November 22, 2022

The Ultimate Guide to OEE (Overall Equipment Effectiveness) for Manufacturing Improvement

What is OEE and what is it used for?

Overall Equipment Effectiveness (OEE) is defined as a metric used in lean manufacturing to monitor, evaluate, and improve the effectiveness of a production process. It is a key performance indicator for any production plant, workshop, etc. It is expressed in percentage.

Since the primary purpose of OEE is measuring the production performance, it is often said that OEE makes manufacturers acknowledge the “hidden value” in production when the calculation is done right. This implies recognizing the potential production capacity and optimizing the equipment & processes to meet such expectations.

OEE dashboard in the Blackbird application

OEE is a tool used to measure machinery and equipment utilization rates. It is one of the leading indicators for production improvement, as it helps companies identify which areas require attention. Despite seeming complex, the OEE calculation is easy to tackle when the necessary data is available through reliable sources, and best if in real-time.

With this guide, we shall explain what OEE stands for, the calculation method, and its implication for sustainable manufacturing and improving throughput capabilities with little investment. Finally, this article shall expose the advantages of using industry 4.0 technologies, such as our Factbird solution, to increase OEE performance.

Table of Contents

  • What is OEE?
  • Components of the OEE calculation
    • Availability
    • Performance
    • Quality
  • Calculating OEE
  • Is there such a thing as “perfect production”?
  • Benefits of estimating OEE in production
  • Team roles in OEE monitoring
  • Common issues that hinder precise OEE calculation
  • How does OEE impact Green Manufacturing tactics?
  • Strategies to improve OEE percentage
  • Why is Factbird a game-changer for improving OEE metrics?
  • Closing thoughts

Components of the OEE Calculation

The OEE manufacturing formula is quite simple to express:

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Therefore, we have three components that make the OEE equation. These shall be explained as follows.


Availability stands for the amount of planned run time that actually delivers production. In other words, the time that machinery is running and producing goods within a planned schedule. Two main categories can list the reasons for losses: planned and unplanned stops.

We can define planned stops as the downtime in production that is attributed to planned events, such as shift start/end, tooling adjustments, maintenance tasks, QA inspections, cleaning, etc. On the other hand, unplanned stops are unexpected events linked to equipment breakdown, unexpected stop codes, material jamming in the production line, lack of operators in the plant, power outages, etc.

It is essential to address that activities such as planned maintenance shutdowns do not take part in the OEE calculation. For some industries, seasonality should also be considered, estimating different OEE measurements for those peak or cold months; otherwise, the breadth between the highest and lowest production months can severely impact OEE calculation.


Before discussing what performance is, we have another metric to define, which is the Ideal Cycle Time, also known as the “Maximum Demonstrated Rate” (MDR). It refers to the specified operating speed, which can be a given or calculated value as it indicates the operating speed in ideal conditions.

The Ideal Cycle Time is required for the Performance calculation, as the Performance indicator measures the achieved throughput in the available run time, contrasting it to the maximum throughput value if the machinery ran at the Ideal Cycle Time. 

There are two categories for performance loss: micro stops and slow cycles.

Micro stops are the instances in which machinery experiences a very short downtime, usually less than a minute. Operators check the stop code to solve the issue without extra technical assistance, but the main problem linked to this performance loss is accumulation. Since micro stops tend to be repetitive, they can build up, leading to availability delays during the day.

Slow cycles, in contrast, are the messages we get from equipment when the ideal production conditions aren’t met: worn-out equipment, wrong settings, lack of proper environmental conditions, low-quality materials, etc. It means the machinery runs at a considerably lower speed than the ideal cycle time.


The final component of the OEE calculation is Quality, which measures the amount of production throughput that meets the required specifications compared to the total amount of production throughput. For losses, we have two distinctive categories: production rejects and start-up rejects.

We can define production rejects as all the goods produced at the steady-state stage that feature any form of defect, including those that can be reworked. Since Quality aims for “Right First Time” production, anything that doesn’t meet the standard reduces the Quality percentage. Broken packages are one of the main reasons for production rejects.

But what about Start-Up Rejects? This term refers to equipment that requires warm-up time, meaning production can suffer defects until it reaches the steady stage. Changeover processes that aren’t optimized can lead to start-up rejects.

Calculating OEE

Now that we have explored the three components of the OEE manufacturing formula, it is time to explain the calculation process.

If we remember the OEE formula:

Availability * Performance * Quality = OEE

It means we have to get the individual values of Availability, Performance, and Quality. We will break the calculation per section.

Calculating Availability

Follow these steps to calculate Availability for OEE.

  1. Estimate the planned productive time by summing all the scheduled production orders minus planned downtime. Seasonality has to be included.
  2. Capture the total downtime, specifying it by category (planned vs. unplanned stops) per production line. 
  3. Get the total Downtime percentage by applying this formula:

Downtime (%) = (Total Downtime / Planned Production Time) * 100%

  1. Calculate the Availability by assuming a 100% Availability and deduct the Downtime percentage:

Availability (%) = 100% – Downtime (%)

Calculating Performance

Follow these steps to calculate Performance for OEE.

  1. Get the available time for production, also known as “Up Time”.
  2. Capture the actual throughput during that uptime.
  3. Calculate the potential maximum throughput (based on the ideal cycle time) for that uptime.
  4. The Performance formula will look as this:

Performance (%) = (Actual Throughput / Maximum Throughput) * 100%

Calculating Quality

Follow these steps to calculate the Quality for OEE.

  1. Capture the total production volume.
  2. Identify the total count of rejected products.
  3. Calculate Right First Time by:

Right First Time = Total Production – Rejected Production

  1. The Quality formula will look as this:

Quality (%) = (Right First Time / Total Production) * 100%

Is there such a thing as “perfect production”?

Considering “perfect production” translates into 100% OEE value. Let’s assume that’s a theoretical aspiration rather than a reflection of reality. The truth is, no process can aspire to a 100% OEE value as that implies there’s no waste during production processes, no error codes, and not even unplanned stops.

Companies these days aspire for a range between 80-95% as superb OEE values, and that depends entirely on the industry in which they work. 

To better understand the implications of “perfect production”, we ought to consider these ideal situations:

  • Availability as an OEE factor can only reach 100% if the equipment is always running during the planned production time, meaning there are no delays in shifts, perfect availability of raw material, no stop causes, etc.
  • Reaching perfect peak Performance in OEE implies running at the theoretical maximum speed, known as Ideal Cycle Time.  
  • In what concerns Quality, getting a 100% means no production of defective goods. Every single piece produced is perfect.

As you can see, the implication of a 100% OEE value is, by all means, a theoretical aspiration. We can aim to get the highest value possible for our processes and throughput, but always be mindful of the actual production capacity and not overestimate potential. 

Benefits of estimating OEE in production

Now that we have discussed the theoretical elements, it is time to discuss the value of OEE in the manufacturing industry. For that very reason, let’s expose the main benefits of estimating OEE in production.

Benefit #1 – Getting the best performance from machinery

This point goes hand in hand with Benefit #2, and experience tells us that by using OEE monitoring software, we can see a drastic increase in production throughput. OEE aims at excellence from machinery; hence, you will get insights into which areas require effort and investment to get the best performance from your current equipment.

Benefit #2 – Return on Investment (ROI)

One of the main deterrents to upgrading machinery is not seeing the value on a short-term basis. Instead, when you opt for monitoring OEE and apply implementations based on those insights, you get an immediate increase in ROI as your machinery is tuned for maximum productivity. 

Benefit #3 – Unveiling a “Hidden Factory”

What if we tell you that you may not need to fully upgrade your equipment but rather invest in manufacturing monitoring software? OEE reveals the true potential of your machinery; therefore, instead of ruling out your current equipment as obsolete, you may be surprised about how some automated settings can drastically change production volume in just a couple of weeks.

Benefit #4 – Transparency for operators and managers

Instead of guessing the potential areas for growth, OEE directly leads both operators and managers in which areas of production can be improved. By counting with live-time, reliable data, operators can address the causes of production losses and prevent them. For managers, it gives a reference for decision-making based on facts.

Benefit #5 – Reducing machine-related costs

Since OEE addresses the causes of machinery malfunctioning as part of what affects performance, managers can evaluate the required maintenance tasks or repairs to improve efficiency on some production lines. Instead of performing scheduled maintenance simultaneously, with the expenses, it brings in terms of standby time, machines can be checked and fixed as required by OEE goals.

Also, keep in mind production monitor software is scalable, so you can start with a pilot in a limited number of equipment, evaluate the impact on performance, and then upscale.

Want to see what benefits you can get from calculating OEE? Get a personal demo of Blackbird and learn how to optimize manufacturing efficiency.

Team roles in OEE monitoring

Although we can monitor OEE via software, it is best to define the roles in which the OEE management is handled, as it is teamwork that starts from manager to operators and then back to management with the retrieved results.

Managers initiate the project by defining its scope, its goals, and defining the practices that operators, maintenance teams, and supervisors must follow. After the reports from OEE come back from the other levels, managers have to audit the results to alter the strategy as required.

Supervisors work by analyzing the losses during shifts and changeovers. They are the ones in charge of defining the ideal cycle time and who set the priorities for improvement tactics.

Maintenance workers retrieve the historical data from manufacturing monitoring software, and by following the indications of supervisors, they apply the required maintenance tasks per equipment. They also must report the eventual need for scheduled shutdowns for maintenance tasks.

Operators are the ones that observe and work on stop codes, but they also can report opportunities for improvement through factual information.

Common issues that hinder precise OEE calculation

Although manufacturing process monitoring software can help us develop precise OEE calculations, some variables directly impact our results. They can be resumed as follows.

Availability mistakes

Excluding too many stops is a common cause of unreliable OEE data. Often, micro stops build up more time than we can estimate, so by analyzing them as a whole, we can determine which areas must be re-checked. 

Ignoring changeovers is another cause, as initially, we can estimate 30 minutes (as an example) in changeover and end up taking 40 minutes or more. This is a significant availability reduction.

Performance mistakes

Inconsistencies concerning performance for the OEE calculation arise when manufacturers underestimate the actual production speed. Seasonality is also a known factor that alters Performance OEE percentage, and the outcome is that manufacturers can end up with OEE values over 100% when the optimization process starts. 

To solve this potential issue, manufacturers ought to consider the specified MDR value by machine manufacturers’, or work with the highest recorded speed in the plant.

Quality mistakes

In terms of what can arise as inconsistencies in Quality percentage calculation, the most common issue is scrap counting – a process that usually is done manually by operators. Since it is a time-consuming process, human-error-prone, reliable Quality data can take weeks to be processed. 

This is where Industry 4.0 technologies give a helping hand, as our Factbird solution can track scrap automatically when paired with a sensor. As we have seen in Quarder’s success case study, implementing scrap monitoring can reduce scrap rates by getting accurate information on the reasons causing scrap.

How does OEE impact Green Manufacturing tactics?

Companies worldwide are shifting towards zero waste strategies to meet the 2030 Climate Agenda. These efforts escalate from raw materials being used to monitoring energy consumption and waste; others aim to reach 100% renewable energy operations by a certain date. However, we can ask ourselves how OEE can make an impact in this regard. 

The main element to take into account is a mindset shift, in which companies observe waste not just from a lean manufacturing view but from the impact it has on the environment. This fuels the need to improve operations to reduce or even eliminate carbon footprint in all manufacturing operations, which in turn attracts both investors and customers. Another advantage of opting for green manufacturing tactics is the reduction in energy and resource costs, mainly if we consider the current rise in energy prices. Thanks to these two points, manufacturers began using OEE as a metric to measure their production impact on the environment.

OEE can provide reliable data on whether machinery should remain powered while in standby mode or if it is best to power down when production is halted. Information on this point can be accurately accessed by measuring OEE values with the current practices and a second OEE measure with greener practices in place. Managers can contrast data and take measures to adjust the strategy. 

As mentioned before, OEE can also be influenced by the scrap rate our production processes deliver. Therefore, improving the Quality percentage in OEE saves money by increasing the “Right First Time” product rate and preserving the environment through a reduction of energy consumed and generated waste.

Strategies to improve OEE percentage

Now that we have explored the different elements concerning the advantages of OEE, let’s talk about strategies to improve OEE in manufacturing. First and foremost, we should consider automating data collection and reports as the key strategy for increasing our OEE stats.

Insufficient data is the main element that hinders OEE, and manually retrieving data through operators takes both time and effort, which in turn can result in human-error situations. Another element is that the delays in this analog data collection process – and even Excel spreadsheets can be considered analog these days – give little room for change as we are usually running against time. Thankfully, IoT devices for manufacturing opened the gates for reliable data collection, providing insights 24/7 with little effort for operators. 

Using production monitoring dashboards on the shop floor can amazingly help operators to understand complex data, as operators are able to retrieve detailed information on stop causes, increase productivity, and in turn, boost sales. Still, a good practice is to build historical data for stop causes, as it helps supervisors and the maintenance team address repetitive stop causes linked to wrong settings, worn-out equipment, or low-quality materials. 

Finally, another strategy you can implement to improve OEE manufacturing metrics is to work on the Six Big Losses:

  • Breakdowns: Linked with the Availability factor in OEE, this refers to equipment failure, unplanned maintenance, energy outage, etc. In case you consider whether this could also be a Performance failure, breakdowns comprehend every loss that requires more than 5 minutes to be corrected, in contrast with minor stops
  • Setup and Adjustments: Linked with the Availability factor in OEE. This loss targets material shortage, changeover, warm-up time, tooling adjustments, etc. Cleaning, quality inspections, and planned maintenance can also be considered among these causes. 
  • Slower Cycles: This is linked with the Performance factor in OEE. It involves rough running, wrong settings, equipment wear, or operator inefficiency. Therefore, to address this loss cause, we should compare the cycle time with the ideal cycle time to find the cause.
  • Idling and Minor Stops: Another loss linked with the Performance factor in OEE. These losses are usually triggered by repetitive stop codes, material jams, misaligned/blocked sensors, obstructions in the product flow, etc. Operators can make a direct impact in reducing this factor.
  • Startup Rejects: Linked to the Quality factor in OEE. These are the scraps and reworks produced during the startup phase, especially in machinery with long warm-up stages.
  • Production Rejects: The last item on this list is also linked to the Quality factor in OEE. It involves similar elements to the Startup Rejects, but they are generated during the steady state. This comprehends packaging defects, incorrect assembly, scraps, pieces to be reworked, etc.

Why is Factbird a game-changer for improving OEE metrics?

Manufacturing monitoring systems are the answer to improving OEE metrics, and in this section, we want to explain how Factbird helps consumers reach their OEE goals.
Since an OEE analysis starts by looking at the plant’s operation time, we will consider that as the Total Equipment Time. From here, we emphasize the importance of establishing planned downtime and consider it in the analysis. Factbird solution uses the following waterfall approach to easily spot bottlenecks and stop causes that impact efficiency most.

TCU gives an overview of the planned downtime, considering vacations, breaks or simply no-production planned. 

OEE 3 accounts for all the planned maintenance work. Usually, this time is allocated during weekends as lines only run for a third of their average run time, according to the schedule. 

Potential stop causes in this stage are

  • Planned maintenance
  • Quality inspections
  • Personnel activities (i.e., meetings, training)

OEE 2 is associated with the time that a production line is not running due to batch changeovers. Although these stops are planned in nature, it takes a significant time in the total available time; and lines shall regularly stop for the changeover activities between batches.

Potential stop causes in this stage are

  • Batch Changeover (i.e., preparation, re-tooling, etc.)
  • Resupply (i.e., material, packaging, labels)
  • Cleaning (i.e., end of shift cleaning)

OEE 1 measure is associated with the actual expected run time. This value is linked to unplanned stops or slow cycles. OEE 1 shall then cover the downtime, cycle time, and scrap of production.

Potential stop causes in this stage are

  • Machine defects
  • Process defects
  • Waiting time (i.e., materials, operators)
  • IT related causes
  • Unplanned cleaning, e.g. after a defect.

By getting accurate data on how OEE is affected at different stages, managers can develop tailored strategies to meet their OEE goals. The Factbird range of solutions has two main advantages: quick installation and simple implementation. After setting the sensors in place and powering them, all that’s required is to log into the Blackbird application (which can be displayed on screens, tablets, computers, and mobile phones) and start monitoring the retrieved data. This information shall be available 24/7, 365 days/year, making it possible to address different situations in production lines, seasonality, etc.

Since no complex IT skills are required, the Factbird products answer the demands of customers that seek to increase productivity while staying on budget. It is adaptable to any kind of machinery – meaning that old machinery can be monitored and improved instead of having to acquire new replacement equipment. 

Factbird® requires Wi-Fi or mobile network connection, and then sends the collected data from the sensors to a secure cloud server. As the Blackbird Application analyses and visualizes data in the cloud, you can access the information in an easy-to-understand platform, accessible to all operators in real time. Ethernet is available on the Factbird® Omron NX1 products.

Closing Thoughts

As we have seen, OEE is a metric that can be used as a standard of efficiency in manufacturing. Ensuring we get accurate readings of OEE is the first step toward improving production performance. We hope this guide can explain the elements that make the OEE calculation and all the possible outcomes linked with this manufacturing metric.

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