Predictive maintenance

So a spike in electricity or, or a, a, uh, a valley, and then that allows you to maintain your, your building and, uh, and the equipment.

So it's predict- predictive maintenance.

Yeah.

I was in my car and I got a ding on my phone.

Yeah, thanks, David.

So SMAI offers really off-the-shelf solutions for process optimization across the entire plant and predictive maintenance from the quarry to the loadout.

We are and have been from the beginning cement focused.

We are born, bred, raised through the cement industry.

There are solutions that have been proven out over from our perspective.

We've got 40 plus process optimizations going on across assets around the globe.

You know, we're in 22 cement plants running predictive maintenance.

These are already proven assets and proven results.

But overall, these are some of the real world examples that we have come across.

In our prep call, another thing that we talked about was this notion of predictive maintenance as opposed to responding to events.

Abstra and Mist working together can help maybe get you ahead of potential problems.

Can you talk about how, give some examples and how the system is actually able to do that?

So these are the things that we want to, and it's fixed in particular release.

We'll give you a fixed recommendation, so you can go ahead and apply those fixes.

So there is a link between what predictive maintenance does and the recommendation engine we talked about early on.

So we've been talking and sort of the examples we've been given, you know, the Apstra and Mist can help a network engineer who's coming into a situation have better context around what happened and maybe what to do next, maybe even recommendations, you know, from the system on next steps.

So you try and engineer it out.

But then you get down to sort of like drive-wise, it's predictive maintenance stuff.

I think the T, you know, I think we talk about a lot having sort of predictive maintenance capabilities and it's huge.

Yeah, we have like 755T for our listeners.

Yeah.

Yeah,

I was going to say, if you're looking at the analysis of vibrations or any sort of mechanical fuzziness, Guardian AI seems like a perfect way to do that, especially on something so critical where you can really get it set up just right.

I think it's just layers, you know what I mean? So like you got predictive maintenance in the drive, tick.

So those are really important options there.

And Aside from that, really leaning into the information that you have available because if you leverage predictive maintenance technology, you're able to anticipate breakdowns before they happen.

And I think that's something that a lot of maintenance managers should really be thinking about.

It matters if a piece of equipment goes down right now.

I need to find the part.

I need to get everything together, the equipment for the install.

What else is it going to take? Versus if you know, using predictive maintenance technology like vibration or temperature sensors and ultrasonic sensors, you know, you're starting to have a failure.

You can schedule it.

We are jumping right into the trenches of industrial reliability looking at the article.

Predictive maintenance starts at the sensor layer.

Our mission today is basically threefold.

Right.

The big three.

We want to help you decide between preventive and predictive maintenance, figure out how to mathematically identify your absolute worst performing assets, and then learn how to play detective by mapping specific failure modes to the right sensor technology.

It's a huge paradigm shift.

I mean, for decades, the entire manufacturing world basically operated on the equivalent of that little windshield sticker.

Exactly.

It's called predictive maintenance, and it's all about using AI to analyze data from sensors that are already commonly used in factories.

So you're talking about temperature vibration, even the sounds a machine makes.

So AI is basically listening to the machines, figuring out if something's off before humans can even hear it.

That makes a lot of sense.

Yeah.

Okay, so we've talked about predictive maintenance.

What are some other ways AI is transforming manufacturing?

Yeah.

I mean, it's interesting also because predictive maintenance is something everyone wants.

It just intuitively makes so much sense.

And yes, we've seen good success stories there.

Wow.

Staggering breaks, starting shifts on time, and catching a few of the little jams and microstops that they wouldn't have otherwise caught.

And so we usually try to focus people on what is your goal? If you as the business leader, and picturing Dave, the owner walking around with AirPods in, if you as the business leader want as your goal to be making more money, You might hit that goal faster by getting a sense of what's losing you money today and then attacking the top cause, even if it's unsexy, even if it's something like, let me put up a simple scorecard and see my team compete to win the hour, win the shift, win the day in safe ways with the right incentives, rather than the obvious and fancy and wonderful predictive maintenance strategy.

thing that makes so much intuitive sense but might actually be icing on the cake.

Which brings us to the third signal in the text.

Vending and water operators are prioritizing connectivity, predictive maintenance and audit ready service data.

Yes.

And this really ties the whole macro trend together for me.

You are solving the customer's problem, which is low employee engagement with an expensive corporate perk before they even realize it's a problem.

That brings us to the second metric, the brief outlines.

Predictive maintenance triggers.

And this loops right back to our discussion about the financial risks of an SLA.