman, senior manufacturing execution system advisor with system integrator Grantek. So thanks for joining me today, Jim.

Jim Toman
Absolutely. It's a pleasure to be here.

David Greenfield
So, you know, let's start by, you know, just first explaining what overall equipment effectiveness is as well as, you know, sharing the formula for calculating it.

Jim Toman
Certainly. So overall equipment effectiveness, or OEE, which was usually how we say that it's a measure of how well a manufacturer uses a specific production liner work cell. So basically there are a few factors that can't be changed in manufacturing, such as how much time there is in a day or perhaps the objective maximum rate at which you can make the product.

But there are other factors that affect the actual rate at which product is made, and those include availability. Which is the amount of time that production happened; Throughput, which is the actual rate at which product is made and quality, which is a measure of how much good product was made versus bad product. There's complexities under each of those measures, but that's pretty much it in a nutshell.

So we take those three elements and we multiply them together as percentages and that's what gives us OEE. So the OEE equation is availability percentage times the performance percentage times the quality percentage.

David Greenfield
And I imagine when doing those percentages, you should put them out at by decimal point based on that to for that. Or do you do them as whole numbers and then it all translate into a percentage or?

Jim Toman
You know, it's it's interesting, depending on how people want to view the number and how much precision they want in it, you can either use decimal points and and move it out by that. Or you can just use whole numbers. A lot of folks in the manufacturing world where they are just trying to understand how close they are to their benchmark. We'll just look at the whole numbers and say, oh, we're 1% up or 3% down or whatever it might be.

David Greenfield
OK, just want to make sure we didn't look over any specifics with that. You know, when it comes to doing that calculation.

Jim Toman
For sure, yeah.

David Greenfield
So Jim, you know, can you give an example of you know how to Cook calculator we you know you gave the basic formula but how to do it for a standard manufacturing line? Of course I know there's going to be differences and every application, but for a good general example of that.

Jim Toman
Sure. You know, I've seen OEE applied in a lot of situations and you know in all three types of manufacturing such as discrete process and continuous. Umm for this example I guess I'll just take discrete manufacturing, cause it's probably the easiest to grasp.

So let's say we have a manufacturer that makes snack packs food and Bev is an area where I commonly work. And let's say that the production line has a filling step or wrapping step, a sealing step, and then maybe weighing and cartooning. Probably some other things in there too, but for simplicity we'll just go with that and we're going to assume that this is just a single line, that there's no deviations in it are no, you know, forking off to different equipment or things like that. So what we would basically do is measure how many packs we fill in a shift, how many were rejected for quality issues, and how much time the line was running versus being down or stopped. So we're not going to take into account things like changeover or cleaning time.

There's a lot of complexity you can add, but for simplicity we'll just kind of ignore those for right now and we'll look at the OEE for the line as a whole. So for availability percentage, what we would do is we would take the number of minutes that the machine or the line actually was running divided by the total minutes that were available for the shift, including uh, sorry, minus the the cleaning and change over time like I had just suggested.

So let's say that the line was down for equipment issues for a couple of hours in an 8 hour scheduled run. So if we had that, then we had eight hours of time available. We had two hours down. So our availability on that was 75%.

Uh, it's that simple.

Then for performance, for the performance percentage, let's say we have, uh, we're going to take the ideal cycle time of the filler. We're going to multiply that by the total number of packs filled, and we're going to divide by the running time. So if we ran at full speed for the entire time that we were running, then we have a performance of 100%.

So in this case, the ideal cycle time is is being met by every pack that we make. It's, say a highly mechanized line and we don't have to worry about variations. So performance is really good in this scenario and then for quality we would simply take how many packs we had to reject for quality issues and then subtracted from the number of total packs would tell us how many good packs we made. We would divide that by the total packs, so if we lost 50 out of 1000 packs, let's say we made 1000 packs on the shift, we lost fifty of them due to quality issues. Then our quality number is 95%, so that's that's basically the long and short of how you get each of the legs or each of the components of OEE. So we would take 75% for availability.

We would multiply that by 100% for performance and we would multiply that by 95% for quality, which would give us an OEE of 71% for that shift.

And again, I'm ignoring decimal points in this for simplicity, but that's how you get the overall equipment effectiveness number it it's affected by each component and they're affected equally by each component.

So if you see your OEE go up or you see your OEE go down, you have clarity on whether or not something got better or worse during that production run.

David Greenfield
Thank you for explaining that. And I think that's one of the key things why OEE continues to be a popular metric in manufacturing, because it looks at those, you know, those constituent legs of the operations individually. And altogether I think that's a very key aspect of it.

And you know, while it's easy to understand, you know what OEE tells you based on the formula is you know, as you just explained, how do you then apply it to achieve a quantifiable impact or an improvement in production operations, you know, what do you, what do you do with it once you know what it is, you know how, how do you go about, you know, instantiating what you've learned to actually improve operations.

Jim Toman
Yeah, it's it's a great question. Umm, what's interesting is over a lot of years I've seen it used several different ways.

One of the I guess I'll list the top three things that I've usually seen. One is that it's commonly used as a success measure. So when companies are doing this, they're they're working to compare, you know, one operation versus another operation either lying to line shift to shift or plant to plant. And it allows managers to understand where the operation is performing better in one situation versus another or which particular product is better to place in an area that better handles higher volume versus one that doesn't. And when they should change operations from one product over somewhere else or two, something else.

It also often in this scenario can be used to plan production and specifically production schedules based on the past performance history so that they can make more reliable commitments to customers and probably another way that that gets used is is to incentivize production teams.

So for example, you can show them what their OEE number is over the course of a shift and they know whether they're ahead or behind of meeting the target that they're meant to to meet. So a success measure is one of those things.

A second thing could be as sort of a Canary in the coal mine for equipment performance issues. Umm, you know, for people that are are using it this way and I often see this when when it's umm an aspect of a maintenance regime, the company might be looking for negative trends and the OEE number.

So in other words, it started here and then it's degrading to here and now it's degrading to here and it seems to be going in a trend that isn't positive and it gives them the ability to then look into underlying data to try to understand what kind of issues might be developing on that equipment. You know it.

I see this a lot like where we have very highly mechanized equipment and if they have, for example, increasingly lower speed, that may be results in a lower throughput number. Perhaps there's something on the maintenance that needs to be done. An investigation needs to be made to find out if maybe that you know a belt needs to be replaced or something needs to be done for a preventive maintenance to prevent to prevent a breakdown situation, or something along those lines.

So you can actually make measurable changes to your overall production performance if you keep an eye on those things and try to get ahead of problems as you see them starting to develop.

So that's item 2 and then #3, which I think is actually the most interesting and maybe the place where it's best used is when companies use OEE and the data that supports OEE as an enabler for continuous improvement. So this is where they might take uh, the data from a particular shift or a particular run, and they would have that available either on a production report at a daily meeting, or perhaps even a weekly meeting or a monthly meeting. And they would look at it to see where did we have issues during the course of this time period. Where did we not make as much product as we expected to or where did we have more breakdowns than others or where did we have troubles?

And then the most effective use of that is where a company will take that information and apply it as part of their general continuous improvement process. So they'll they'll apply Lean 6 Sigma principles to it.

Maybe they'll do a five whys investigation, or they'll do a fishbone analysis. If there was a particular set of incidents that happened over and over again, and they'll figure out how to address the problem points and then they might make a change and then they'll use OEE to verify that the change had the benefit they were expecting.

So they'll look at seeing that OEE score back at the level that they expected it to be out or maybe even better depending on how those improvements take effect 3 different ways. You know, there's probably more than I'm that I'm summarizing here, but it's it's amazingly flexible how people use it. It's got a lot of capability just in one number to be able to detect whether things are getting worse or getting better.

David Greenfield
Thank you for explaining it, because it's one don't know if I call it a criticism of OEE but one of the things I've heard pointed out about OEE over the years was, you know, it's great. It tells me where I'm at and what we're doing, but now what do I do? But You know, how do I apply it? So those 3 examples are good.

You said there's probably many more, but good areas to start with and reviewing what the numbers tell you so you know over the years, you know, OEE has it's become a standard feature on many automation software packages, particularly manufacturing execution systems but stand alone. OEE software is also out there so can you share some insights into what manufacturers should look for in both types? That is where OEE is a feature and a larger software package and where it's standalone software.

Jim Toman
Great question. You know, this is an interesting marketplace to have been in over the last two to three decades. Things have changed a lot as as I've been, you know, moving through the course of my career in that time frame. I mean, at first it was sort of a A either roll your own kind of a thing or people would build a, you know, a point solution to to be able to handle, you know, more automated forms of OEE.

But nowadays we have so many different offerings out there and they cover so many different aspects. We have cloud offerings, et cetera. The way I see it, you know, companies are either looking for something that will help them to get quick operational wins or else they're looking for an OEE capability that's part of a larger strategy toward digitalization.

Those are kind of the two avenues that I see a lot.

I'm sure that there's probably variation in there, but those those tend to be the places I would mentally categorize them. You know, if a company is just looking for operational quick wins and they're really not concerned about the greater digital transformation journey, then that's where I see the role of those standalone OEE packages. Really, you know, making the mark, they're the ones that tend to work mostly by mobile app and usually cloud based. So that you don't have to, you know, spend money and own a server and do a capital setup or any of that kind of stuff.

And then they normally handle, you know, their data from a manual data input standpoint. That's the basic way that they do it.

Sometimes they might deploy specialty sensors out into the production environment to collect, you know, production counts and things like that. And there's probably a few ways that you could do integrations to various things if you really want to get creative. But mostly they're really sort of a one size fits all. Yeah, kind of a a special point type of system that people will tend to deploy in order to to try to make it come up as quickly as possible.

Get as much data as they can and then make decisions from that data and knock, you know, not just the low hanging fruit out, but really try to Plumb the whole tree so that they can get get operational improvements where they wanna see them.

On the other hand, companies that are looking for you know more of a a I'm sorry, I actually didn't really finish talking everything about what I wanted to say on that. So you know those those point solution applications are typically subscription based. They can be cloud based. They offer mobile apps natively, and usually the reports that they have are very highly templated and they normally have umm uh templated data entry forms for manual data entry and for product in downtime data and so on. So usually you'll see those deployed at the discretion of an operations department. For the most part.

So on the other hand, companies that are making more of a digital transformation journey, there's a lot of platforms on the market that can offer OEE as a capability inside of a broader set of capabilities and some of these have even been templated out by integrators to make them easier to install and operate and to get fast time to value when implementing them.

But doing that without sacrificing some of the larger opportunities that those platforms could give you toward greater MES capabilities, manufacturing execution systems shorthand as meds, or for other integration points where we might have higher degrees of automation or we want to integrate them to limb systems and things like that by deploying those kinds of systems, they give the manufacturer several advantages, including native integrations to production equipment.

So lot of companies, even if they aren't fully automated across their systems, their equipment typically will have a digital controller in it that these days normally will have communication capability used to be 10-15 years ago it was harder to find that, but now it tends to be more common and that controller has data in it and you can use it. So applying these applications can help you get that.

They also give you the ability to share that production information with other capabilities, including production, order management, batch control, statistical process control, track and trace, et cetera. And a lot of these applications are also mobile app based.

Nowadays, some of the platforms have mobile app capability as sort of just another way to view the same information that's in it, but so you get mobile apps for that. You can get templated reports with that, actually usually a wider variety of reporting capability. Because of that, and they also usually have much more flexible integration models to other systems and so you can get those integrated to your ERP system more easily.

You can get them integrated to your quality management systems and so on as well, and the other key advantage to these is typically they're deployed in a much more in a much wider variety of ways.

So you can deploy them on premises if you have a capable IT department that's bringing this in house, you can do a hybrid model. You can deploy them fully in the cloud if you want to, but you also can essentially own the licensing for that, so you're not paying a monthly subscription fee if if that's attractive to you as part of a capital project.

So really it's a decision point that comes from not just the key features, but also looking at it from the standpoint of is this really just to solve a particular problem or are we building this as part of a platform that's going to give us a higher degree of digital transformation down the road. And OEE is really the first step for it.

David Greenfield
Thank you for explaining. I know there's a lot of moving parts in both of those, but like I said, it comes down to really what you're you know what your strategy is. You know, around, you know, manufacturing improvements in your company and what you're going towards that ultimately helps decide that.

Jim Toman

David Greenfield
Umm, so in your experience you know, working with manufacturers in a variety of industries, you know, aren't there key mistakes that you see them tend to make around OEE that prevents them from gaining you know the real benefit from it?

Jim Toman
Yeah, you know it's it's a, a truism, I guess that when you try to do something for the first time, you often don't do it correctly the first time and you end up doing it a second time and you get better at it. I've seen a lot of things happen.

I think probably the single biggest mistake I see people make is really trying to do too much with it too soon. So what that means is, for example, in an OEE system, one of the things that OEE systems will typically measure is downtime and downtime measurement affects the availability part of the equation.

But because you're measuring downtime, usually these systems will give you the ability to measure many reasons for downtime, and so you can connect that if you have an automated set of equipment you can connect that to lots of different things, and it's pretty common for engineers to want to expose as much data as they possibly can as efficiently as they possibly can.

And So what often happens is we'll have a scenario where, umm, instead of starting at the top and implementing it and letting it run and trying to figure out what's going on from that point, what will happen is we'll try to design out all the possible downtime reasons in these giant trees of data that can have multiple levels and lots of depth and so on, which is fantastic for detail, but what ends up happening in practice is it it's too much detail. It's  no beneficial because you end up looking at you end up #1 trying to collect too much data, which sometimes can be a performance constraint on your system.

But #2 you end up looking at too much information, it's hard to see the it's hard to see the forest for the trees to really understand what's going on. So what we typically recommend for people to do in that scenario is to maybe pick the first one or two levels of downtime reasons, narrow it down to like 5 or 6, and look at the categories first. So before you do anything else, start to collect data for categories to understand what tends to be your most common category, and then when you narrow in on that, then add another level and then when that doesn't give you enough information, narrow in on the next level so that you're using it truly to troubleshoot your process as opposed to just trying to create a big wave of data that you can then try to mine later.

That's one pretty common mistake.

I would say that I really maybe another one would be failing to properly integrate it. Integrate your technology with your people and your process, and this is particularly common in continuous improvement. So where I've seen this happen is I'll see someone say hey we need OEE because OEE will tell us how we're performing.

OK, great. And we want we want to be able to make our performance better. OK, good. But then they'll install it, and then they'll have a continuous improvement group over here on the other side that doesn't know anything really about it, doesn't know how to access it, can't get the data. It's not helpful to them. They know, OK, we're doing something with it. We have a number. We can see it goes up or down, but we don't have any access to the information to help us make it better.

And so you need to think about it when you're building it in, when you're building it into your system with the intent of using it as a continuous improvement tool, you need to enable your continuous improvement team members to access and use that data in a way that they can actually mine it and get the appropriate processes in place to be able to act on what it's showing you.

I know it sounds almost ridiculous for me to even say that, but I've seen it a lot of times where it's part of a bigger project and it gets put in and then I ask who's using this or how's it going after a year or two and people will say, Oh yeah, we see it up there on the on the display board, but doesn't really impact us very much. And I just wonder about the lost opportunities when I see that.

David Greenfield
Interesting. They're showing it to him and it's visible and you can't miss it. But yet there is just another item on the board.

So one last question, Jim, and I can't help but ask this one. This took place a few years back and it was just after I posted one of an article about OEE that we had written here for automation world and I received a comment from a pretty well known industry investor and they said the 90s called and they want their metric back in reference to E Now you know this person was right in noting that OEE as we discussed as a long standing metric that you know is a common feature in many automation software packages. So I see how it could be considered something of a commodity metric.

At this point, you know, but the fact remains that OEE is one of the top search terms in manufacturing online and you know we're constantly reviewing our traffic and what's bringing people to our site. And OEE is always and has been for as long as I've been affiliated with automation World, one of the top three terms that lead people to the automation world site. And while we do have a good amount of OEE articles, it's far from our main focus, so it's not like we're over concentrating on that and that's why we're in the search results and it's very much, it's very clear that this is what people are inputting because they want to know more about it.

So with all that, I'm interested to hear your take on this. Is OEE still a relevant metric that should be a top concern for manufacturers or is it more of a table stake metric that's something of a given at this point or with all the equipment data analysis that's available now through AI and all the you know the a plethora of software systems that are out there has a OEE somewhat outlived its usefulness?

Jim Toman
Yeah. So the 90s called and they want their metric back that that's a funny statement.

David Greenfield
I'll never I'll never forget that, yeah.

Jim Toman
And it's certainly been around since since way back when. Umm, you know, it's a it's an interesting point in in technology circles and having worked my whole career in technology now I think we're trained by the way the market works and the way technology changes over time and the rate at which it changes over time. We're trained to sort of equate older things with obsolescence.

You know, at some point we say OK, that's not how we do this anymore. Now we do it like this or that's not the current thing anymore. Now it's this. You know there's a strong element of being attracted to shiny new things, you know, I'm guilty of it, for sure. I think those of us who live in this space were always we're always really looking forward. So the older things that maybe were foundational, they tend not to hold our attention much past a certain point.

But I guess what I would say to that is just because something is older doesn't make it no longer useful or relevant. Yeah, sort of a funny case in point—the wheel and the lever were invented millennia ago. Last time I checked, we're still using them and here we are in the 21st century.

You know, likewise, I've still got a dashboard in my car that tells me how fast I'm driving. One of my key metrics is mph. Uh, I still need it every day, right? Because I don't wanna get a ticket when I'm out there driving on the freeway. So and you know that was invented, what, 100 years ago? And it's still useful. It's still valuable.

I think uh for that reason and because of the fact that OEE is a basic metric that measures the performance of production and it's not industry specific and it's not even production specific, it's it's something that you can use across a lot of different capabilities. I really think that what you're seeing when you see people being drawn to the automation world website with that as a search term is the fact that it's enduring.

And if I think about the last two to three decades in the way that the way that technology has moved and especially automation knowing that the digitalization wave started, let's say in the 90s and it started with big companies that could afford to make significant investments into highly customized systems and we're talking a lot of capital being applied in those situations and we're talking about systems that weren't easy to build back then like they are now.

But over time it's got easier. It's got better. It's got smoother to do those things. The smaller and mid size manufacturers. What what I like to call the massive middle and the smaller manufacturers, they maybe didn't have budget to do that back then. Or maybe they couldn't do it with as much risk. Or maybe they couldn't do it because it was not part of a bigger strategy and they just didn't have, you know, the ability to, you know, put something in more inexpensively or or so on. They're starting to do that. They're starting to be able to say, hey, we want to implement this now in a way that is more automated. And so I think as we see a lot of those more mid size and smaller manufacturers waking up to adding that level of automation to their systems, you're seeing a lot of searches on how do we do this.

And I think that I can say that with some confidence because I know that, you know, in our business at Grant Tech, we talk to small and mid size manufacturers as well as large manufacturers all the time they come to us and they request to talk to us about things like our OEE accelerator. I know that's, you know, a very small piece of what we do. But we do hear those questions coming to us as well and it's because maybe that they just didn't have the focus 10 years ago. But now they're looking at it and they're saying we have to do this because we won't be competitive if we don't, we have to stay on the curve and we've gotta keep it moving.

So as long as manufacturers are using their capital assets for production, large or small, they're going to want to know how effective they are at using them. And I think now that the marketplace has broadened as much as it has and the technology has resolved the way that it has, we're going to see that OEE, we'll continue to be rolled out by manufacturers across the space.

It's certainly what I would call sort of the entry door to more digitalization, but I don't think it's the end at all. And I think certainly that you know, if you're going to do the other things in the digitalization space and you don't do OEE, then you're missing something because it's not that hard to bring that into the picture.

David Greenfield
Well, thank you again for joining me for this podcast, Jim and thanks of course to all of our listeners and please keep watching this space for more installments of Automation World Gets Your Questions Answered and remember you can find us online at to stay on top of the latest industrial automation technology insights, trends and news.