That Day OEE Dropped to 45%, and the Boss's Face Turned Green
That afternoon, the production line's alarms rang incessantly. I sat in my office, watching the OEE number on the war room's large screen plummet from 70% all the way down. When it finally stopped at 45%, the entire meeting room fell silent for three seconds. The boss's face was paler than a cleanroom suit. He just said, "Find out exactly where the problem is!" To be honest, this kind of situation is nothing new to us veterans, but how do you pinpoint the problem? You can't just rely on instinct. Many times, with a number like OEE, if you just look at the total, you have no idea where to start.
When OEE Drops, Who's Responsible?
To put it simply, OEE (Overall Equipment Effectiveness) is a metric that measures equipment capacity utilization, aiming to maximize "available time." But it's not just a simple number; behind it lie the Six Big Losses. Frankly, many newcomers only know that OEE should be high but don't understand where the problems actually come from. It's like seeing a broken-down car – you know it's broken, but you don't know if it's the engine, the tires, or the fuel tank. These Six Big Losses help you "categorize" the problems, allowing you to clearly know which department to contact.
So the key is that these Six Big Losses are divided into three main categories: Availability, Performance Efficiency, and Quality Rate.
- Availability Losses: This part is the easiest to understand – the machine could have been running but wasn't.
* Setup and Adjustment Loss: When changing product models, the equipment needs parameter re-setting and calibration. The time the equipment cannot produce during this period also counts.
- Performance Efficiency Losses: The machine is running, but not at its optimal efficiency.
* Reduced Speed Loss: The equipment is not running at its optimal design speed. This could be to avoid defects, due to equipment aging, or poor parameter settings. Previously, one of our machines had a normal throughput of 100 pieces/hour, but it only ran at 85 pieces/hour – that's reduced speed.
- Quality Losses: The machine ran, but the output products are unusable.
* Startup Rejection Loss: After equipment startup or adjustment, the first few products cannot be counted as good products due to unstable parameters or testing requirements. Many times, when you start a machine, the first 5 pieces are directly discarded.
How Is It Actually Done?
To calculate these figures, you first need data. We typically categorize the equipment's operational states and then compile statistics for each category's time.
For example, a piece of equipment operates 8 hours a day (480 minutes).
- Planned Production Time: 480 minutes.
- Downtime: 30 minutes for malfunction, 20 minutes for mold change. So actual operating time = 480 - 30 - 20 = 430 minutes.
- Idling/Minor Stop Time: The machine idled for 10 minutes waiting for materials.
- Reduced Speed Loss: Assume a normal output of 20 pieces per minute, but for some reason, it actually produced only 18 pieces per minute.
- Number of Defective Products: A total of 8000 pieces were produced, of which 400 were scrap.
By calculating these, you can identify which type of loss accounts for the largest proportion. So, if you see OEE drop, the first thing to do is differentiate whether the problem lies in Availability, Performance Efficiency, or Quality. If most of it is downtime loss, then the focus shifts to equipment PM and maintenance; if it's reduced speed, then process parameters or equipment health need to be examined.
The Most Common Pitfalls
The most common pitfall we encounter is inaccurate data. Many people, to make reports look good, ignore brief downtimes or categorize reduced speed as "new product process limitations." Last time, a newcomer directly attributed the "setup and adjustment" time for changing wafer sizes to "PM maintenance," resulting in the PM department getting severely reprimanded. It turned out that the equipment wasn't broken at all; he had simply messed up the setup procedure. Frankly, if the data is inaccurate, any analysis you do is pointless.
Another pitfall is looking only at the total. If OEE drops to 50%, just saying "OEE is bad" is useless. You absolutely must know which of the Six Big Losses is the worst bottleneck. Once, our OEE was very low, and everyone was busy fixing machines. Later, we discovered that the yield was simply too poor; fixing machines was futile, as they were still producing waste. We were completely headed in the wrong direction.
One Thing You Can Do Today
Go back and look at your OEE data. Try categorizing downtime, and see which item accounts for the largest proportion.