Measuring optimum performance in the manufacturing sector is no mean feat. Herein, tracking individual KPIs can prove moot in light of the complexity of the processes involved. That’s why Overall Equipment Effectiveness (developed by Seiichi Nakajima: founder of the Total Productive Maintenance system) came into being. It revolutionised the monitoring of machine effectiveness by clubbing three KPIs (Performance, Quality, and Availability) together.
What Is OEE?
In the paragraph above, we’ve already talked about what OEE stands for. Now, let’s delve into its definition and practical underpinnings. On the surface, OEE means the percentage of production time that’s demonstrably productive.
In manufacturing verbiage, a 100 percent OEE score constitutes three key insights:
• You’re manufacturing good parts across the board.
• The pace of manufacturing is as fast as it could be.
• The stop time (the time when the machine is not in production: downtime) is zero.
Now, these three insights pave the way for a neat framework that forms the mathematical basis of calculating OEE. Like we mentioned before, OEE is a compound KPI that accounts for three KPIs at once: Performance, Quality, and Availability. Mathematically, we calculate it as follows:
OEE = A X P X Q
{A (Availability) = Run Time/Planned Production Time;
Run Time = Planned Production Time - Stop Time;
P (Performance) = (Ideal Cycle Time x Total Count)/Run Time;
Q (Quality) = Good Count/Total Count}
Now, OEE has established itself as the gold standard in machine effectiveness because of its practical utility. It illuminates holes in the manufacturing process and helps mitigate underlying losses by delivering valuable insights. Until recently, tracking OEE was thought of as a task allocated to the end of the production cycle. However, with advancements in IoT and the emergence of Industry 4.0, monitoring OEE has become a real-time pursuit.
Here are some reasons why that’s a revolutionary development:
Predictive Analytics Minimise Planned And Unplanned Downtime
When working with intricate machinery, the risk of equipment or parts failure is ever-present. Unplanned downtime of this sort lasts the longest and thereby proves hyper-expensive. Before the age of IoT, equipment monitoring required scheduled maintenance checks (which disrupted operations). However, machine learning algorithms can help manufacturers attain cognisance about patterns that usually spawn before failures. This can have a tremendously positive effect on reducing Mean Time to Repair and soften the blow incurred by downtime. Even regular maintenance gets streamlined with IoT sensors. They help increase asset visibility and allow manufacturers to schedule repairs in tandem with equipment conditions. In the past, manufacturers only had the option to impose guestimate-driven planned downtime.
When working with intricate machinery, the risk of equipment or parts failure is ever-present. Unplanned downtime of this sort lasts the longest and thereby proves hyper-expensive
Real-Time Sensors Help Reduce Minor Stops And Speed Inefficiencies
Inefficiencies in the manufacturing process extend beyond big issues that demand downtime. Minor stops affect performance and thereby contribute to a lower OEE score. Thanks to real-time IoT sensors, dealing with minor stops and speed inefficiencies can be more effective than ever. Moreover, manufacturers forget that minor stops can pile up and give rise to alarming failures. Herein, IoT can help shine the light on recurring issues. Then, manufacturers can exercise positive intervention before issues snowball. All in all, IoT helps keep real-time tabs on performance and thus contributes to a higher OEE score.
IoT Delivers Insights Valuable For Overall Process Improvement
So far, we’ve discussed how IoT can help manufacturers improve OEE by making tweaks in the domains of performance and availability. Now, let’s dive into how IoT tech can contribute to improved manufacturing quality. In industrial settings, even minor changes like temperature variance and humidity levels can affect production standards. Thanks to environmental sensors and real-time equipment monitoring, manufacturers can drill down root causes of quality degradation over time. Quite obviously, the data analysis capabilities made possible by IoT will make hefty positive contributions here.
Conclusion
With Industry 4.0 set to surpass the 300 billion mark by 2023, IoT and OEE will remain deeply tangled for years to come. After all, the ever-evolving capabilities that the amalgamation of MI, AI, and IoT bring to the table are unparalleled. To close off, we wouldn’t be surprised if a 100 percent OEE score becomes the industry standard in a decade or so. That’s what we’re hoping for anyway.
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