OTE, not OEE: Addressing OEE’s Shortcomings
Updated: Jun 17
As an OEM, how great does Overall Equipment Effectiveness (OEE) sound? It’s a single, comprehensive metric telling you how well your manufacturing is performing. It’s tempting to use OEE as a holistic score for “grading” production facilities, for measuring the effectiveness of improvement initiatives and comparing suppliers.
But this has its shortcomings. First, OEE wasn’t meant to be used as an aggregate plant or supplier score -- OEE reflects the performance of individual assets. Second, on its own, OEE doesn’t tell you enough about an asset. To do that, the component factors of OEE need to be looked at in-depth. And finally, OEE requires data and analytics from different sources. The real challenge is to track down and monitor this data -- preferably in real-time.
By monitoring toolings rather than equipment, the eMoldino solution gives its own take on OEE -- or as we like to call it internally, “Overall Tooling Efficiency” (OTE).
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On its analytics platform, eMoldino crunches each tooling’s shot count, cycle time, uptime, and temperature data to tell you:
Availability: how long your tooling downtimes (and by extension, your production downtimes) were compared to scheduled uptime.
Performance: how fast your toolings were producing.
Quality: the percentage of good parts out of all the parts produced.
The great thing about eMoldino is that the platform provides OTE for each tooling. Our software platform crunches data from each tooling attached with our IoT sensors, which lets it neatly compartmentalize OTE on a per-tooling basis. The neat thing about this is that end-users can break the OTE score down to its component factors on the system. That way, OEMs and factory personnel can pinpoint exactly the areas needing improvement. And since the solution is an all-in-one IoT solution, there’s no need to coordinate with other sources of data -- all the data needed to create OTE is provided by our sensors.
How Do We Do OTE?
Each of the scores making up OEE can be calculated on our system through the data tracked by our sensors.
:: Availability ::
On the software platform, a tooling’s uptime and downtime can be compared to its scheduled uptime. The system takes into account planned downtime like preventive maintenance, as the system itself knows when maintenance should be scheduled based on the count of shots. Unplanned downtime will be clearly marked as areas of improvement. Over time, as these mistakes are recorded, AI and machine learning algorithms can be leveraged to find out the variables affecting Availability and forecast the next time an unplanned stop might occur.
:: Performance ::
The actual cycle time monitored on a tooling can be compared to the tooling’s designed or standard cycle time. This figure is then represented as a percentage. This percentage could exceed one hundred percent, but overly fast production speeds could adversely affect the quality score.
:: Quality ::
Scrapped and faulty parts recorded by on-site personnel are inputted into the system, where this is compared to the total number of parts produced. With AI and machine learning applications, the system can, over time, learn how to predict part quality with differing cycle times, temperature, and tooling pressure levels.
On our system, each of these were designed to be their own self-contained KPIs. Putting them all together to create our version of OEE -- OTE -- is simplicity itself, and is highly useful for companies looking for a quick, general metric for manufacturing performance.
Take a look at our solution by arranging a meeting online. Ask to see how our solution works, and discuss the metrics you would like to see.