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Manufacturer using machine monitoring to measure and improve OEE
Robin-Hartley-WillowsMar 27, 2023 12:02:44 PM6 min read

How to Improve OEE

Overall Equipment Effectiveness (OEE) is seen by many manufacturers as the gold standard in productivity monitoring, taking into account Performance, Availability and Quality.

This article looks at what OEE is, how to measure and improve OEE and considers the benefits and drawbacks when using it as a measure of manufacturing productivity. 

What is OEE in manufacturing?

Wikipedia defines OEE as, a measure of how well a manufacturing operation is utilised (facilities, time and material) compared to its full potential, during the periods when it is scheduled to run. It identifies the percentage of manufacturing time that is truly productive. An OEE of 100% means that only good parts are produced (100% quality), at the maximum speed (100% performance), and without interruption (100% availability).

How to measure OEE

OEE is established by identifying any losses in performance and availability of the machines, and quality of the end product. A high OEE score is the result of these three factors being correctly monitored and managed, and any losses minimised.


Let’s start by looking at performance.  By identifying where performance is lost, what stops the machine from working to its full potential/speed, and how this overall loss of performance affects the overall outcome are all key to increasing your OEE.

Things that can affect machine performance are wear and tear, age, jams etc and these factors can cause slowing or stoppage. By quickly recognising the loss of performance and the root cause, the relevant corrective action can be taken to remedy it and increase the ratio of run time to performance loss.


To establish availability, manufacturers should compare the machine run time with the total job time to establish the overall availability and availability loss. Availability loss is usually a result of any substantial stops (caused by perhaps material shortages or mechanical problems) and staff and shift changes. In a perfect world, the ratio of loss will be significantly smaller than the machine run time.

Monitoring the availability losses and working to decrease them, will increase the productivity and potential of the machines and your overall OEE score.


The last measurement in OEE is the quality of the end product. Manufacturers need to look at how many products from the production line meet the product quality standards and offset any time taken up by quality loss such as remanufacture and reassessment. Identifying how much quality is lost, and why, can again help operators remedy any problems in their control and increase productivity.

Factories should be aiming for an OEE of at least 80%.  However, most achieve 60% or less; massively impacting manufacturing productivity and profitability. 


Things to consider when using OEE as your productivity measure

A potential drawback of using OEE

As with any process, ‘one size’ does not fit all when it comes to OEE. In many cases, manufacturers have traditionally found the process of measuring OEE laborious, yielding potentially inaccurate results.

Without systems and automated processes in place, measuring OEE is usually a manual job; each line must be assessed on the above criteria to work out the OEE and then create a plan of how to optimise it. This is a time-consuming process and potentially causes more issues than the value it creates, as the data may be inaccurate or outdated.  

The benefits of OEE

If measured correctly, monitoring OEE and using the data as your productivity measure can have incredible benefits to manufacturers. These include improving production line efficiency by identifying and encouraging the right behaviours that result in the minimisation of losses in key areas.

An example of this may be where machine operators use real-time machine data and historical trends to take corrective action to reduce unproductive machine downtime, thereby increasing availability and performance which will reduce the cost of manufacturing goods and increase production capacity

What solutions are available to Improve OEE?

There are a number of solutions available to manufacturers looking to measure OEE, but as we have touched on in this article, traditional methods are labour-intensive and typically inaccurate, whereas fully integrated OEE machine monitoring systems are typically out of reach for many manufacturers.  

The positive news is that machine data monitoring is becoming more and more accessible to manufacturers of all sizes, thanks to the development of Industry 4.0, or ‘smart technology’. Today, manufacturers can see in real-time their machine utilisation or OEE productivity measures using machine monitoring solutions such as FourJaw’s productivity improvement software.

The technology is both affordable and accessible to manufacturers, large and small and the technology works across all machine types, no matter the brand, model or age, giving manufacturers who want to measure OEE a quicker and more accurate way of obtaining and measuring OEE data. 

Using real-time machine status data can calculate OEE figures for the current job, or shift, enabling comparison to previous shifts and encouraging the machine operator and factory floor supervisor to take corrective action if needed. 

OEE Machine monitoring

Deploying a reliable and accurate OEE machine monitoring platform will help to minimise wasted costs and resources, eliminate human error, and ensure progress towards achieving OEE performance, availability, and quality goals.

So how does FourJaw measure OEE?

OEE in FourJaw tracks the performance and availability of your machine using the downtime reasons logged by machine operators. OEE and Performance, Availability and Quality all start at 100% and are reduced when issues are logged.

Anything that means your machine is not available to use because it is broken, undergoing maintenance or being programmed etc. is set to the downtime OEE category 'Availability'. Everything else is set to 'Performance'.

When a part is produced and known to be out of specification, the operator must log the period of time associated with this component (or the components that are incorrect) within the FourJaw platform.

Performance %

Performance = ((Total time in the period – downtime logged to availability issues - offline time) - downtime logged to performance issues) / (Total time in the period – downtime logged to availability issues - offline time) x 100

Availability %

Availability = ((Total time in the period – downtime logged to availability issues - Offline time) / Total time in the period) x 100


Quality % = ((Uptime logged in time period - time labelled bad)/Uptime logged in time period) x 100


The old saying “You can’t improve what you don’t measure,” holds especially true when it comes to improving your factory’s OEE.

To remain competitive, manufacturers need to embrace technologies that allow them to become more productive, measuring OEE and using machine monitoring software to do so, is one way to ensure this.

Although OEE isn’t a productivity measure suited to all manufacturers, those that do want to use it, but have been unable to do so because of resource restrictions, can now do so by deploying smart technology from manufacturing analytics companies such as FourJaw. FourJaw’s OEE functionality enables both small-batch and volume-production workflows to measure OEE in real time.

Machine operator logs machine downtime reason


FourJaw’s machine monitoring platform knows when each of your machines is running productively, by capturing the downtime reasons which are input and displayed on an easy-to-use tablet. This gives machine operators and managers easy-to-digest accurate and reliable data insight that can be used to drive continuous improvement across the factory floor. 

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Robin is the Co-Founder and CTO of FourJaw Manufacturing Analytics. Robin has a Masters degree in Engineering and is a Fellow of the Royal Academy of Engineering Enterprise. Combining his love of software with a background in engineering, Robin became a researcher at the Advanced Manufacturing Research Centre where he developed the core technologies that power FourJaw's machine monitoring platform. Nowadays, Robin heads up the FourJaw technical team and oversees the roadmap and development of the platform.