Sean Riley:
So with all the fancy introductions out of the way, welcome to the podcast, Amit.
Amit Patel:
Thanks for having me, Sean. I appreciate it.
Sean Riley:
We're really going to delve into a bunch of different topics involving Floor to Cloud, but one thing I was hoping you could explain for some of our listeners who might not be familiar is, can you explain digital transformation for anyone that might be unfamiliar with the term?
Amit Patel:
Yeah, absolutely. Digital transformation is such a commonplace word, if you will, that's across our industry. And oftentimes when I ask one individual to another customer, oftentimes you get different responses, but there is a common thread, and I hope in my response here I can demystify some of that. And digital transformation is really a journey. It's a journey that an organization and its people take on to deploy digital tools and technologies at scale, and ultimately with the goal to understand forming criteria, I think. It's, what happened yesterday in my plant? What's happening now in real time? What's going to happen tomorrow predictive? And what should I do next? And that's prescriptive, right? So it's really a combination of technology and people coming together to deploy those tools and create value at scale.
Sean Riley:
It's a great way of summarizing it because like you said, digital transformation can get a bunch of different answers and have a bunch of different explanations when you ask somebody for something specific about that. What would be the parts and the tools that are utilized on the line or in the operations to make up the digital transformation? What technologies and stuff would be the key points of that?
Amit Patel:
Yeah, I would say it falls in three major categories. So the tools and technologies can fit in three main categories that I said is, are you digitizing a machine or an operation? Then the next layer is, how do you digitalize it, right? Start to move away from analog and get digital types of signals. And then, of course, with that, then you can begin to really automate.
And to go back to your question, the types of tools and technologies, it's really driven around fundamentally sensors. Sensors are the backbone to digital transformation because you create that data, you unlock trap data and you use that data to contextualize it. And how you contextualize it can happen in several ways. And you're seeing a huge shift or let's say adoption in edge technology. And edge technology is a perfect tool technology that lets you collect data on-prem or offsite and collect, analyze and push that data, or now information, if you will, to larger systems and servers where you can really begin to tap in to see this is what's happening at my enterprise level or this is what's happening at my machine level. And that's just one example of the vast amount of tools that are available in the umbrella of digital transformation.
Sean Riley:
Then using those ideas, could you give us an overview on optimizing operations with this Floor to Cloud approach?
Amit Patel:
Yeah, absolutely. First and foremost, a Floor to Cloud approach is ultimately based on two things, I believe. It's based on the principles that are set by an organization and those principles have to include the leadership, the decision makers, if you will. You have the IT folks. Digital transformation isn't just about operational technology, it's about also information technology. Ultimately, when you create set data and you connect set data and convert into information, you have to have the right stakeholders to own the data highway, which is the IT folks. And then you also have to have, as I mentioned, operational technology. You have to have the right folks who understand the machine, who work on the machine at the floor. And together, ideally, the principles are set, a north star, some guiding rules, if you will.
And then the second piece to that is pragmatism. Oftentimes, digital transformation tends to be this large undertaking and boil the ocean strategy. I've seen it in my own experience as well. And ultimately, the pragmatism piece is just as important because it helps you identify what are the right steps and pieces that you need to take on in order to make justifications around ROI to prove your concepts within your own plants or machines. And a perfect example of being pragmatic is starting with one use case, right? Starting with one use case and proving out the value, justifying the ROI allows you to create a set of principles that you can ultimately scale across your plan.
Sean Riley:
Very interesting. I know one aspect of digital transformation and some of these tools can result in energy monitoring. And I can't do a presentation or get to a podcast without talking about sustainability, so now's as good a time as any. With it's so important to consumers, how can energy monitoring boost sustainability in manufacturing?
Amit Patel:
Yeah, absolutely. It's such a huge topic in our industry today, right? Many customers have set aggressive goals to reach net-zero or have created sustainability targets, right? And sustainability is one of a few others that customers focus on, of course. But to stick on the topic of sustainability, I boil it down to this age-old question, how do I know I have a problem if I'm not measuring it in the first place? And customers today, CPGs, they all understand that energy is obviously a utility. The resources that are used to produce said good or packaged said good are not really going to go away, but how can I get more with less?
And understanding a baseline or what normal is for your machine, whether it's using energy, compressed air, water, steam, understanding and tracking those utilities is a really important step. And oftentimes that can happen at a site level or facility level, maybe a line level, but really, if you're getting to these aggressive targets that are being set, really getting to carbon neutrality, you want to make sure that you have control and visibility or the resolution that goes down to set a machine, that goes down to a device. And energy monitoring is really the first step in answering that. How do I know if I have a problem if I'm not measuring it in the first place?
Sean Riley:
Very interesting. And I guess... I'm thinking of energy monitoring and I'm wondering how else that can help beyond sustainability. And I'm thinking, is this something that can also... We know labor is an issue and losing experience personnel to retirement is an issue. Is something like energy monitoring, could that ease some of the teams that are stretched thin because they're losing labor? Is that something that can help close the skills gap?
Amit Patel:
Yeah, I think it definitely helps reduce that stretched labor or fill in some of the skillset gaps. I think anybody who's probably listened to this show here understands that finding talent and especially the right talent is becoming increasingly difficult. The folks that have spent decades, years at a site are either retiring or moving to other opportunities. And oftentimes we call that the tribal knowledge. I know how a machine works, I understand its heartbeat and this individual ends up leaving and now those secrets are with that individual.
And what something like energy monitoring does beyond obviously beginning to make those optimizations and energy efficiency is to your point, is it gets the data in the right hands that it needs to get to, right? There's a historical understanding of how this machine behaves in terms of energy consumption, peak demand hours. And if you talk about data, that's stored somewhere that's accessible. If you look at ways to consume data, we as humans look at dashboards and visualization tools that are built around software. And if you take that a step further and you have somebody who's relatively new in the field or needs to understand how a machine operates, it's usually that interaction between that human and that interface, that software that helps them fill that gap quicker than perhaps trying to learn on their own. Understanding what normal is and having a record of that and keeping that is a really important step in filling that skillset gap and at least getting a idea of how a machine or an asset or a line is being.
Sean Riley:
Fascinating. So then, at the end of the day, everything, especially in manufacturing and packaging and processing, everything is about productivity at the end of the day. It's about getting the product out as fast and as efficiently as possible. So I guess, how can this energy monitoring help packaging lines do more and experience less downtime?
Amit Patel:
Yeah, no, it's a great question. And one example I like to bring up is the topic around compressed air, for example. Compressed air is used across manufacturing processes from packaging to processing. They're used to open valves and extended retract cylinders. I think of compressed air being the heartbeat of a machine or a plant. And when you look at areas like compressed air and trying to see how it impacts productivity, one of the things we think about is the rebalancing or balancing, if you will, of the devices that consume said compressed air.
The best example I give you is pneumatic cylinders and actuators. And oftentimes these devices are suboptimized within a pneumatic circuit. And by conducting a form of energy monitoring that gets to those device levels, you can figure out what that optimal ratio is between cycle time, pressure and airflow. Meaning, can I get the same cycle time by having that cylinder use less pressure or reduce that flow? And oftentimes we see the answer is yes with customers. And that's one way of going back to that comment I made very early on is doing more or doing the same with less. And that's one example of productivity.
Another one is think about air audits in the same vein of compressed air topic here. Today, around 76% of manufacturers still manually test for leakages. And by deploying energy monitoring where you have the right airflow sensors that measure said compressed air, you have the right edge technology, for example, that collects, analyzes that data at the machine level and then some visualization tool and dashboard where you can see it, you can begin to see where compressed air is still running, whether it's on a specific machine or a line, and start to prioritize those larger culprits. And now what you've done is prioritized your maintenance. You're way more structured around this and you can really begin to tackle those larger culprits first in your maintenance practices.
So those are two examples of improving productivity from maintenance perspective, but also on the rebalancing of pneumatic devices as an example.
Sean Riley:
That's really cool. That's a great example of something that's as simple that everybody experiences. Anyone that's walked by a packaging line has heard compressed air. So that's a good way of putting a picture on what we're talking about.
I guess to wrap up, do you have a couple of tips on how manufacturers can begin monitoring energy in this way?
Amit Patel:
Yeah. We talked a little bit about that idea of around approaching digital transformation and energy monitoring around a Floor to Cloud approach. And just reiterating that I think it's important that folks that are listening to this look at two things, the principles that are set and having the pragmatism to start with a single use case. And by taking a Floor to Cloud approach, it's really about starting small and scaling fast. And scaling fast allows you to capture set of value at scale. That's ultimately what you want to do. You want to create value on a single machine and line and then expand that out.
And I would say it starts on the plant floor. And one quick way to really think about a use case is compressed air. We talked about it throughout this podcast, and it's about having the right airflow sensors at the point of use that collect that data. Then you have the right hardware, whether it's a industrial PC, an edge device that allows you to quickly connect, collect, analyze, and push that data to a dashboard. And you could start to get that data visualized and start to understand what normal looks like.
Ultimately, it's also about the people that take on this project because using these insights, you want to make sure that you get them in the right hands of the operators, maybe the plant manager, maybe a sustainability manager as well. So taking a Floor to Cloud approach is really a way to look at a specific machine, align and test, learn and adjust the deployment of those technologies and start to scale that, and it's iterative at the end of the day.
Sean Riley:
That's perfect. That's a perfect button to put the end on our little discussion here. We can't thank you enough for taking time out of your busy day to come on here and break down this energy monitoring process for us. So with that, we just want to really thank you for coming on here with us.
Amit Patel:
Absolutely. No, it was my pleasure. Thank you so much for having me.
