When the same number pops up across very different industries, it's attention-grabbing. And the number (and phrase) that got my attention recently was this: "We reduced downtime by 40%." You don't see that every day. And how are industries getting there from here? They're implementing predictive maintenance, trying to avoid unplanned downtime through advanced AI strategies.
Not only have all these businesses significantly reduced downtime, but also when they focused on predictive maintenance productivity increased by roughly 20%, again regardless of industry. Impressive.
Question: How costly is unplanned downtime in a mill? That's a number I've never heard (I was only ever told, "a LOT") although there are certainly more than a few people who have the current dollar figure. (For the oil and gas industry, unplanned downtime costs up to $500,000 per hour.)
So, what can you use predictive maintenance for? Use it for anything at all that needs monitoring, utilizing the thousands of sensors in your processes. Here are some common things tracked:
- Motors
- Engines
- Wear patterns
- Leak detections
- Emission points
- Fuel efficiency fluctuations
And much, much more.
Basically, if you're monitoring something, you can use an advanced AI program to learn and predict when it'll fail, so you can repair or replace the equipment before an unexpected repair is urgently required. The goal is to eliminate surprises.
Not only that, but a problem detected in one piece of equipment, let's say it's a pump, can predict failures with other pumps of the same model. (Makes sense.) This (alone) has huge ramifications and benefits.
In addition, you can have specialists come in and do predictive maintenance inspections also - the old-fashioned way, in person, by using equipment such as infrared thermography, sound and vibration monitors, and more.
Then the AI program will take the damage, the successes, the repairs, and even the not-needed repairs, feed it all into the algorithm, and learn and improve on it, thus creating a steadily improving feedback loop. Thus the program gets better and better at predicting your maintenance needs.
What are the results? Try this on for size:
- Maintenance costs down 35%
- Equipment lifespan extended 20%
- Fuel efficiency for trucks increased by 15%
- Sensor costs reduced (% varies)
And in one specific case:
- Leaks to ecosystems prevented
- EPA fines eliminated due to 24/7 monitoring
Staggeringly impressive for focusing on a single area such as predictive maintenance.
So managers: what's your biggest maintenance challenge that you think predictive maintenance could solve? And if you're already using predictive maintenance, what's been your experience? Let Nip Impressions know. |
