WEBVTT
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Hello and welcome to Full Tech Ahead.
00:00:22.160 --> 00:00:23.679
I'm your host, Amanda Rizzani.
00:00:23.920 --> 00:00:26.079
And with me today, I have Israel Ortiz.
00:00:26.160 --> 00:00:28.879
He's the VP and General Manager at Accruant.
00:00:28.960 --> 00:00:30.079
How are you doing today?
00:00:30.399 --> 00:00:31.359
I'm doing great, Amanda.
00:00:31.440 --> 00:00:31.679
Thank you.
00:00:31.760 --> 00:00:32.560
How about yourself?
00:00:32.880 --> 00:00:33.759
I'm doing well.
00:00:34.000 --> 00:00:35.520
Good start to the week.
00:00:35.759 --> 00:00:36.479
Yeah, that's right.
00:00:36.640 --> 00:00:38.320
It's a Monday, but let's get to it.
00:00:38.560 --> 00:00:39.200
Yes.
00:00:39.840 --> 00:00:40.240
All right.
00:00:40.320 --> 00:00:43.200
Well, can you share a little bit about uh your company?
00:00:43.359 --> 00:00:44.799
What services do you provide?
00:00:45.280 --> 00:00:46.560
Oh, yes, accruant.
00:00:46.640 --> 00:00:49.359
Um, it really helps shape the world and how it works.
00:00:49.520 --> 00:00:56.960
And we operate in facility and asset management and helping improve efficiency and uptime for our customers uh through a series of products.
00:00:57.119 --> 00:00:58.960
So wonderful.
00:00:59.280 --> 00:01:10.159
Well, we're gonna talk about manufacturing teams and the growing retiring workforce and how we can tackle that with AI tools.
00:01:10.480 --> 00:01:19.200
So, my first question for you is how big a challenge is the retiring workforce right now in the manufacturing industry?
00:01:20.000 --> 00:01:25.280
Uh, retiring workforce is something, it's an inevitable thing that is happening across.
00:01:25.359 --> 00:01:30.400
Uh, and I was just visiting customers uh two weeks ago where they're having this exact problem.
00:01:30.799 --> 00:01:39.359
Uh they're the the great experience, 20, 30 year, you know, under their belt uh workforce is deciding to move on to a better life.
00:01:39.439 --> 00:01:45.599
And uh and that's leaving behind the novice, the newer folks, uh, to try to figure out how to close that gap as soon as possible.
00:01:45.680 --> 00:01:48.239
So it's very much a real problem out there.
00:01:48.560 --> 00:01:52.799
So you're seeing a real gap uh and a need for to bring on new talent?
00:01:53.200 --> 00:01:54.319
Oh, yes, they are.
00:01:54.400 --> 00:01:57.120
Uh a lot of folks are trying to figure out how to do trading better.
00:01:57.280 --> 00:02:04.799
And, you know, being in the software space, a lot of what I hear from our customers is how can technology help solve the experience gap that I have?
00:02:04.959 --> 00:02:08.719
And so this is very much um something that everyone's trying to solve for.
00:02:09.120 --> 00:02:09.599
Got it.
00:02:09.759 --> 00:02:19.599
So then here comes AI, and everybody's looking at how they can harness these AI tools, and it has advanced quite rapidly over the last few years.
00:02:19.919 --> 00:02:25.680
So, how is the new version of AI offering assistance in this area?
00:02:25.759 --> 00:02:29.520
And how do you advise business leaders to use it?
00:02:30.560 --> 00:02:32.960
Well, here's where the world is at right now.
00:02:33.120 --> 00:02:35.199
Everyone is trying to mine their data.
00:02:35.280 --> 00:02:41.759
And so there's been a lot of good history out there in particular the asset management tools.
00:02:41.840 --> 00:02:45.599
And so now the challenge is how do I go and tap that information?
00:02:45.680 --> 00:02:55.680
And so, what a lot of our customers are trying to do is uh de-duplicate information, clean the information, grind get the right pattern so that AI can then tell them a couple of things.
00:02:55.840 --> 00:02:58.560
Uh, number one is what should I be doing with that asset?
00:02:58.639 --> 00:03:05.280
So making that decision of I'm getting some signals from different sorts of IoT devices, what does that really mean?
00:03:05.360 --> 00:03:09.360
So the history within the asset management tools will tell you that.
00:03:09.520 --> 00:03:17.599
And then uh going back to the experience gap, as the newer technicians go out and try to fix something, what should they actually be doing?
00:03:17.759 --> 00:03:21.520
Um, and so uh AI can help give that information to them.
00:03:21.680 --> 00:03:27.199
And so this is how AI is is being leveraged and starting to evolve uh to help replace that gap.
00:03:27.680 --> 00:03:37.759
Can you share how that is helping with the AI being connected to the Internet of Things and also some of the risks with that as well?
00:03:38.560 --> 00:03:39.680
Okay, yeah, great.
00:03:39.919 --> 00:03:51.439
So the smartest and the greatest uh technicians out there, I was actually joking with one of them, and uh, and they always say, Oh, I just have to put my hand on that, feel the vibration, and I can tell what I need to do there, right?
00:03:51.599 --> 00:03:56.639
Well, you take someone who's brand new, there's no way they know if it's even vibrating at that at that moment.
00:03:56.800 --> 00:04:00.400
Uh and so what does uh the connected you know uh IoT do?
00:04:00.560 --> 00:04:07.439
Uh well, there's a lot of companies out there that are helping understand vibration, temperature, you know, motor decrease, et cetera, power decrease.
00:04:07.599 --> 00:04:10.400
And all those are just signals.
00:04:10.879 --> 00:04:12.000
What does that mean?
00:04:12.159 --> 00:04:18.319
And so uh what AI can start helping do is understand what those signals could mean.
00:04:18.399 --> 00:04:24.480
Uh and so you're really starting to see, you know, that predictive maintenance get to the place where people want it.
00:04:24.639 --> 00:04:27.920
The biggest drawback here is to blindly trust it.
00:04:28.079 --> 00:04:31.920
And so then one thing is um it's getting really good.
00:04:32.240 --> 00:04:34.800
Everyone should definitely start investing in this way.
00:04:34.959 --> 00:04:42.879
However, we should continue to use the folks who are still with us, uh, still have the experience to pressure test and ensure that we got the right thing.
00:04:43.040 --> 00:04:57.839
So, what it is, what what I recommend for folks out there is is when you get something, take it seriously, understand it, but then go and understand that information, go and do whatever validation you need to, and then go confirm uh that what you are seeing through AI is indeed valid.
00:04:57.920 --> 00:05:00.879
Uh, it's a lot of, you know, the way we would use it to write something today.
00:05:00.959 --> 00:05:05.519
We would, we would, we would ask the prompt to help us think about how to how to be creative.
00:05:05.600 --> 00:05:09.120
And then we would say, okay, now I'm gonna read it and make sure it's what I need it to be.
00:05:09.199 --> 00:05:11.920
Uh similar to when you're doing reliability maintenance.
00:05:12.000 --> 00:05:16.319
Uh, we need to leverage AI uh to make sure that we're getting what we want out of it.
00:05:16.399 --> 00:05:17.920
So that's that's my advice.
00:05:18.240 --> 00:05:18.560
Okay.
00:05:18.879 --> 00:05:23.120
All the legacy technology that manufacturers are having to deal with right now.
00:05:23.279 --> 00:05:26.800
How are they moving to this new A, the new AI tools?
00:05:27.199 --> 00:05:42.079
Technology is going to be a slower, I mean, the these assets, these, these, these huge production lines that our food ab or any manufacturing is dealing with, they're starting to slowly start bringing in uh these manufacturers that will have IoT devices installed in them.
00:05:42.160 --> 00:05:43.920
And they'll come out of it this way.
00:05:44.079 --> 00:05:51.199
Uh, and so uh in one of the tours I was doing, they said, hey, let's go to our smart manufacturing and let's not let's not go to this way, right?
00:05:51.279 --> 00:05:59.040
So the the legacy ones will continue to need those inspections, we'll continue to need you know the monthly rounding, et cetera.
00:05:59.199 --> 00:06:11.279
Uh, but as uh items are being replaced, as you know, 20-year-old pieces of equipment uh come around, those manufacturers will start to embed that intelligent uh assets within them.
00:06:11.439 --> 00:06:18.560
And so then my recommendation is to start investing in those and ensuring that you're slowly building a modern manufacturing plant for yourself.
00:06:19.360 --> 00:06:22.720
Well, AI tools are advancing rapidly, as we mentioned.
00:06:22.959 --> 00:06:31.600
What do you envision as the future of manufacturing and how can business leaders prepare and harness these tools?
00:06:32.480 --> 00:06:37.360
Yeah, well, the future is slowly um being shown to us in different ways.
00:06:37.600 --> 00:06:41.120
And um uh, you know, take those same plants, for example.
00:06:41.360 --> 00:06:46.240
One of them needed a lot of human intervention uh within those manufacturing sites.
00:06:46.399 --> 00:06:50.399
The other ones was a lot of automation already there.
00:06:50.720 --> 00:06:56.240
And what the future will be is us supervising that automation.
00:06:56.480 --> 00:07:05.120
Again, always having that human element in it because we're gonna get smarter about the way we uh we operate versus be completely replaced.
00:07:05.199 --> 00:07:14.240
And and so then the the future will be um let's let uh technology work for us, but let's always stay in the loop to ensure it's it's producing what we want.
00:07:14.319 --> 00:07:21.519
And so we're more uh in the in that control room, ensuring things are there so that when something pops up, we can then go intervene.
00:07:21.680 --> 00:07:26.480
We can go, we can then go make sure that uh asset doesn't stay downtime too long.
00:07:26.639 --> 00:07:29.439
Uh and then your second question is uh advice, right?
00:07:29.519 --> 00:07:31.120
That I would that I would give here.
00:07:31.199 --> 00:07:37.199
Uh and my advice is that is this is is to hey, you've got a rich set of data within you.
00:07:37.439 --> 00:07:40.959
Start ensuring that you're capturing all your information.
00:07:41.040 --> 00:07:57.519
And so those work orders that you're closing out are it's much more important that you take those notes, that you annotate what happened, that you get that problem cost and remedy in there, uh, and then clean that data and bring it into your data lakes because that information will be what will help you respond in the future much faster.
00:07:57.759 --> 00:08:06.639
So uh I just I just say now more than ever, ensure that uh you're you're getting that information within there, and then you can apply AI to get what you need from it.
00:08:07.759 --> 00:08:14.800
Well, from your experience, do you have a particular use case story that you can share about a company?
00:08:14.959 --> 00:08:25.920
Maybe you don't uh name the company, but is there um a transformation story you can share when they um harnessed the AI technology and IoT and they saw a great result?
00:08:27.040 --> 00:08:30.480
Yeah, I could think of uh automotive uh manufacturing.
00:08:30.560 --> 00:08:35.279
Uh, and of course, there's there's plenty in different industries, but we we we dealt with one of those.
00:08:35.440 --> 00:08:39.919
And they definitely uh saw a huge increase in productivity.
00:08:40.080 --> 00:08:48.159
Uh, but again, it's the reason why I think about the human in the loop is that this uh IoT device got them to the predictive maintenance that they wanted.
00:08:48.320 --> 00:08:58.159
So they were able to reduce their there's reactive and then this they were able to produce their preventive maintenance uh 30% uh because they were able to look at their predictive maintenance.
00:08:58.240 --> 00:09:04.080
And then they had every Tuesday, and this is advice for everyone, every Tuesday they would just look at that telemetry data.
00:09:04.240 --> 00:09:12.240
They would look at what the system was telling them to go do, uh, and then they would uh take out things that they knew weren't an issue and then focus on the things that were.
00:09:12.399 --> 00:09:14.960
And so they were able to see improve their uptime because of that.
00:09:15.200 --> 00:09:17.120
So uh this is why I'm a big believer.
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I was able to see it firsthand and how technology was being used uh for them to get the efficiency that they wanted.
00:09:23.200 --> 00:09:24.080
Wonderful.
00:09:24.320 --> 00:09:29.519
Well, if there was one key takeaway you could share with our audience today, what would that be?
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I said don't wait and start now.
00:09:32.480 --> 00:09:34.240
Uh, start investing in the small ways.
00:09:34.320 --> 00:09:36.320
Like I said, start investing in your information.
00:09:36.559 --> 00:09:46.159
Start investing, making sure you're getting that intelligence out there because when that experienced workforce leaves, all that information will be in your asset management system.
00:09:46.399 --> 00:09:55.120
Then you can plug in all the IoT devices uh and have that harmony that you want to have so that the next person that's more of a novice will have the right information.
00:09:55.279 --> 00:10:00.320
Imagine a world where um all they have to do is scan the asset in front of them.
00:10:00.480 --> 00:10:03.039
And this system can tell them, oh, great.
00:10:03.120 --> 00:10:04.960
Well, here's the history behind that asset.
00:10:05.039 --> 00:10:10.879
Here's what telemetry did is telling me I recommend you do X, Y, and Z and with these sort of tool sets.
00:10:10.960 --> 00:10:16.320
And so the more you start investing in your information now, the more it'll play dividends in the future.
00:10:16.720 --> 00:10:17.360
Awesome.
00:10:17.519 --> 00:10:20.480
Thank you so much for coming on the show and sharing your insights today.
00:10:20.639 --> 00:10:22.080
Of course, Amanda, thank you for having me.
00:10:22.159 --> 00:10:23.279
I'm excited about this.
00:10:23.440 --> 00:10:24.879
Yes, and thank you to our audience.
00:10:24.960 --> 00:10:28.799
If you have any questions or comments, link them below and I'll respond back as soon as possible.
00:10:28.960 --> 00:10:31.279
And meanwhile, have a wonderful week.
00:00:19.600 --> 00:00:22.079
Hello and welcome to Full Tech Ahead.
00:00:22.160 --> 00:00:23.679
I'm your host, Amanda Rizzani.
00:00:23.920 --> 00:00:26.079
And with me today, I have Israel Ortiz.
00:00:26.160 --> 00:00:28.879
He's the VP and General Manager at Accruant.
00:00:28.960 --> 00:00:30.079
How are you doing today?
00:00:30.399 --> 00:00:31.359
I'm doing great, Amanda.
00:00:31.440 --> 00:00:31.679
Thank you.
00:00:31.760 --> 00:00:32.560
How about yourself?
00:00:32.880 --> 00:00:33.759
I'm doing well.
00:00:34.000 --> 00:00:35.520
Good start to the week.
00:00:35.759 --> 00:00:36.479
Yeah, that's right.
00:00:36.640 --> 00:00:38.320
It's a Monday, but let's get to it.
00:00:38.560 --> 00:00:39.200
Yes.
00:00:39.840 --> 00:00:40.240
All right.
00:00:40.320 --> 00:00:43.200
Well, can you share a little bit about uh your company?
00:00:43.359 --> 00:00:44.799
What services do you provide?
00:00:45.280 --> 00:00:46.560
Oh, yes, accruant.
00:00:46.640 --> 00:00:49.359
Um, it really helps shape the world and how it works.
00:00:49.520 --> 00:00:56.960
And we operate in facility and asset management and helping improve efficiency and uptime for our customers uh through a series of products.
00:00:57.119 --> 00:00:58.960
So wonderful.
00:00:59.280 --> 00:01:10.159
Well, we're gonna talk about manufacturing teams and the growing retiring workforce and how we can tackle that with AI tools.
00:01:10.480 --> 00:01:19.200
So, my first question for you is how big a challenge is the retiring workforce right now in the manufacturing industry?
00:01:20.000 --> 00:01:25.280
Uh, retiring workforce is something, it's an inevitable thing that is happening across.
00:01:25.359 --> 00:01:30.400
Uh, and I was just visiting customers uh two weeks ago where they're having this exact problem.
00:01:30.799 --> 00:01:39.359
Uh they're the the great experience, 20, 30 year, you know, under their belt uh workforce is deciding to move on to a better life.
00:01:39.439 --> 00:01:45.599
And uh and that's leaving behind the novice, the newer folks, uh, to try to figure out how to close that gap as soon as possible.
00:01:45.680 --> 00:01:48.239
So it's very much a real problem out there.
00:01:48.560 --> 00:01:52.799
So you're seeing a real gap uh and a need for to bring on new talent?
00:01:53.200 --> 00:01:54.319
Oh, yes, they are.
00:01:54.400 --> 00:01:57.120
Uh a lot of folks are trying to figure out how to do trading better.
00:01:57.280 --> 00:02:04.799
And, you know, being in the software space, a lot of what I hear from our customers is how can technology help solve the experience gap that I have?
00:02:04.959 --> 00:02:08.719
And so this is very much um something that everyone's trying to solve for.
00:02:09.120 --> 00:02:09.599
Got it.
00:02:09.759 --> 00:02:19.599
So then here comes AI, and everybody's looking at how they can harness these AI tools, and it has advanced quite rapidly over the last few years.
00:02:19.919 --> 00:02:25.680
So, how is the new version of AI offering assistance in this area?
00:02:25.759 --> 00:02:29.520
And how do you advise business leaders to use it?
00:02:30.560 --> 00:02:32.960
Well, here's where the world is at right now.
00:02:33.120 --> 00:02:35.199
Everyone is trying to mine their data.
00:02:35.280 --> 00:02:41.759
And so there's been a lot of good history out there in particular the asset management tools.
00:02:41.840 --> 00:02:45.599
And so now the challenge is how do I go and tap that information?
00:02:45.680 --> 00:02:55.680
And so, what a lot of our customers are trying to do is uh de-duplicate information, clean the information, grind get the right pattern so that AI can then tell them a couple of things.
00:02:55.840 --> 00:02:58.560
Uh, number one is what should I be doing with that asset?
00:02:58.639 --> 00:03:05.280
So making that decision of I'm getting some signals from different sorts of IoT devices, what does that really mean?
00:03:05.360 --> 00:03:09.360
So the history within the asset management tools will tell you that.
00:03:09.520 --> 00:03:17.599
And then uh going back to the experience gap, as the newer technicians go out and try to fix something, what should they actually be doing?
00:03:17.759 --> 00:03:21.520
Um, and so uh AI can help give that information to them.
00:03:21.680 --> 00:03:27.199
And so this is how AI is is being leveraged and starting to evolve uh to help replace that gap.
00:03:27.680 --> 00:03:37.759
Can you share how that is helping with the AI being connected to the Internet of Things and also some of the risks with that as well?
00:03:38.560 --> 00:03:39.680
Okay, yeah, great.
00:03:39.919 --> 00:03:51.439
So the smartest and the greatest uh technicians out there, I was actually joking with one of them, and uh, and they always say, Oh, I just have to put my hand on that, feel the vibration, and I can tell what I need to do there, right?
00:03:51.599 --> 00:03:56.639
Well, you take someone who's brand new, there's no way they know if it's even vibrating at that at that moment.
00:03:56.800 --> 00:04:00.400
Uh and so what does uh the connected you know uh IoT do?
00:04:00.560 --> 00:04:07.439
Uh well, there's a lot of companies out there that are helping understand vibration, temperature, you know, motor decrease, et cetera, power decrease.
00:04:07.599 --> 00:04:10.400
And all those are just signals.
00:04:10.879 --> 00:04:12.000
What does that mean?
00:04:12.159 --> 00:04:18.319
And so uh what AI can start helping do is understand what those signals could mean.
00:04:18.399 --> 00:04:24.480
Uh and so you're really starting to see, you know, that predictive maintenance get to the place where people want it.
00:04:24.639 --> 00:04:27.920
The biggest drawback here is to blindly trust it.
00:04:28.079 --> 00:04:31.920
And so then one thing is um it's getting really good.
00:04:32.240 --> 00:04:34.800
Everyone should definitely start investing in this way.
00:04:34.959 --> 00:04:42.879
However, we should continue to use the folks who are still with us, uh, still have the experience to pressure test and ensure that we got the right thing.
00:04:43.040 --> 00:04:57.839
So, what it is, what what I recommend for folks out there is is when you get something, take it seriously, understand it, but then go and understand that information, go and do whatever validation you need to, and then go confirm uh that what you are seeing through AI is indeed valid.
00:04:57.920 --> 00:05:00.879
Uh, it's a lot of, you know, the way we would use it to write something today.
00:05:00.959 --> 00:05:05.519
We would, we would, we would ask the prompt to help us think about how to how to be creative.
00:05:05.600 --> 00:05:09.120
And then we would say, okay, now I'm gonna read it and make sure it's what I need it to be.
00:05:09.199 --> 00:05:11.920
Uh similar to when you're doing reliability maintenance.
00:05:12.000 --> 00:05:16.319
Uh, we need to leverage AI uh to make sure that we're getting what we want out of it.
00:05:16.399 --> 00:05:17.920
So that's that's my advice.
00:05:18.240 --> 00:05:18.560
Okay.
00:05:18.879 --> 00:05:23.120
All the legacy technology that manufacturers are having to deal with right now.
00:05:23.279 --> 00:05:26.800
How are they moving to this new A, the new AI tools?
00:05:27.199 --> 00:05:42.079
Technology is going to be a slower, I mean, the these assets, these, these, these huge production lines that our food ab or any manufacturing is dealing with, they're starting to slowly start bringing in uh these manufacturers that will have IoT devices installed in them.
00:05:42.160 --> 00:05:43.920
And they'll come out of it this way.
00:05:44.079 --> 00:05:51.199
Uh, and so uh in one of the tours I was doing, they said, hey, let's go to our smart manufacturing and let's not let's not go to this way, right?
00:05:51.279 --> 00:05:59.040
So the the legacy ones will continue to need those inspections, we'll continue to need you know the monthly rounding, et cetera.
00:05:59.199 --> 00:06:11.279
Uh, but as uh items are being replaced, as you know, 20-year-old pieces of equipment uh come around, those manufacturers will start to embed that intelligent uh assets within them.
00:06:11.439 --> 00:06:18.560
And so then my recommendation is to start investing in those and ensuring that you're slowly building a modern manufacturing plant for yourself.
00:06:19.360 --> 00:06:22.720
Well, AI tools are advancing rapidly, as we mentioned.
00:06:22.959 --> 00:06:31.600
What do you envision as the future of manufacturing and how can business leaders prepare and harness these tools?
00:06:32.480 --> 00:06:37.360
Yeah, well, the future is slowly um being shown to us in different ways.
00:06:37.600 --> 00:06:41.120
And um uh, you know, take those same plants, for example.
00:06:41.360 --> 00:06:46.240
One of them needed a lot of human intervention uh within those manufacturing sites.
00:06:46.399 --> 00:06:50.399
The other ones was a lot of automation already there.
00:06:50.720 --> 00:06:56.240
And what the future will be is us supervising that automation.
00:06:56.480 --> 00:07:05.120
Again, always having that human element in it because we're gonna get smarter about the way we uh we operate versus be completely replaced.
00:07:05.199 --> 00:07:14.240
And and so then the the future will be um let's let uh technology work for us, but let's always stay in the loop to ensure it's it's producing what we want.
00:07:14.319 --> 00:07:21.519
And so we're more uh in the in that control room, ensuring things are there so that when something pops up, we can then go intervene.
00:07:21.680 --> 00:07:26.480
We can go, we can then go make sure that uh asset doesn't stay downtime too long.
00:07:26.639 --> 00:07:29.439
Uh and then your second question is uh advice, right?
00:07:29.519 --> 00:07:31.120
That I would that I would give here.
00:07:31.199 --> 00:07:37.199
Uh and my advice is that is this is is to hey, you've got a rich set of data within you.
00:07:37.439 --> 00:07:40.959
Start ensuring that you're capturing all your information.
00:07:41.040 --> 00:07:57.519
And so those work orders that you're closing out are it's much more important that you take those notes, that you annotate what happened, that you get that problem cost and remedy in there, uh, and then clean that data and bring it into your data lakes because that information will be what will help you respond in the future much faster.
00:07:57.759 --> 00:08:06.639
So uh I just I just say now more than ever, ensure that uh you're you're getting that information within there, and then you can apply AI to get what you need from it.
00:08:07.759 --> 00:08:14.800
Well, from your experience, do you have a particular use case story that you can share about a company?
00:08:14.959 --> 00:08:25.920
Maybe you don't uh name the company, but is there um a transformation story you can share when they um harnessed the AI technology and IoT and they saw a great result?
00:08:27.040 --> 00:08:30.480
Yeah, I could think of uh automotive uh manufacturing.
00:08:30.560 --> 00:08:35.279
Uh, and of course, there's there's plenty in different industries, but we we we dealt with one of those.
00:08:35.440 --> 00:08:39.919
And they definitely uh saw a huge increase in productivity.
00:08:40.080 --> 00:08:48.159
Uh, but again, it's the reason why I think about the human in the loop is that this uh IoT device got them to the predictive maintenance that they wanted.
00:08:48.320 --> 00:08:58.159
So they were able to reduce their there's reactive and then this they were able to produce their preventive maintenance uh 30% uh because they were able to look at their predictive maintenance.
00:08:58.240 --> 00:09:04.080
And then they had every Tuesday, and this is advice for everyone, every Tuesday they would just look at that telemetry data.
00:09:04.240 --> 00:09:12.240
They would look at what the system was telling them to go do, uh, and then they would uh take out things that they knew weren't an issue and then focus on the things that were.
00:09:12.399 --> 00:09:14.960
And so they were able to see improve their uptime because of that.
00:09:15.200 --> 00:09:17.120
So uh this is why I'm a big believer.
00:09:17.200 --> 00:09:22.879
I was able to see it firsthand and how technology was being used uh for them to get the efficiency that they wanted.
00:09:23.200 --> 00:09:24.080
Wonderful.
00:09:24.320 --> 00:09:29.519
Well, if there was one key takeaway you could share with our audience today, what would that be?
00:09:29.919 --> 00:09:32.320
I said don't wait and start now.
00:09:32.480 --> 00:09:34.240
Uh, start investing in the small ways.
00:09:34.320 --> 00:09:36.320
Like I said, start investing in your information.
00:09:36.559 --> 00:09:46.159
Start investing, making sure you're getting that intelligence out there because when that experienced workforce leaves, all that information will be in your asset management system.
00:09:46.399 --> 00:09:55.120
Then you can plug in all the IoT devices uh and have that harmony that you want to have so that the next person that's more of a novice will have the right information.
00:09:55.279 --> 00:10:00.320
Imagine a world where um all they have to do is scan the asset in front of them.
00:10:00.480 --> 00:10:03.039
And this system can tell them, oh, great.
00:10:03.120 --> 00:10:04.960
Well, here's the history behind that asset.
00:10:05.039 --> 00:10:10.879
Here's what telemetry did is telling me I recommend you do X, Y, and Z and with these sort of tool sets.
00:10:10.960 --> 00:10:16.320
And so the more you start investing in your information now, the more it'll play dividends in the future.
00:10:16.720 --> 00:10:17.360
Awesome.
00:10:17.519 --> 00:10:20.480
Thank you so much for coming on the show and sharing your insights today.
00:10:20.639 --> 00:10:22.080
Of course, Amanda, thank you for having me.
00:10:22.159 --> 00:10:23.279
I'm excited about this.
00:10:23.440 --> 00:10:24.879
Yes, and thank you to our audience.
00:10:24.960 --> 00:10:28.799
If you have any questions or comments, link them below and I'll respond back as soon as possible.
00:10:28.960 --> 00:10:31.279
And meanwhile, have a wonderful week.
