Watch a demo Factbird's and see how you can improve OEE in a matter of days.
How to improve OEE
Enhance your OEE score with our expert tips. Discover ways to minimize waste and achieve your production goals efficiently in our OEE improvement guide.
Overall Equipment Effectiveness (OEE) is a key measure of manufacturing efficiency, combining equipment availability, performance, and quality.
Improving OEE is vital for better production efficiency and cost savings. In this article, we'll explore strategies to enhance OEE, its multiple benefits, the role each team member plays in this optimization process, and offer some case study examples of OEE improvement.
The benefits of improved OEE
Benefit #1 - Increased production throughput
OEE monitoring software can help to identify and eliminate bottlenecks in production, resulting in a significant increase in production throughput.
Benefit #2 - Unveiling a “Hidden Factory”
OEE can reveal the true potential of your machinery, and you may find that investing in additional industrial internet of things (IIoT) devices and sensors, coupled with smart data analytics, significantly increases production volume within a matter of weeks. This approach can save you the cost of upgrading or replacing equipment that may not be entirely obsolete.
Benefit #3 - Transparency for operators and managers
Instead of guessing the potential areas for growth, OEE shows operators and production managers where production can be best improved. With accurate, real-time data, operators can quickly address the causes of production losses and prevent them. For managers, it enables data-driven decision making. Here's a walkthrough of how production managers optimise production with Factbird:
Benefit #4 - Reducing machine-related costs
Since OEE provides insights into the causes of machinery malfunctions, maintenance managers can better evaluate required maintenance tasks or repairs to improve efficiency on production lines. Additionally, OEE helps manufacturers identify and eliminate waste in production, resulting in better cost control and improved profitability.
Automate data collection
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. 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.
Information on the shopfloor
Using production monitoring dashboards on the shop floor can help operators to understand complex data, as operators are able to retrieve detailed information on stop causes, and increase productivity. Factbird also help 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.
Healthy competition between shifts is also common when production data is available on shopfloors, driving motivation and improving OEE.
Work on the six big losses
Another strategy you can use to improve OEE manufacturing metrics is to work on the Six Big Losses:
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
You can read more about OEE calculation and the six big losses here: How to calculate OEE.
Team roles in OEE monitoring
Although we can monitor OEE via software, it's 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 through reporting.
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.
Operators are the ones that observe and work on stop causes, but they also can report opportunities for improvement through factual information.
Maintenance specialists retrieve the historical data from manufacturing monitoring software and apply the required maintenance tasks per equipment. They also must report the eventual need for scheduled shutdowns for maintenance tasks. Here's a walkthrough on how maintenance managers streamline processes in Factbird:
OEE Improvement Case Studies
Here are some examples of Factbird customer success stories to help inspire your OEE improvement efforts.
How Danfoss improved production by 40%
Danfoss, a prominent global manufacturer with 95 facilities, sought to enhance manufacturing efficiency through real-time data aggregation. Motivated by rising energy costs and a desire to identify the root causes of unplanned downtime, they partnered with Factbird in 2021 for a pilot program.
Within three days, sensors and cameras were installed on three machines, making data available 24/7 across devices. This pilot revealed crucial insights into unplanned stoppages and machine downtime, prompting a broader implementation across facilities.
Danfoss noted that the system pinpointed issues causing hours-long stops, translating to significant production losses. The sensors enhanced visibility into manufacturing processes and energy measurement, leading to significant consumption reduction. Factbird's technology streamlined troubleshooting, with logs detailing machine stops, causes, and locations. It also identified a potential 77% reduction in standby energy consumption.
We had a lot of data. It is a big company, but it was a kind of hidden knowledge before the digitalization using Factbird. Now with the information and data reports in front of every people, we have information to react on.
Martin Ole Madsen, Manager - Operations Excellence SVS EUR at Danfoss Power Solutions.
Within a year, Danfoss reported a 40% increase in production output and 20% fewer production stops, attributable to Factbird's efficient tracking and analysis. This partnership showcased the transformative power of data-driven decision-making in manufacturing optimization.
How Autins improved OEE by 20%
Autins, a global leader in acoustic and thermal insulation solutions, faced a challenge in March 2020 when Covid-19 hit and demand dropped.
They urgently sought a solution to understand and optimize production efficiency in light of rising labor costs. Opting for Factbird's plug-and-play system, Autins aimed to comprehend current efficiency, boost productivity, attain real-time visual management, and discern machine stoppage reasons.
Post-installation, Factbird revealed precise output metrics and machine stoppage reasons, providing clarity where none existed before. This insight shifted the shop floor culture; instead of pushing for higher production, the emphasis was on stable output. Operators became more collaborative, focusing on consistent performance and proactive improvements like material changeover.
When we started to use the Factbird solutions, we in July had an OEE of 54 %. In October we had improved the OEE to 81%.
Miguel, Operation Manager at Autins
With Factbird, Overall Equipment Effectiveness (OEE) soared from 54% to 81%. The enhanced efficiency meant production, previously running in three shifts, now operated in just two, eliminating the costly night shift. Factbird's data analytics not only guides further on-floor improvements but also offers other departments, like sales, reliable manufacturing cost data.
Get in touch for expert advice
OEE is a powerful tool for identifying improvement areas and facilatating changes in manufacturing processes. If you would like to get expert advice on how to improve your OEE metrics, get in touch with the Factbird team for a no-obligations chat.
Watch a demo Factbird's and see how you can improve OEE in a matter of days.