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Measuring OEE with Machine monitoring
Robin-Hartley-WillowsJun 9, 2023 12:22:31 PM10 min read

What Is OEE & How Can Manufacturers Benefit From It

Looking at ways to improve productivity in your factory? In this latest article, we explore OEE. Starting with the basics and exploring how manufacturers can benefit from it, important things to consider and how, if implemented with the right tools, can enable manufacturers to achieve manufacturing excellence. But first, let's start with the basics and define what OEE is. 

What Is OEE?

Overall Equipment Effectiveness (OEE) is a metric widely used in manufacturing to measure the efficiency of production equipment. Although OEE is not suitable as a productivity measure for all manufacturers, it’s widely viewed as the gold standard when looking for a KPI that which identifies what amount of manufacturing time is truly productive.

OEE for manufacturing uses a combination of three factors: Availability, Performance, and Quality. These metrics help manufacturers drive continuous improvement by identifying areas of improvement in their production process in order to improve efficiency, productivity, and profitability.

An OEE of 100% means that you are only producing good parts, with no downtime, as fast as possible. By measuring OEE, manufacturers can identify areas of their production process which could be improved. 

What Is OEE Used For?

OEE is used to measure the effectiveness of equipment and production lines. Understanding their OEE score allows manufacturers to identify areas of improvement in their production. For example, reducing downtime and waste, increasing productivity and, overall, improving profitability. 

OEE Benchmarks

OEE data can be used to determine the effectiveness of equipment and production lines. OEE benchmarks help manufacturers to identify areas of their production process that are underperforming. The following are the benchmarks for OEE:

  • A good OEE is considered to be 85% or higher
  • An average OEE is considered to be between 60% and 85%
  • A poor OEE is considered to be below 60%

Why Is OEE Important?

OEE is important because it provides manufacturers with valuable insights into their production process. By measuring the effectiveness of equipment and production lines, manufacturers can identify areas of improvement and make data-driven decisions to improve efficiency, productivity, and profitability.

Machine operator and operations manager look at improving process

Benefits of Measuring OEE

Measuring overall productivity is a crucial part of any manufacturing process. By using OEE as their productivity measure, manufacturers can gain insight into inefficiencies in their production process. Here are some of the benefits of measuring OEE:

Identifying production bottlenecks

By analysing OEE data, and focusing on the Performance and Availability indicators, manufacturers can identify areas where equipment or processes are causing delays or downtime, allowing them to take corrective action to improve efficiency.

Improving Equipment Performance

Measuring OEE, particularly the Performance metric, helps manufacturers track equipment performance and identify maintenance needs, allowing them to schedule repairs or upgrades to keep machines running smoothly.

Increasing Productivity

By monitoring OEE, manufacturers can identify opportunities to streamline processes, reduce waste, and increase throughput, leading to higher productivity and output.

Reducing Costs

Assessing all the OEE metrics allows manufacturers to improve equipment performance and productivity. In turn, this helps manufacturers reduce costs associated with downtime, maintenance, and waste.

Supporting Continuous Improvement

OEE provides a baseline for evaluating the effectiveness of process improvements and helps manufacturers track progress over time.

Stronger ROI

Measuring OEE helps manufacturers ensure they are getting the most out of their machinery. Using all three OEE metrics allows manufacturers to identify issues with equipment, reduce downtime and speed processes.

Greater Competitiveness

Using OEE to analyse productivity and product quality helps manufacturers to identify areas for improvement in production and stay ahead of the competition.

Actionable Data Insights

Having accurate data enables manufacturers to analyse their production process with objectivity, seeing where improvements can be made and OEE increased.  

How to calculate OEE

In practice, OEE is calculated as:

OEE = Availability × Performance × Quality

However, if we substitute the equations for Availability, Performance, and Quality included above and reduce them to their simplest terms, we can express the OEE formula as:

OEE = (Good Count × Ideal Cycle Time) / Planned Production Time

Measuring OEE: The 'standard' Calculation

OEE is measured using three key indicators: Availability, Performance and Quality. Your overall OEE is a combination of the three indicators; understanding them individually is key to improving production processes and increasing your OEE percentage. 

The formulas for each component are included below:

Standard Availability Calculation

Availability considers Availability Loss, which constitutes all events that stop planned production for a noticeable length of time (at least several minutes). Availability Loss includes Unplanned Stops (material shortages, equipment issues, and so on), and Planned Stops (such as transition time). Availability is calculated as:

Availability = Run Time / Planned Production Time

Here, Run Time = Planned Production Time − Stop Time.

Standard Performance Calculation

Performance considers Performance Loss, which constitutes all elements that make production slower than maximum possible speed. This includes factors like Slow Cycles and Small Stops. Performance is calculated as:

Performance = (Ideal Cycle Time × Total Count) / Run Time

Here, Ideal Cycle Time refers to the fastest theoretical time to manufacture one piece.

Standard Quality Performance

Quality takes into account Quality Loss, which excludes pieces that don't meet quality standards, including those that are later reworked. The standard Quality calculation is:

Quality = Good Count / Total Count

FourJaw's machine monitoring software uses an OEE calculation that differs slightly from the standard method.  We have designed it this way as it allows our software to provide OEE to small-batch as well as volume-production manufacturing, making 'OEE' accessible to manufacturers of all sizes.

How does FourJaw calculate OEE?

As we mentioned in the standardised OEE example above,  OEE involves three metrics: Performance, Availability, and Quality. We use these same three metrics at FourJaw, however, we calculate them slightly differently. 

FourJaw OEE = Performance x Availability x Quality

Calculating Availability with FourJaw

Availability in OEE measures the percentage of time that equipment is available for production. It includes both planned and unplanned downtime, such as scheduled maintenance or equipment failure. This metric is useful for identifying how much downtime impacts your manufacturing operations.

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

Calculating Performance with FourJaw

Performance measures the speed of production, taking into account any slowdowns or stops due to operator error, machine setup, etc. This metric compares how many units you are producing compared to how many you could theoretically produce in a given timeframe. 
How is Performance Calculated?

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

Calculating Quality with FourJaw

Quality is a measure of the overall quality of production parts produced versus the number of defects. Measuring quality helps manufacturers identify where production defects are occurring, allowing for necessary improvements to be made.

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

What are the benefits of FourJaw's OEE calculation?

As we have outlined in this article, the standard OEE productivity measure is not suitable for all manufacturers. Like our technology, we want to make measuring productivity measures accessible to all manufacturers.

The standard OEE calculation relies on access to part count data. This requires integration with the machine control systems, which can be expensive and won't work on all machines.

OEE performance report for benchmarking productivity

Furthermore, part-based calculations don't take into account what's going on at the shop floor level. For instance, if you scrap two parts, one may take an hour to fix while another takes a minute. However, in traditional OEE part-based calculations, these are both considered equal as "one part". FourJaw's calculation takes into account the actual time taken, which is far more representative of the impact on the shop floor.

Learn more about OEE machine monitoring with FourJaw.

OEE and The Six Big Losses

One of the most popular reasons for measuring OEE is to reduce the main causes of efficiency loss, known as The Six Big Losses:

  • Equipment Failure
  • Set-up and Adjustment Time
  • Idling and Minor Stoppages
  • Reduced Speed
  • Quality Defects
  • Reduced Yield

The Six Big Losses can be segmented into the three OEE metrics and subsequently used by manufacturers to improve efficiency. Learn more about how to increase production capacity with FourJaw.

Available Losses

One of the major causes of availability loss is equipment failure, which occurs when machinery is scheduled for production but is not operational. This unplanned downtime can be a result of machine breakdowns, unplanned maintenance stops, and more.

Another common cause of availability loss is set-up and adjustments, which occur when production is halted to accommodate changeovers, machine and tooling adjustments, planned maintenance, inspections, and setup or warm-up time.

Performance Losses

In manufacturing, idling and minor stops occur when equipment halts for a brief period of time. This can result from jams, flow obstructions, incorrect settings, or the need for cleaning, and is typically resolved by the operator.

Reduced speed occurs when equipment operates at a pace slower than the ideal cycle time, or the fastest possible speed. Causes of reduced speed can include worn or poorly maintained equipment, substandard materials, poor environmental conditions, or inadequate lubrication practices. Identifying and addressing these issues can help optimise equipment performance and minimise downtime.

Quality Losses

In manufacturing, quality defects occur during stable production and refer to any defective parts that are produced, including those that may be scrapped or require reworking. Common causes of quality defects include incorrect machine settings and errors made by operators or equipment.

Reduced yield, on the other hand, occurs when defective parts are produced during the startup phase until stable production is achieved. Similar to quality defects, this can result in scrapped or reworked parts. Reduced yield often occurs during changeovers, incorrect settings, and machine warm-ups. By identifying and addressing these issues, manufacturers can improve their yield and reduce the amount of scrap or rework required.

Why Using OEE as a Productivity Measure Isn’t Right For All Manufacturers

While OEE is the gold standard for measuring productivity in manufacturing, it's not the right fit for every manufacturer. One of the biggest challenges with OEE is that it can be time-consuming to gather all the necessary data, adding extra labour to often already busy manufacturing plants.

Given that OEE can be time-consuming and measures continuous productivity, it is typically a better productivity measure in continuous production environments that have few or little stops/changeovers. For manufacturers that operate in a batch production or job shop environment, where there are frequent changeovers or setups, OEE may not provide an accurate picture of productivity.

Common Limitations of OEE

While OEE is a valuable tool for manufacturers, there are some situations that can affect the accuracy of OEE measurements:

  • Not accurately tracking downtime and the reasons for it
  • Not accounting for all the production time, including scheduled downtime and breaks
  • Not accurately measuring the actual production rate
  • Not accurately measuring the ideal production rate
  • Not accounting for quality issues and waste

To ensure the accuracy of OEE measurements, it is essential to accurately track all production time, including scheduled downtime and breaks, and to accurately measure the actual and ideal production rates. It is also important to account for quality issues and waste, as these factors can significantly impact OEE measurements.

Still convinced OEE is the right productivity measure for your factory?

There are a number of solutions available to manufacturers looking to use software to capture OEE data, however, it is often expensive and requires technical skills to integrate it with other ERP systems.

However, as technology develops, there are solutions on the market that enable manufacturers to use plug-and-play platforms such as FourJaw's machine monitoring software, which offers manufacturers the chance to choose either 'utilisation' or OEE as their productivity measures.

OEE Machine Monitoring Software from FourJaw

To avoid the common frustrations of measuring OEE, manufacturers can utilise software solutions such as FourJaw. FourJaw is a powerful OEE measurement tool that helps manufacturers track downtime, identify production losses, and optimise their manufacturing processes.

With FourJaw, manufacturers can easily measure OEE and understand the underlying reasons for any production losses. Our software provides real-time data on availability, performance, and quality, allowing manufacturers to quickly identify areas for improvement and take action to address any issues.

OEE manufacturing data Visualised on factory floor

FourJaw enables manufacturers to see OEE in real-time or compare it by machine, shift, or factory overtime in order to benchmark and track progress. By monitoring OEE on an ongoing basis, manufacturers can continually optimise their production processes and achieve greater efficiency and productivity.

Using FourJaw to measure OEE, manufacturers can avoid common errors in measurement and gain a comprehensive understanding of their manufacturing operations. Empowering manufacturing plant managers to make data-driven decisions, optimise production processes, and ultimately improve their bottom line.

See how FourJaw can improve your manufacturing operations with a Free Demo

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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.