Travis Cox is the Chief Technology Evangelist for Inductive Automation. His company develops Ignition, a software platform that helps manufacturers connect to, monitor, and control their industrial equipment.

Nate Wheeler is the host of the popular Manufacturing Insiders podcast. He also owns weCreate, a nationally recognized marketing agency that helps manufacturers grow, save money, and become more efficient.

In this episode of Manufacturing Insiders, Travis Cox explains why tracking machine downtime is the most effective first step for any manufacturer starting a digital journey. He details how to move past manual data entry to get real-time visibility into your most critical operations. This approach allows companies to prove value quickly without a massive upfront investment.

Travis discusses how to identify the single machine or process that offers the biggest return on your initial efforts. He also covers why choosing open, non-proprietary software is essential for long-term success and avoiding vendor lock-in. Listen in to learn how to build a clear business case for automation and use data to empower your operators on the shop floor.

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Nate Wheeler (00:00.821)
Welcome to Manufacturing Insiders. Today I have the Chief Technology Evangelist of Inductive Automation. They make industrial automation software. Travis is extremely knowledgeable about his product and the industrial technology space in general. I thought this would be a valuable conversation to share with people running a shop who have had their eye on topics like automation and AI. They may not be sure how to get started or what action steps to take to gain a competitive advantage in the technology space.

Welcome to Manufacturing Insiders, Travis. Glad to have you.

Travis Cox (00:47.01)
Thanks for having me. I’m glad to be here.

Nate Wheeler (00:48.861)
Tell me about the industrial automation software, Ignition, that you guys have developed. What are its applications from a high level?

Travis Cox (01:03.852)
We are a software company. That’s all we do; we don’t do any hardware or services. We purely build our software platform, Ignition. Ignition is a development toolkit, a platform where you can develop any kind of industrial application. This falls into categories like HMI (Human Machine Interface), which is used right next to a piece of equipment to interact with it, see its status, and control it.

It can also be a full-on SCADA (Supervisory Control and Data Acquisition) system. The idea is that at a manufacturing facility, you can have a central system to aggregate all the data from the plant floor. From there, you can provide alarming, reporting, and visualization applications to all the right stakeholders. It also extends to full-on MES (Manufacturing Execution Systems) to do things like track the overall efficiency of a production facility, monitor equipment downtime, trace products from raw materials to finished goods, and manage quality.

Finally, it supports IIoT (Industrial Internet of Things) applications, getting data from the plant floor to the business or the cloud. We call the Ignition platform a unified industrial integration platform. It allows companies to unify their plant floor, build the right applications for anyone, and bridge the gap between the plant floor and the business to take advantage of all the great technologies available today.

Nate Wheeler (02:48.394)
I love talking about this topic because US manufacturers need to think about how to remain competitive in a global and regional market. I think the companies that combine tools like your software with hardware like sensors and other interfaces will be the ones that are successful in five years. The ones that don’t, will not. One thing I really like about your product is that it’s approachable for smaller companies, which a lot of other solutions are not.

Travis Cox (03:31.98)
If you look at the industrial side of the fence, for a long time, a lot of systems were proprietary and closed-minded. Many of the big automation companies had the whole stack. They would provide the hardware and the software, locking you into their ecosystem. Today, for a company to take advantage of all the available technologies, everything has to work together and be interoperable. With Ignition, from the very beginning, we wanted to be transparent and open.

Our licensing model is simple and unlimited. Customers can put the software in place and continue to scale up with as many data points, applications, or device connections as they want. Their limitations are really just hardware and what they can dream up. Leveraging open standards and IT standards is also important for interoperability and getting data to other systems.

For us, one of the most important things is allowing people to download and try the software. You can go to our website and download a trial version that runs for two hours before needing to be reset. This allows you to fully evaluate every part of the product and make sure it works for your needs.

You can also see all the pricing on the website. We have a full Inductive University with over a thousand free videos on how to use the product. We also have a great documentation system and an online community where people can exchange ideas and questions. We are very much about supporting the community and providing all the tools and resources they need to understand what we bring to the table.

Nate Wheeler (05:15.774)
The point you mentioned about open standards is important. As a marketing agency owner, I think of it like using WordPress versus a niche, proprietary content management system for building a website. That’s something I’ve competed on. Someone might pitch a weird, one-off content management system, but if you want to move to another company or use different technology with it, you can’t. You’re stuck with them; you’re a hostage. With WordPress, everybody knows how to use it because it’s open software. Can you talk about that in the context of your software?

Travis Cox (05:59.266)
That resonates so much in the OT (Operational Technology) arena. As I said, technology there has been proprietary and closed-minded, not leveraging open standards because it was about locking people into an ecosystem. Nowadays, when you look at Industry 4.0 initiatives, which are all about digital transformation and taking advantage of the latest technologies, that closed mindset doesn’t work. You have to be able to get that data out and make it available to other systems. Open standards are a really important way for that to happen.

On the OT side, there are standards like OPC UA, which has been around for a long time. The idea is to have an OPC server that provides connectivity to the different PLCs (Programmable Logic Controllers) on the plant floor. Many of them have different protocols for getting data, and some of those are proprietary or legacy protocols. OPC servers can connect to these disparate systems and provide the data as a standard to anyone who wants to access it. Our product is both an OPC UA server and a client, so we can leverage any system with that standard. This allows us to connect to pretty much any device on the plant floor and allows other applications to get data from us.

Another example is MQTT, which is a Pub-Sub protocol. It allows you to publish data to a centralized broker, and anyone can connect and subscribe to consume that data. It democratizes the data and allows it to get out there. If you support MQTT, IT departments or any other system that uses that standard can access the data, which guarantees interoperability. This extends to things like SQL databases, REST APIs for connecting systems, and identity providers for authorization, like logging in through a Google account, Okta, Duo, or Microsoft Federation Services.

The latest one, if you look at AI, is an MCP server, which is basically how an AI engine can get and understand data from your system. All these standards are important because they ensure interoperability. If we want to achieve all the amazing things we can do today, we have to have a foundation that supports these standards. We believe very strongly in this and have always supported it. With a closed-minded approach, a company might achieve its immediate goal, but what can they do outside of that? That’s where open standards become really important.

Nate Wheeler (09:15.732)
It’s kind of like everybody’s speaking the same language versus having a workforce where everybody’s speaking a different language and they can’t communicate. When we talk about pieces communicating with each other using standardized languages, what are some of those pieces? How are they used in the shop?

Nate Wheeler (09:45.395)
To clarify for myself and the listeners, what are some of the technologies in a manufacturing facility that feed information into your system to make it meaningful?

Travis Cox (10:04.777)
A lot of manufacturing facilities have really old equipment that has been around for a while and still works well. This equipment may not have a PLC or any way of getting data from it. For example, some facilities have manual valves for opening, closing, and moving product, but no way to get data out of them. One of the most important first steps is to do an inventory or survey to see what data is available to access.

Then, you can put a system in place to capture and bring that data in. When you bring it in, you want to contextualize it by providing extra metadata. For example, if I bring in a signal, what are its engineering units? What’s the expected range? Is it part of a specific asset or piece of equipment? If I can contextualize that, I have more information to provide for analytics later on. The first step is really capturing that data.

In some cases, these devices are already in place, and we can bring the data in. In many other cases, they aren’t. That’s where we can leverage new smart sensors. There’s LoRaWAN, which is a wireless technology with battery-powered sensors that are magnetic. You can just place one on a machine to get vibration, temperature, humidity, and other information you may not have had before. For those manual valves, you can put a sensor on it that gives you its position, so you know if it’s open or closed. Now you have a signal you can bring into a system to see if all your valves are in the state you want them to be. The most important part of these projects is to first figure out the data.

We are in a good position now because the technology is available and not that expensive. It used to be very expensive to get access to data. You had to get electricians, run power, and do all these things that were costly. Nowadays, with new smart sensors, I can literally just place one, and it’s wirelessly sending data back. That’s the first step—the data part. Once you bring the data in, if you can just look at it on a screen and provide some simple history, the people who care will know what to do with it. You’ll get a lot of wins just by doing that.

Nate Wheeler (12:43.86)
Almost any piece of data you can measure will probably add some value. But is there a science to deciding which data to track first? For example, do you say, ‘My profitability is tied to the parts per hour I produce, so I need to start measuring that’? Is that the logic behind it?

Travis Cox (13:08.588)
You’re absolutely right. It is about identifying the business case. What is the thing we’re struggling with? Are we struggling with product changeovers? Is it equipment that has a lot of downtime? Or have there been failures of equipment that we care about? Or are we just trying to optimize our process? Identifying that business case first is important because you don’t want to bring in data for its own sake. What are you going to do with it? Many people say that if you just bring in data and store it, it can become a data swamp. We have to know how we’re going to act on that data and who will be acting on it.

A lot of times in manufacturing, if you have a line, there’s always one piece of equipment that is the most constrained and most important. If that machine goes down, you’re not running or making product. Being able to fully track the downtime of that equipment, see all the little microstops, and understand what’s happening is key. It’s hard for an operator to see that, especially in high-speed operations, so identifying that business case is important.

In some cases now, it’s things like energy. Being able to measure the energy consumption of these machines and understanding peak demand can save money on how we operate our facilities. There are a lot of different use cases you can go after, but you have to identify what you’re trying to solve.

If you can get one use case, like figuring out energy consumption, you can simply start with a couple of important assets. Put some sensors in place, get that data, start measuring the energy, and get it to the people making decisions. You start small, get the win, and then move on to the next challenge. That’s the goal. You can’t eat the whole elephant at once; you have to break it up into bite-sized chunks.

Nate Wheeler (15:12.948)
Does an example stand out in your mind of a company that started using your software with other tools and sensors and had a dramatic result? Is there a story you normally tell about that?

Travis Cox (15:30.618)
There are a lot of different stories. Every year we have our Ignition Community Conference, and at that, we have what’s called the Discover Gallery. Customers and integrators submit projects to us, and we get to see their challenges and their successes. There’s always something that really resonates with you in terms of what they’re doing.

I’ll use a couple of examples. One is Chobani. Everybody knows Chobani, the leader in making yogurt. They have a huge facility in Twin Falls, Idaho, that’s over a million square feet. It’s a huge process with a lot going on. For them, every piece of equipment has its own little HMI and control system, but the challenge was enterprise dashboards and visibility of everything that’s going on. They needed to see where in the plant they should be focusing their attention and where issues were happening.

They created their enterprise dashboards with a focus on getting data from all the equipment so they could analyze it and see where their efficiencies were or where they should be focusing their attention. That has created huge yields for them. Because it’s a very modern facility, they are able to identify the areas where they have issues and then put solutions in place to optimize them. A lot of that for them was downtime. Downtime is a big piece. If you can minimize downtime, you’re able to produce more product. That’s a good example.

I like another example: Sherwin Williams. Everybody knows them for paint. For them, they started really small. They did a presentation at one of our conferences about getting just one signal from a machine: whether it is running or not. From there, they stored that data in a database and could get all sorts of results. How long has it been running? How long has it been down? Is there a pattern? All these metrics can come from one simple signal, and that has propelled them to focus on where they should be applying this throughout their process. These are a couple of good examples, and you can see the full case studies on our website, which go into more detail.

Nate Wheeler (18:22.678)
If someone listening identified the linchpin of their operation or their main constraint and wanted to start measuring electricity usage or uptime, would they start with you? Would you set them up with an integrator for the technology piece, or how would that work?

Travis Cox (18:45.198)
Oftentimes, they start with us, and we show them what is possible and help them understand what our product can do. Ultimately, we’re not the ones implementing it. When it comes to getting the solution in place, they can either do it themselves because the tool is approachable, but many don’t have the resources. A lot of times, they will bring in an integrator to do that.

We have a network of over 4,000 integrators that companies can choose from. They can help identify what they’re trying to do, implement it, and support it long term. That’s really the best way to get started. Anyone can download the software, play around with it, and see the value, but an integrator can really drive that ROI.

Nate Wheeler (19:36.415)
We talked earlier about the cultural perspective and having a digital-first mindset. This can sometimes be a barrier to a successful digital transformation. Do you think that’s a big issue? If I’m the CEO of a company and I know I want to start measuring data to become more efficient and profitable, what steps do I have to take beforehand?

Travis Cox (20:14.614)
That’s a great question. When you look at industrial applications like the SCADA systems we mentioned, which are full supervisory applications for the plant floor, or MES (Manufacturing Execution Systems), we are typically thinking only about the data needed to solve that specific problem. We focus on providing what operators need to run that part of the operation. We’re not necessarily thinking about what that data means to the business or how we should design our architecture to get that data to the business more easily. We’re just thinking about building one application, getting it done, and moving to the next.

If you use this siloed approach, it can be harder later on when you want to pull and aggregate data because you’re getting it from different systems. It’s a mindset that has to change now. Leadership has to drive a culture of being a digital-first company. That means we need more collaboration between our different teams, especially between operations and IT. They need to work in tandem.

They need to figure out how to provide a better architecture, one that thinks about data first. We decouple the data from the applications. Let’s get the data, give it the right context, model it correctly, and put it into an accessible infrastructure. From there, it’s easy to build applications once the data is in a common infrastructure. But it takes collaboration between teams to make that a reality; it’s not going to happen from just one side. The problem these days isn’t really the technology; it’s more about the culture. If you can drive that digital-first culture and establish a standard for how you want things to work, that will yield big results.

Nate Wheeler (22:22.006)
From a management perspective, do you have any recommendations for how to instill that culture? Is there a step-by-step approach?

Travis Cox (22:34.894)
I wish there was a step-by-step approach. We’ve been trying to figure out how to instill that. It typically comes down to having leadership that really believes in what they need to do going forward. Usually, there’s one champion in an organization who understands the full picture. If they can get the rest of the teams to understand and be on the same page, that can go a long way.

One action I’ve seen done a few times is to take the operations team, the leaders of operations, and the leaders of IT and put them into the same team. You bring them together into one new team that will drive this going forward. Now decisions are being made with input from both sides instead of independently, which is how it’s been done for a long time. Things like that can really help drive the change we’re looking for. It is a difficult thing, and you have to get organizations that are excited and motivated to do this.

Nate Wheeler (23:49.995)
The motivating factor for most companies is profit. I think if you can make a case for the possibilities, like saying, ‘We have the potential to become 15% more profitable, which adds up to $3 million a year,’ that would be a great way to approach it from a management standpoint. A lot of times, this seems intangible to me. What are the deliverables? What are the possibilities? How do you sell that aspect to people?

Travis Cox (24:36.878)
I think it goes back to what we were saying about identifying that business case. Profit is huge; it’s what every manufacturer wants to do—be able to produce more and drive more profit. A lot of times, the people driving these decisions know what they need to solve. But they’re often attacking it from the top down, saying, ‘Here’s how we’re going to approach it from a digital infrastructure on the top.’

They’re neglecting that all the data and domain experience is on the plant floor with the people running these facilities. Getting them engaged in that transformation is crucial because they know how to make things better and drive things forward. It’s all about that collaboration. You have to prove and show a win. There has to be a place to start. What is the lowest-hanging fruit we can do? Let’s go drive that, get the value, and hopefully, it’ll propel more projects and get people more motivated.

Nate Wheeler (26:08.884)
When you talk about the business case and look at the data from companies that have adopted your software, let’s use small to mid-sized companies as an example. Would you be able to identify the most common use case for your software in those companies? Would it be measuring downtime or production levels? What would that be?

Travis Cox (26:43.406)
If you look at analysis of search terms, you would think something like AI would be the highest. But in reality, especially in manufacturing, the number one thing people are looking for is OEE and downtime. It’s been around for a long time, but it’s still a very important area because it’s tangible and actionable. If I don’t know how my equipment is performing, I’m not going to know what to do. It’s a perfect place to start.

I’d say downtime is still one of the biggest things people are trying to figure out and put in place. There’s never been one easy button for that. In our software, we make it easy to collect and look at that data, but you have to have the right sensors in place. Sometimes you just start with, ‘Is my machine running or not?’ But from there, you have to identify the reasons why it is down. Is it down because a belt broke or because there’s a broken item in there? What is the reason?

Identifying those reasons and honing in allows you to know which piece of equipment has the most downtime and which downtimes you should be focusing on. How can I adjust my maintenance schedule to avoid these downtimes? I still think this is the biggest area because you have to crawl, walk, and then run. The crawling is bringing the data in and getting the right information from the plant floor. The walking is doing things like downtime analysis and getting the data to the right people. Then, running is getting to further optimizations where you’re leveraging AI and ML. So I do think downtime is still a huge topic.

Nate Wheeler (29:01.438)
That’s very informative and helpful. If we can help manufacturers think about what problem they should solve first, I think we can get them closer to adopting technology. Another thing I was interested in talking about is digital signage. In lean circles, you often hear about having displays to show information throughout the shop floor. I know your software plays a role in that. What are some ways companies are doing that, and what are some ways that are really impactful to their business?

Travis Cox (29:53.358)
That’s a good question. Let’s go back to the OEE and downtime example. For a long time, there has been a culture where operators feel like if downtime and OEE are measured, they’re being watched and will get in trouble if their efficiency is low or if they’re compared to other teams or shifts. It’s really about putting these things in place to help operators understand how they’re doing and figure out how to perform better, not to get them in trouble. That’s what we’re trying to accomplish.

Digital signage can really help. Imagine having dashboards where you can see at a glance where things are right now, especially compared to where you have been or where you know you can go. It can help motivate the operators and gamify it a little bit. They’ll want better numbers and will go and figure things out. Digital signage can provide the right context to the people who need it quickly. Their hands might be full, and they might not be right near the equipment panel or able to get out their phone. A quick look up at a big TV can really help.

Some successful companies have heads-up displays literally everywhere showing various KPIs, whether it’s OEE, downtime, or other metrics they care about. The operators know they can act on that information. It’s really about making it actionable and getting them motivated to understand what’s happening quickly.

Nate Wheeler (31:50.997)
I was talking to another guy about Lean, and he said something like someone who knows nothing about your operation should be able to look at it and, in two seconds from 20 feet away, know exactly what the status is—is it good or is it bad? I’ve also heard companies talk about the gamification aspect, where it lends itself to a better work culture and more motivated employees because they feel like what they’re doing is being recognized. If they do a good job, they want people to notice it.

Travis Cox (32:36.846)
Exactly. I think that comes down to culture, too. It’s about why we put these things in place, why it’s important, and how you can benefit from it. It’s about recognizing when people do a great job, like showing, ‘Hey, this team achieved this OEE for this shift, congratulations.’ That culture and recognition go a long way. The most important thing is the recognition that operators don’t want to do a bad job. Often, they’re the scapegoat for everything.

When something goes wrong, it’s pinned on the operator. But when you really look at it and audit those situations, it’s typically that the systems weren’t there to help them do their job right. We’ve got to help them do their job, and in turn, you’re going to get better results. If you can change that culture, it’s very impactful. I came from an integration background myself, and I’m all about being operator-first, making sure that systems are designed the way they need them. It’s not designed for the person at their desk; the people who need to run this every day need to have the right tools in front of them.

Nate Wheeler (33:49.879)
In my mind, it seems like every type of company could benefit from some level of automation. Do you find that there are companies that really benefit more, whether it’s because they have a high volume of repeatable parts or a certain number of employees? Is there a typical use case where one kind of company will benefit more than another?

Travis Cox (34:28.654)
That’s a good question. When you look at the size of companies, usually the big guys have the resources and budgets to bring in a lot of technology and take advantage of it. When you get to the smaller manufacturers, their teams are a lot smaller. However, data is still as critical to them as it is to the large manufacturers. The small and medium-sized ones could really benefit from bringing in specific technologies.

What’s great now is that those technologies are more approachable than ever. When a technology is introduced, everybody gets excited, but it’s really expensive. Then it comes back down and normalizes, and those things become approachable. I think the smaller teams are really hungry for the data. The bigger companies have the data and are now trying to figure out more complex things, whereas a lot of smaller ones don’t have it. I think they can certainly benefit the most from just getting as much data as they can from the plant floor because we know time and time again it pays off if they can just get that into the right people’s hands.

Nate Wheeler (35:51.359)
You mentioned before having this data in a format that lends itself to being open source. Is there data being aggregated by companies using your software that other companies new to it can pull from to start building a business case?

Travis Cox (36:21.87)
That’s a good question. That’s what I was talking about earlier regarding what our digital infrastructure looks like and decoupling the data side from the application side. Where people are driving this right now, there’s a concept called UNS (Unified Name Space). The idea is that you have a lot of data, and you want to organize it into a hierarchy that makes sense. If an application or somebody is going to tap into the data, they have to be able to understand it, know what’s in there, and find things quickly. It can’t be a data swamp; it has to be organized.

UNS is where they can organize into a hierarchy, and this often follows a standard like ISA-95. You can define the enterprise, the sites, the areas of the manufacturing facility, the lines or pieces of equipment, and the type of data it’s coming from—process data, KPI data, OEE downtime data, whatever it might be—organized into this namespace. That’s step number one. Organization and modeling of data are incredibly important.

By modeling, I mean defining an asset, object, or information model. The idea is that if I have a compressor, a tank, or a motor at different facilities, they might be from different manufacturers and have different sensors and PLCs behind the scenes. But to my business, they are still a tank or a motor. If we can define what that looks like and standardize it across the entire board, we can map the differences behind the scenes to this common model. Then we’re looking at assets and can compare these things, which makes it much easier to build an analytical model. Information modeling and organization are paramount.

That’s why if you think about digital infrastructure first, define what you want that to be, and drive that, the applications will come because you have it organized. The other part of this is making data accessible from a technology standpoint. That’s where open standards become really important.

For example, a lot of people are using MQTT right now. I can publish my data from the plant floor into a centralized broker where all that data is sitting, and different people can connect and consume it. When I publish new data, they get it, organized in the form we need with the context they need. That’s one area where people are getting success right now.

There are different products that support that, plus historical data, and there are open ways of getting that information out, like data warehouses, time-series databases, and GraphQL databases that handle relationships. The idea is to define what that infrastructure looks like, how to organize it, and how to get it into a form that is open and accessible to all the right stakeholders. If you have that mentality, it makes your next job really easy. You’re not having to write custom code to map data from one system to another and then normalize it.

When machine learning first came out, data scientists would grab data from a legacy historian that wasn’t contextualized, and some of the data was bad. They would go through this whole effort of bringing it in and writing code to clean it, normalize it, and map it to something different. Then you can’t trace it back to where it originally came from.

They would write algorithms off that data to get analysis. It would be better if you could define that at the edge as the single source of truth, so you don’t have to ever think about remapping it. Everywhere you want to use that data, it’s understood, and you have all the information and context needed to do your job. This is a big area right now, all wrapped under this UNS concept, which makes it easier for people to conceptualize.

Nate Wheeler (40:52.414)
My understanding is a lot more simplistic than most of what you explained. I’m also picturing scenarios where you have workforce issues, with new employees coming in and older guys with all the knowledge retiring. It seems to me that this is a way to centralize knowledge and make the training process easier down the road. Is that accurate?

Travis Cox (41:24.162)
Absolutely. When you bring in the newer workforce, they are used to certain technologies. If they have to learn old, legacy stuff, they’re going to be less productive. But if they can tap into something they already know, they can start doing more themselves. This isn’t just about understanding the languages, data formats, or protocols, but also about getting information on a phone or a tablet in a form they’re used to.

By doing these modernization efforts, you’re addressing a world where so much domain expertise is required. The plant floor is a wild west of legacy systems and different types of technology. If you normalize it, you’re making it easy for new people to do more with less to learn. I truly believe this is an important part because if you have these old systems and you don’t modernize, you might be stuck when those people retire. How do you manage that?

Nate Wheeler (42:39.828)
When we were talking about having a repository of information across different companies to build models, you mentioned compressors. What came to my mind was, if you know a compressor of a certain size should use a certain amount of electricity, and yours is way out of whack, you can flag that as a problem based on that information. Am I right?

Travis Cox (43:21.506)
Yeah, absolutely. Right now, in a lot of data warehouses, there are automatic tools for things like anomaly detection. You generally know what a good profile is for a piece of equipment; the manufacturers typically tell you what’s normal. If you collect the data and have common formats for all of it, you can approach anomaly detection really easily. It could tell you, ‘Of your 100 compressors, here are the three that are deviating from normal.’

Then you know where to focus your investigation, look up other data, and figure out what’s happening. Maybe the equipment itself needs maintenance, or maybe it’s on its way to failure. Just being able to figure out where to point your attention is a huge area right now.

Nate Wheeler (44:21.194)
This might be a bad question, but a lot of manufacturers and machine shops talk about ERPs. How does that relate to your industrial automation software? Does it tie into it or replace it? How does that work together?

Travis Cox (44:44.558)
It definitely ties into it and integrates. We’re not trying to replace ERP; we don’t do ERP. That’s the business system where all the orders, transactions, and bill of materials exist. The integration is paramount and a necessity. Ultimately, the schedule of what I need to run is typically in the ERP system, and I want to pull that down. We may have to make some adjustments to that schedule based on what we know about the equipment, but we need to be able to pull it down.

When we execute, we know what we’re executing and what products we’re making. We have the recipes and all the important information for that. Then, the results of that run need to go back into the business systems. Having it integrated both ways lets people on both sides get the full context of what’s going on. You should never be entering that data by hand. If you are, you have the potential for a mistake. You could fat-finger it and put the wrong information in. The more we can automate the integration between the operations side—the SCADA and MES—and the ERP, the better. We’ve seen that time and time again.

Nate Wheeler (46:18.802)
That’s fascinating. You definitely need both pieces of technology. You’d be amazed how many shops out there are still entering stuff by hand. There is a lot of paperwork in many of these places.

Travis Cox (46:31.502)
It’s a common thing, and a lot of people are ashamed of the fact that that’s just the reality of how things have been done. They have whiteboards and are entering data manually. It’s not their fault; it was just very difficult and expensive to get a lot of these things put together. That’s coming down a lot now, and these things are more approachable.

But if you’re doing it that way, the problem is you’re not as efficient as you could be. In a digitized world, things need to be more automatic and digitized, using tablets and phones and getting data directly from the equipment. Integrating the systems together becomes more paramount now because of where we are. But nobody should feel ashamed if that’s how they’re doing it. We just know there are areas for improvement, and that’s where you can hone in.

Nate Wheeler (47:33.908)
I’d like to wrap up with a little segment called ‘What Do You Need?’ If there’s somebody listening who either has a resource or is in your target market, are there any problems you’ve been trying to solve recently that you’d like to meet that person for?

Travis Cox (48:05.272)
That’s a great question. First of all, if anybody has any questions on how Ignition works, we’re more than happy to do a demonstration. You can download a trial or request a demo. We’re very transparent and open to that. If there are any problems you’re trying to solve, we can see how the product can fit in.

We’re in the middle of getting a new release out the door. There will be a new beta coming in the next couple of weeks where we are really bringing more IT technologies into the fold and bridging that gap between OT and IT. Ultimately, if you have any digitization efforts, there are ways to start. There are great technologies out there right now that you can easily take advantage of.

Nate Wheeler (49:09.812)
With your beta, are you using people who are already customers for testing, or are you doing it in-house? Are you looking for companies to participate?

Travis Cox (49:20.556)
It’ll be a public beta that anybody can access. With it, we’re getting into a lot of the newer ways people are managing and deploying these systems at scale. That’s where a lot of IT departments are now more responsible for deploying and physically managing these systems, but of course, these things are still controlled and maintained by the operations folks who are building them. This release is focused on that management side as well as getting data to more places.

We’re supporting newer protocols like Kafka to do event streaming and move data between different systems more easily. We’re building a new historian into Ignition using new time-series technologies. We’re adding things like offline forms so people can enter data on their tablet even if they have no cellular or Wi-Fi. There are a lot of these features that are going to help bridge that gap even further. It’ll be public, and anybody can play around with those features once it comes out.

Nate Wheeler (50:32.758)
Cool. So, beta testers. All right. Well, Travis, thanks a lot for coming on today. You are a wealth of knowledge. I think anybody in our manufacturing audience should reach out and have a conversation with you because we need this technology inside our manufacturing facilities if we’re going to stay competitive. So thanks a lot.

Travis Cox (50:58.178)
Yeah, thanks for having me, Nate. I really appreciate it.

Nate Wheeler (51:00.394)
Awesome.