Real-time automation and cloud updates for robotic “employees”
For the latest episode of our manufacturing podcast, Data In Depth, we sat down with Zach Boyd from Hirebotics. Zach helped us dig into the next step in manufacturing automation and the Internet of Things (IoT) — robots as a service. In this model, customers can “hire” robots to tackle specialized tasks, especially in areas where safety may be a concern. He shared how customers can gain real-time insights into how their robot is performing and make adjustments through cloud updates.
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Transcript
Announcer: Hi, and welcome to Data in Depth podcast where we delve into advanced analytics, business intelligence and machine learning, and how they're revolutionizing the manufacturing sector. Each episode, we share new ideas and best practices to help you put your business data to work. From the shop floor to the back office. From optimizing supply chains to customer experience. The factory of the future runs on data.
Andrew Rieser: Welcome, and thanks for joining us for season two of Data in Depth, the podcast exploring data and its role in the manufacturing industry. I'm your host Andrew Riser. Today we are joined by Zach Boyd, VP of Platform for Hirebotics, welcome, Zach.
Zach Boyd: Hey, thanks for having me, Andrew.
Andrew: Yeah, really excited for today's discussions and the topics that we're gonna cover. But before we dive into that, how about you share a little bit about your background and what ultimately led you to Hirebotics and then maybe the elevator pitch on Hirebotics just to set the tone for the conversation today.
Zach: Sure, so my background is in software. The past few years, I've been at other companies leading software teams and building out software. And so about a year and a half ago, I was approached by Hirebotics to come help evolve and continue what they had already started to build. And I really appreciated the vision that they had set out and the space that they were going after. Long story short, I made the decision to join the team. And here I am.
Andrew: Very cool, so how about just dive in a little bit into Hirebotics, about the company, about the products and the space and then we can kind of pivot into conversations around that and the importance of it.
Zach: Okay, so the goal of Hirebotics is to help manufacturers with automation. Automation is hard, and manufacturers are typically risk averse. So our goal is to help de-risk automation. And we do that in several ways. One we know that a lot of companies are constrained by capital. At the same time, they might also not have the resources or technical skills to deploy and support automation in the long run. So we align our business model to help achieve that for that customer, and in this case, you can pay by the hour to hire a robotic worker, we typically go after applications around machine tending. And with this, there's no upfront capital cost to you. We actually do the mechanical design, integration to the machine, build out the software and then provide support after the fact. So we really try to align our incentives with the manufacturers and making it as easy for them to hire a robot as it is for them to hire an employee.
Andrew: Very cool, I love the business model around that. And obviously to make all that work, there's a lot of different components right. So you mentioned a capital part upfront of just bringing automation into the shop floor, the actual robot or Cobot that's being brought in, the resources to kind of assemble all that and create this work cell or work center for this thing to operate. And what you guys are doing is essentially taking away all of that, for lack of a better word, headache and kind of challenging aspects of that. And essentially providing a robotic worker that's ready to perform day one. And the company is essentially getting the value out of that, because they're just paying by the hour that the robot works, right?
Zach: Exactly, and when the founders started this, the thought was that it's gonna be a lot of small, medium sized businesses that just couldn't afford to buy robotics, or get into robotic automation. And over time, what we found is even large companies that have teams dedicated to this and they know how to deploy and support will still come to us to help them with the project because they know the headaches that can come along when it comes to deploying a robotic cell.
Andrew: Right, so now let's get nerdy and geek out a little bit about the software side of this. So why cloud connected, like explain your guidance thoughts around that and how you've approached that to make that a more seamless part of the process.
Zach: Yeah, so in order to de-risk it for a particular customer, we take on a lot of risk, capital risk, as well as other risk just around support and the amount of time we might put into a cell. So we knew that we had to have a robust set of tools to support these robots. Not just on site when we are helping the customer, but when we're remote. So in our case, we're based out of Nashville, Tennessee, but we have customers as far as California. It's not economically feasible for us to hop on a plane every time the customer has an issue to find out what the root causes and then provide a resolution. So we Cloud Connect our robot so that we can gain real time insights into how that robot's performing, what problems it might have. And with that, we're able to 98% of the time, come to a resolution, identify that root cause and provide a fix from a mobile application that we've developed versus sending somebody on a plane to go help that customer.
Andrew: Right, now that's fantastic. So the typical, I guess, Internet of Things, if you will, from a buzzword perspective, but really being able to provide that proactive and preventative remote support and maintenance, but then also upgrades and enhancements to continue to provide more value for the customer as well.
Zach: Definitely, so we are able to provide updates to the customer remotely, we have visibility into how that robots running, as I mentioned, whether that's viewing the state of I/O, viewing logs, variables as that program is executing, we surface up a lot of this information. And then that helps empower our team to really understand what's happening in that cell. And as I mentioned, identify that root cause. We also in order to align with that business model, we do have minimum number of hours that we look for in a particular application. So we're able to aggregate up all that data and look back and see the historical trends on, how has this robot ran over time? How many hours per week does it run? So we use that from a billing perspective as well.
Andrew: Cool, maybe you can help kind of paint the picture for the audience of, you don't have to name customer names or anything like that but just a machine tending Cobot, and the start to finish and some of the data and how the app works, whatever the the logical flow is of that explanation, but maybe put it into the context of a real world scenario.
Zach: Sure, so a customer will typically approach us because they've got a particular machine that they might not be able to keep an employee in front of, for a long period of time, or there could be safety reasons to put a robot there. But either way, they wanna automate this process. And typically it involves picking a part up, placing it into a machine and then unloading that part. So part gets processed in the machine. And when they approach us with that, and we identify that it's a good fit, we begin the mechanical design, a risk assessment, and then begin writing the software to support that application. So the actual, you know, the heartbeat of the application. And as mentioned before, the customer's not paying anything up until this point in time. So eventually we'd go through all of that we go for deployment to the customer site, coordinate it with them, can take up to five days depending, there's a lot of things that would have to take place for that to be successful. And we'll do the integration into the customers machine at that point in time as well. And then once we leave, that's when we start using the tools to remotely monitor and support that application. I'll point out, the customer has access to this as well. So a customer who's got access into our app is able to sit at home on their couch and see how that cell is running without having to go in and actually take a look. So, see how many parts it's produced that day. We have charts and dashboards that are unique to each customer based on what they value, what they want to see, so we set that up. And so we try to give them as much information as possible to really help drive their day to day decisions as well.
Andrew: So you mentioned software controlling this is that literally, you're setting the program for the robot and the actions that the robot's taking, in addition to the data that that specific robot is spinning out. And so maybe just provide a little bit more context around your definition of software in this case, and how that's being thought of and controlled.
Zach: Sure, so yeah, in this case, I am referring to the software that executes on the robot itself. Now we do some special things in that so that we can pull data from the cloud. We might have variables in that software that we actually wanna be able to change remotely. And so we'll program the application in a particular way to leverage that. That's what really gives us the ability to start to tweak how this thing runs remotely. We're not changing the core program, but we might be tweaking a way point here, tweaking a way point there, in case something in it's cell has shifted over time. You know, that's one piece of it. We do have a Cloud Connector and that's really what's dealing with the integration to the cloud. And then the backend systems is at the end of the day, what's gonna help surface all of that to the mobile application.
Andrew: So maybe you can share a little bit more, dive a little bit deeper into the analytics. So whether it's the real time analytics, these aggregated kind of key value points that then gets surfaced back to the mobile app. But I assume that with a connected device like this, there's probably streams and streams of data that this thing's kicking off. And so how do you sift through all that noise and identify the kind of key elements that you're gonna care about, and ultimately, the customers wanna care about?
Zach: Yeah, so there is a lot of data that comes from those robots, and we probably send a fraction of it up compared to what we could. We think about data is really in two sides. One, there's the piece for the customer and really what's going to help drive value and insight for them around that cell, around that process. And so depending upon what the customer needs, they're gonna get certain things around parts made per day, being able to bucket that by hour. We can set up the cells to run multiple parts so they can see charts of that group together. But if they have any special requests as well, because we're doing the work and writing that software that runs on the robot, we can give them custom analytics as well. And with that, we can then have charts in the mobile app for them that displays that. So that's the customer side of it. The support side for us, we wanna know how this robot's performing, and if there's any issues that should be addressed. So we send a lot of the metrics around the number of stops a robot might have, has it hard crashed, which would be considered a protective stop in the case of the robot that we use. And so we take a look at all of that and have specialized dashboards and charts in the mobile application as well, with real time alerting and monitoring.
Andrew: Very cool, so one of the things that I've seen not only talking with you all, but some of the stuff that's been promoted out there publicly recently is around the BotX and robotic welding applications. Can you maybe touch on that a little bit and just kind of share the use case and how you're expanding beyond some of those basic applications like machine tending and evolving into something that might be more complex like the notion of welding.
Zach: Definitely and, you know, this is a topic that we are really excited about. So the BotX is a for-hire robotic welder with the same model that you pay by the hour. And compared to machine tending, this is more of a product solution that if you needed one, within two weeks, we could have one shipped to your facility, we would help set up the system for you, and then train you how to use it. So in the first model of machine tending where we do everything from the mechanical design, to writing the software, to supporting it, the big difference with the BotX is you actually teach the robot the welds that you want it to perform. So you're not having to contact us if you wanna run a new part number. From the mobile application itself, you have the ability to set up what we call a flow. We avoid the word programming in this case, because it's significantly easier than that. And you can set up these flows to perform the welds that you want, and depending on the complexity it could be in a matter of minutes. And then from there, you're able to do what you want. We still provide the support through our mobile app to any questions that you might have, if you aren't sure that you're doing something right. Or there's an issue that we need to take a look at. So we use those same tools to gain insight into how that cell's running to help support the customer.
Andrew: Yeah, that's a really cool application, and definitely encourage everybody to check that out. We'll provide some links and some background around that information in the show notes. But thanks for explaining that and talking about the common use cases around it. I think what becomes really powerful in these conversations that we've been having with folks on the podcast is that a lot of people are super intrigued by Internet of Things and connected devices. And I think more and more manufacturers are now purchasing things, whether it be automation or just new machines that now have the ability to connect to the internet on the shop floor, and I think that data becomes the next big lever that they're trying to figure out how to get the most advantage out of. And so now when you think about your traditional kind of transactions that happened with the manufacturer, it's primarily with the ERP system, and now bringing this shop floor data and an automation around all that. I think it's just opening up the floodgates for data. So any perspectives on that? Any customers where you're seeing advancing beyond just having a Hirebotics work center and are connecting other devices on the shop floor or integrating into their back office, ERP systems? Any stories that you could share around that or insights?
Zach: Yeah, I mean, as far as particular customers and the types of machines they have, you know, everybody's a little bit different, but suppliers who are selling these products are starting to allow these customers to connect them to the internet and aggregate data and do what they need. And so we definitely see a trend more of a desire to Cloud Connect. And where there was what's more of a barrier, or I would say, hesitance towards cloud connecting something, we're starting to see that go away as well. I would say the value proposition is becoming higher at this point, due to the data that you mentioned, right? There's a lot of insights that can be gained from that to help drive efficiency or process optimization. And so as these tools continue to evolve, and the accuracy continues to increase adoption is gonna follow suit.
Andrew: So Zach, we also like to ask your perspective on where the future's evolving with this, so can be Hirebotics related or just kind of trends that you're seeing. But when you think about Cloud Connect, where do you think the next handful of years are gonna take that and exploit this data?
Zach: Well, as mentioned before, you know automation can be hard. And what we are starting to see is as more things are being cloud connected, as you pointed out, managing automation can be hard, right? So even getting it in, getting it set up is hard enough. But then after the fact, being able to support it and diagnose and really keep it running as you want, that's a difficult thing to do. And so, as robotic automation continues to expand in manufacturing and connecting to the cloud becomes more of a common thing, we see a need for a robust tool set to help support and monitor applications. And that's where we spend our time is helping build out that tool set. And over time, letting the community start to use that as well to where it doesn't have to be a Hirebotics managed self. But instead if you have a piece of automation that you want to support and monitor and do these types of things remotely, in our case, it's Universal Robots, the manufacturer that makes the Cobot. We want to provide those tools back to community.
Andrew: Very cool, well really appreciate you joining the podcast and telling the story about Hirebotics and the cool stuff that you're helping support there. And for those of you listening, if you'd like to learn more about Zach and Hirebotics and their solutions, I'd encourage you to visit their website, hirebotics.com, and we'll also connect the show notes with relevant links to the online profiles and videos that we talked about throughout the podcast. Also, if you enjoyed this episode, please take a moment to rate the episode and subscribe to Data in Depth. Available on iTunes, Google, Spotify, Stitcher, and pretty much anywhere else you might listen to your podcasts. Thanks again for joining us.
Announcer: Data in Depth is produced by Mountain Point, a digital transformation consulting firm focusing on the manufacturing sector. You can find show notes, additional episodes and more by visiting dataindepth.com. Thanks for listening and be sure to subscribe wherever you get your podcasts.