What is OEE in Manufacturing: The Complete Guide

OEE, or Overall Equipment Effectiveness, is one of, if not the most important metric to measure when it comes to evaluating (and improving) a manufacturing process’s productivity.

In a nutshell, OEE measures the percentage of what’s productive out of the manufacturing/production time while considering three different components: Availability, Performance, and Quality.

This article will be an ultimate guide to understanding OEE in the context of manufacturing. By the end of this guide, you’d have learned about:

• The concept of OEE
• Three different elements of OEE: Availability, Performance, and Quality
• The formula for calculating OEE
• Six Big Losses and how they impact OEE calculation
• How to improve OEE score

And more.

Without further ado, let us begin this guide right away.

What is OEE?

Overall Equipment Effectiveness, or OEE, is a concept developed by Seiichi Nakajima in the 1960s as a part of the Total Productive Maintenance (TPM) initiative.

OEE score is expressed as a percentage from 0% to 100%. A ‘perfect’ OEE score of 100% means your manufacturing process is performing:

• As fast as theoretically possible (100% Performance score.)
• Without any planned/unplanned stop time during the manufacturing time (100% Availability score.)
• While producing only good units (100% Quality score.)

Measuring OEE will give us valuable insights into three things:

1. The current productivity, efficiency, and especially effectiveness of the manufacturing process
2. Any bottlenecks and underlying losses (Six Big Losses)
3. How to systematically improve the OEE score and your manufacturing process in general

With that being said, we can say that OEE is the most important metric to measure in manufacturing that can help you benchmark your progress, identify underlying losses, and improve the productivity of each manufacturing piece of equipment. In practice, this will help you eliminate waste and improve the profitability of your manufacturing process.

Three Components of OEE

OEE, in a nutshell, measures how much of the production time your manufacturing line is running at its full capacity while considering three different components: Availability, Performance, and Quality.

By considering these three underlying components, we end up with the calculation formula for OEE:

OEE= Performance x Availability x Quality

Keep in mind that there’s a unique nuance to this formula. If, assume, you have a 100% score for two components (i.e., Availability and Performance), but only an 80% score for the other (i.e., Quality), you’ll get an 80% OEE score.

Meaning, having a high OEE score (i.e., above 85% “gold standard” score) can be easier said than done. 

Each of the three OEE components describes very specific areas of the manufacturing process, so you can pinpoint bottlenecks in each component and target them for optimization, which will lead to an overall increase in your OEE. 

Below, we will discuss each of these components one by one. 

OEE Performance

Performance score in OEE refers to the percentage of time the manufacturing process is at its theoretical maximum speed.

Two out of Six Big Losses affect Performance: 

• Slow Cycles: any period in which the machine is performing slower than its theoretical maximum speed, for example, due to machine wear or the usage of substandard materials
• Small Stops: any downtime under a specific threshold (downtimes above this threshold are considered Availability Loss instead) due to reasons like material jams, overheating, and so on.

The remaining time after all Slow Cycles and Small Stops are subtracted is called Net Run Time. 

Performance example

Let’s assume an LCD screen manufacturing plant can produce—in theory—240 LCD screens per shift; each shift is 8 hours (480 minutes) long. Meaning, the manufacturing process has an ideal production time of 2 minutes per product.

If, instead, it takes 500 minutes to actually produce the 240 LCD screens, we would take the ideal production time of one screen (2 minutes) times the total number of screens we need to produce (240) and divide the number by the actual run time (500 minutes.)

Performance score = (2 x 240) / 500 = 96%

OEE Availability

Availability refers to the percentage of time the equipment actually operates during the Planned Production Time.

Availability also takes into account two out of Six Big Losses: 

• Planned Stops: any planned downtime longer than the threshold we’ve discussed in the Small Stops discussion. For example, caused by planned maintenance, changeover time, warmup, etc. 
• Unplanned Stops: any unplanned machine downtime longer than the threshold we’ve discussed. For example, caused by equipment failure, depleted materials, etc. 

The actual remaining time available after Planned and Unplanned Stops are subtracted is called Run Time. 

Availability example

Let’s use the same LCD screen factory example here. Let’s assume that in the 8-hour shift, there is a total of 40 minutes break throughout the shift (Planned Stops), and there is an instance when the machine breaks down and cannot produce any LCD screen for 30 minutes.

Thus, we have a Run Time of 480 minutes - 40 minutes - 30 minutes = 410 minutes.

Then we can calculate the Availability score by taking this Run Time of 410 minutes and dividing this number with the Planned Production Time of 480 minutes:

Availability score = (480 - 40- 30) / 480 = 83.84%

OEE Quality

Quality in OEE refers to the percentage of units produced that meet up to the quality standards compared to the total number of units produced. 

Quality score is calculated simply by taking the Good Count (the actual number of units that meet the quality standards) and dividing the number by the total number of units produced (Total Count. 

Quality also takes into account two out of the Six Big Losses:

• Startup Defects: defective units produced between the machine’s startup/warmup time and the stable (steady-state) production time.
• Production Defects: defective units produced after the stable (steady-state) production time has been reached.

The remaining time after Startup Defects and Production Defects are subtracted is called Fully Productive Time.

Quality example

Again, using the LCD production factory example, the manufacturing equipment can, in theory, produce a screen every two minutes, and we have an actual production time of 410 minutes.

This will give us a Total Count of 210 LCD screens produced.

Assume that out of these 210 screens, 10 have dead pixels and did not meet the agreed quality standards. Thus, we take out the 10 screens from the Total count of 210 screens, ending up with a Good Count of 200 screens.

Now we can calculate the Quality score by dividing the Good Count of 200 screens by the Total Count of 210 screens.

Quality score = 200 / 210 = 91.32%

OEE score example

Now that we’ve got the Availability, Performance, and Quality scores for this LCD screen factory example, we can also calculate the OEE score of this scenario.

OEE score = Performance x Availability x Quality

OEE score = 96% x 83.84% x 91.32% = 73.5%

As you can see, although the plan scores relatively high on all three components, the overall OEE score is ‘only’ 73.5% due to the nature of the formula.

What is The Ideal OEE Score?

Every factory and every manufacturing process is unique, and there is no single ideal OEE score or even OEE industry standard for every industry you should use as a benchmark.

There is, however, a popular notion that the world-class OEE score is 85%, which originates from—again—Seiichi Nakajima in his 1984 book Introduction to TPM, in which he mentioned four “world-class” scores:

• 95% for Performance
• 90% for Availability
• 99% for Quality
• 85% for OEE

According to Nakajima, these are the minimum scores for which companies should strive based on his practical experience in the 1970s Japanese automotive industry.

However, not only has the industrial landscape dramatically changed since the 1970s, but the reality now is that most really good manufacturing companies have OEE scores between 60% and 70%.

Meaning, while you can strive to get closer to the 85% world-class OEE score, it might not be realistic for most manufacturing plants. 

Another consideration is that even within a single factory with multiple lines, it can be counterproductive to set just a single OEE score target. Even two seemingly identical production lines can have significantly different OEE scores in different situations.

For instance, if you have two identical production lines, but one line makes a single part while the other makes five different parts, the line making five different parts is likely to experience more losses (especially Availability loss due to changeovers,) which will result in a significantly lower OEE score. 

So, what’s recommended? It’s typically better not to put too much focus on a set number but rather on your plant’s ability to improve on your current score. 

How to set an OEE target

So, how should your manufacturing plant set an OEE target?

Your ideal OEE score is one that’s incrementally improving. 

For example, if your current OEE score is 65%, then strive to have a 70% OEE score next 3 months and 72% for another three months. Even if you experience a slight decline in your OEE score or one of the Availability, Performance, or Quality scores, that’s perfectly okay as long as you can identify the underlying cause. 

Start by setting an OE target that will drive solid, incremental improvement for your manufacturing process, then set attainable stretch targets, preferably within three months or a quarter.

It’s typically best to avoid comparing your manufacturing process with other factories, as well as external OEE benchmarks.

Your plant and your manufacturing process are unique, so there’s only one OEE target that really matters: the target that will drive improvement for your manufacturing process and productivity. 

OEE project implementation: how to effectively measure OEE

While every manufacturing process is unique and thus will require unique approaches to measure and implement OEE, we can generally divide the OEE project implementation into three key  phases:

1. Setting up the foundation. Defining the project and key decisions before you start the OEE project.
2. Capturing OEE data. Setting up sensors and OEE software to capture everything you need to calculate your OEE score.
3. Capturing Loss data. Establish a system to measure Six Big Losses and their respective impacts on Availability, Performance, and Quality factors.

Below, we will discuss these phases one by one.

Phase 1: Setting up the foundation

This first phase is about preparation. 

How well you set up these foundations for your OEE project will ultimately make or break the OEE initiative’s success, so don’t underestimate the importance of this phase. 

To kickstart your project, it’s typically best to start small unless you have a very specific area in your process you’d like to measure for OEE right away.

Ideally, find a single production line or, if possible, a single machine to start and expand from it as a base of success. You wouldn’t want to measure too many machines or processes in your initial OEE initiative, or else you might be confused due to conflicting information and lose focus on otherwise critical areas. 

Try to find a machine or production line that might engage or interest your operators to kickstart your OEE implementation, preferably a machine that produces only one type of unit. If it’s not possible, then pick a machine that produces multiple units with the same cycle time.

Identify bottlenecks

To be effective, you should measure OEE at the bottleneck (constraint) step of your manufacturing process.

A bottleneck is a single step in the process (or a whole machine) that limits the throughput of the process for one reason or another. The idea is that reducing/eliminating bottlenecks will improve your overall process’s productivity.

Along the way, the bottleneck and constraint may move after you’ve improved a constraint or reduced a bottleneck. In this case, move the OEE measurement to the new constraint and repeat the process.

Choose the ideal measurement method

Depending on the complexity of your manufacturing process and other factors, you can choose to measure OEE with either manual or automated methods.

If this is your first measurement, we recommend starting with manual OEE measurement. Doing so will also help you learn more about OEE in practice, which will help a lot in the long run. 

Later on, once you’ve got a better understanding of OEE, you can automate the measurement and data collection process to save time/resources and improve accuracy. Along the way, you’ll also want to expand the measurement to track beyond simple OEE like tracking the underlying Six Big Losses.

Phase 2: Capturing OEE data

Capturing OEE data is actually pretty simple in theory since it only involves three key metrics to measure:

1. Ideal Cycle Time. The theoretical fastest time for a machine to produce a single unit.
2. Good Count. The quantity of good/non-defective units produced.
3. Planned Production Time. The time the machine is scheduled for production.

To discuss them one by one:

1. Ideal Cycle Time

Measuring Ideal Cycle Time can be tricky in practice, and a very common mistake is to use the wrong Ideal Cycle Time numbers in the OEE calculations, skewing the actual score and leading to confusion instead.

Ideal Cycle Time is the theoretical fastest time the machine can produce one unit. It is critical that the number should be both honest and accurate for the time it was measured. That is, even if the process currently runs in a non-optimal way due to one reason or another, it’s important to report the honest number.

While there are many different methods you can use to measure ideal Cycle Time; the two preferred and easiest methods are:

• Nameplate capacity: the theoretical capacity specified by the machine’s/equipment’s manufacturer. 
• Manual data collection: manually measuring the absolute fastest speed that the equipment can perform in the current situation/environment

2. Good Count

Good Count refers to the total number of units produced that are defect-free. Units that need to be reworked (even if it’s a very simple rework) in any way should not be included in the Good Count calculation. 

You can either calculate Good Count manually (i.e., with manual observations) or with automated methods (i.e., by setting up sensors.) In both methods, you should be careful and focus on accuracy at all costs. 

In manual measurement, measure immediately after a constraint that accurately and reliably counts only good units. 

On the other hand, in an automated measurement, place the sensor immediately after a constraint that is accurately triggered only for good units.

3. Planned Production Time

Again, Planned Production Time is the Total time a machine/equipment is scheduled for production.

An important consideration when measuring Planned Production Time is to decide if certain types of Planned Stops will be excluded from the OEE calculation, for example, if breaks and certain meetings will be excluded.

Again, when collecting and measuring OEE data, it’s important to be honest and always strive for accuracy, even when data collection is challenging or difficult, or else the whole OEE calculation will be inaccurate. 

Phase 3: Capturing Loss data

While after the second phase, you should have enough data to calculate your OEE score, you won’t be able to leverage OEE to improve your process’s productivity if you don’t measure the underlying loss.

In this phase, you’ll need to measure two more metrics: 

• Total Count: the total number of units produced throughout the manufacturing process
• Run Time: the actual amount of time the machine operates throughout the Planned Production Time

1. Total Count

Total Count is required to measure OEE Quality score, in which:

Quality score= Good Count / Total Count

You can measure the Total Count directly, for example, via manual observation or by installing appropriate sensors. For manual calculation, identify and measure a constraint into which all produced units go. Or, if you’d like to implement automated calculation, you can place a sensor before this same constraint.

Another alternative is to measure Reject Count in the same way you measure Good Count (refer back to the previous section.) Then, you can simply add Reject Count to Good Count in order to get the Total Count number.

2. Run Time

We can calculate Run Time by subtracting Stop Time from Planned Production Time.

So, we need to collect Stop Time data to properly calculate Run Time.

Stop Time

Stop Time refers to all instances when the manufacturing process is scheduled to operate (within the Planned Production Time) but is not actually operating due to planned stops (i.e., changeover, planned maintenance) and unplanned stops (i.e., equipment failure.)

You’ll need to establish a system (either manual or automated) to record Stop Time accurately. The simplest manual method to collect Stop Time data is to use a check sheet/tick sheet to record start and end times for each stop. Alternatively, you can establish an automated data collection system to automatically record when your machine stops working. 

It’s crucial to remember that while Planned and Unplanned Stops are considered Availability Losses, Small Stops are considered Performance Losses. It’s important to decide the time threshold between the two (any stop shorter than the threshold is considered a Small Stop and shouldn’t be recorded as Stop Time.

For automated collection systems, a typical stop threshold is two minutes, while for manual systems, it’s five minutes.

3. Establishing a Reason Codes system

To help you accurately identify underlying losses and improve your OEE, it’s crucial to collect insights into why Unplanned Stops occur. 

This is where establishing a Reason Codes system can help you. And you can do so by:

• Create a list of stop reasons and assign a code for each.
• It’s best to start small, with no more than ten reason codes
• To keep your list small, create a catch-all code for “All Other Reasons” 
• Every reason should be clear and specific, and each should describe symptoms instead of underlying causes
• Along the way, remove reasons that aren’t regularly used 
• Add new reasons if “All Other Reasons” is in your top five losses

4. Measuring Changeover Time

For most manufacturing plants, Changeover Time is the most common cause of Planned Stops, so to maintain accurate measurement of your OEE and your losses, it’s important to measure Changeover Time consistently and accurately.

To do so, you should establish a clear and consistent policy on how you are going to measure Changeover time.

In general, you have three main options:

1. First Good Part. The time between the last good unit/part produced before setup to the first good part/unit produced after setup.
2. Consistent Good Part. The time between the last good unit produced before setup to the first instance of steady production of good parts (that meet quality standards.)
3. Full Speed. The last good unit produced at full speed before setup/changeover to the first good unit produced at steady/consistent speed after setup.

Conclusion

By now, you should have a clear understanding of the concept of OEE, how to calculate OEE, and how to implement OEE measurement in your manufacturing plant. With this information, you should be able to start leveraging OEE on your business and improve your productivity. 

It’s best to start small and measure the OEE score in a single machine or line first, and then you can expand little by little until, eventually, you can measure the OEE of every production line in your company.