Unifying Operational Technology Data into Actionable Information

by , | Aug 4, 2021 | Digital Transformation, Reliability | 1 comment

Emerson's Alessea LaneA post several weeks back, Driving Reliability Improvements by Monitoring Assets Plantwide, proved to be very popular. It featured Emerson’s Alessea Lane describing how technologies in the Plantweb digital ecosystem help to improve the overall reliability of operations to meet production targets, ensure safety and avoid high maintenance costs–on various asset types and across multiple sites.

I invited Alessea to share more about these technologies with our podcast listeners. She discusses how operational technology can be made highly accessible by breaking down the silos in which they live and turning data into actionable information that’s available to the right people at the right time to drive transformational performance.

Visit the Plantweb Optics Platform section on Emerson.com for more on these technologies and solutions and connect with other digital transformation experts in the IIoT & Digital Transformation group in the Emerson Exchange 365 community.


Jim: Hi everyone, I’m Jim Cahill, and welcome to this Emerson Automation Experts podcast. Data is a critical component in digital transformation initiatives. Often, it’s scattered and difficult to gather, assess, and make decisions from. A few weeks ago, I shared a very popular video by Alessea Lane about driving reliability improvements through the use of organized and accessible data. Alessea joins us today to discuss more on this in this podcast. Welcome, Alessea.

Alessea: Thanks for having me, Jim. So, really great to be here today talking about digital transformation.

Jim: Yeah. It’s really great to have you here. Now, for our listeners who may not have yet seen the video, can you share your educational background and path to your current role?

Alessea: So, again, my name is Alessea Lane, I’m currently the product manager for Plantweb Optics. I started with Emerson as an intern. Before that, I went to the University of Florida. I got my bachelor’s degree in chemical engineering, worked in our lab there, and then took that chemical engineering knowledge to Emerson as a sales development intern associate. I worked with our reliability solutions team for a summer and that, and then started full-time as a sales enablement engineer with the same group. I worked in that role for about three years, and then now in my current role as product manager. I’ve been in this role for about seven months here in Austin, and focus on the Plantweb Optics products, which includes our optics data lake and portal and connectors.

Jim: That’s great. And I know as we get into this, some of the factors, you know, there’s massive demographic shifts underway and, of course, we still got the COVID global pandemic going around and it’s caused many manufacturers and producers to re-examine their business practices and undergo these digital transformation initiatives. What are you hearing from some of our customers about the challenges they face in these initiatives?

Alessea: Three of the main challenges that we’re seeing which have become more prevalent now especially with COVID and people needing to work remotely are having siloed IIoT architectures. So not being able to manage all of your data in one central way, also not having visibility to the data. A lot of that is maybe down at the control network level, where it’s only accessed by a few power users and it’s not always reaching those decision-makers that need that. And the third one that we’ve seen a lot is just the inefficient workflows. Especially with people having to change the way that they work, they need access to that data and they need ways to be able to fix the issues, whether they’re experts or there’re at the site or if they’re working remotely or at a different site at that time.

Jim: Okay. So, of those three challenges what are some of the reasons behind them?

Alessea: With the OT architectures, so typically, you might have different data that’s spread across different systems. There’s different teams that are monitoring and managing this data whether it’s the DCS, the historian, the lab data, or the maintenance data, and they need different tools to be able to bridge the connectivity gaps between that data and the application. Having those different personas in different teams responsible for managing the data and having different end needs, leads to different tools that they use. By having all of these different tools, it’s just becomes very difficult to manage in one central way. That’s kind of the first one on the OT architectures that we’re trying to address, and some of the other ones so as far as having the visibility to the data, so I mentioned that a lot of this data is at the control system.

If you think about our instrument asset management system, our AMS Device Manager, it has a lot of this smart HART diagnostics but it’s hard to access. It’s locked down to only certain users. Especially with people not on site, they have more difficulties accessing that data.

And the third one about the inefficient workflows, so the responsibilities are typically shared across multiple different departments. You have different groups that are collaborating on solving issues depending on what that might be. And if they don’t have consistent work practices that are shared across those different departments, if they don’t have those resources and those knowledge base articles documented to share the expertise, they can get locked with just a few individuals who really know what’s going on. It just becomes hard to capture and spread the knowledge across all of those different people who need it.

Jim: Wow. That sounds like a lot of reasons behind some of those challenges that keep people feeling stuck. What are some of the possibilities now to be able to address these challenges different from the ways they’ve been doing in all these years?

Alessea: Instead of having multiple different tools to manage your data and get it from their sources to those end applications, we really need one central way, one central tool that can handle all of those different data types. Different data types whether it’s structured or unstructured, time series, if it’s photos or images, lab data, having one tool to account for all of that, and the different update rates as well. Being able to manage how quickly you’re getting that new information. That’s where we use our Plantweb Optics Data Lake and all of its connectors to be able to centrally manage all of your data, and then also contextualize it for use in other applications. For the visibility, so just actually once you’ve collected all that data, as far as getting it delivered to those other users, so we have different options to send that data whether it’s read-only.

So, making sure that it’s still secure, you don’t want everybody to have unlimited access to the data, but getting it in the format that’s secure and also contextualize for those users to be able to use it in tools for visualization and taking action. And then on the workflows, so different tools available for that to kind of progress us into this age of digital transformation. We’ve seen more adoption of tools like augmented reality, where you don’t have to be at site, you can call a remote expert and they can walk you through exactly what you’re seeing or using tools like CMMS [computerized maintenance management system] integration, where you can turn all of those alerts and insights that you’re already collecting into actual work requests and speed up some of those processes.

Jim: Exactly how does the Plantweb Optics Data Lake provide this connectivity, the data management, and also be a repository for all these different sources of data?

Alessea: We have a variety of different connectors to connect to these OT [operational technology] data sources. Both native connectors that are custom built to deliver the exact information that you might need based on Emerson’s domain expertise or other third parties. And also those open protocol connectors like OPC UA, and MQTT to be able to account for all of the different data sources you might have, and beyond just collecting all of the data, having abilities to throttle and hedge that so that you can automize that data transfer so that you’re able to get the data at the rates that you need without disrupting your operations. Keeping those systems running but accessing the data as well. For the data management side of it, so we’re able to handle any type of data. I’ve kind of mentioned this by having the unstructured and structured data.

So not just time series, but some of that data that’s kind of in between and maybe hasn’t had a home in the past, you’re able to correspond that with your time series data and see the full picture. You’re also able to make that data relevant so you can add the hierarchies, so you understand exactly where that asset is, how it fits into the overall picture of your facility, and also understanding the data modeling. Whether there are specific KPIs that you need to build off of that and understanding how that impacts the greater operations for your facilities.

For the data repository, so we use a MongoDB, which is a very flexible on database architecture. It’s used by Netflix to stream. So it’s very scalable and flexible, infinitely, scalable if you really need it to be. And it allows you to securely store all of your data and also optimize the way that it’s stored. And then also, once you’ve connected the data and you have it in your repository, going back to just getting the data out again. Egressing the data with multiple different options, sending it to the cloud, different IT data lakes, or any of the visualization tools and reporting that your users might need.

Jim: Okay. And you had mentioned some of the industry standard ones like OPC UA, and I’ve seen it’s a pretty long list of different ways that you can connect in. Can you just list off some of the other popular ones or ways people are connecting in through the connectors?

Alessea: We also have an OSI PI bridge, so if you’re using that for your historian, we can connect to that. We can connect to Honeywell PHD, which is another popular historian that our customers use. We also can support ODBC to pull from SQL databases, OPC classic which will be OPC DA, HDA. And then also all of our Emerson sources like AMS Device Manager and AMS Machinery Manager, Plantweb Optics Analytics, and Plantweb Insight. We have a host of those open protocol connectors for just the industry standard, and then also some of those customized solutions for our Emerson products.

Jim: I can see it all coming together from different places in there. So, what are some of the capabilities in the application that can help with improving operational performance?

Alessea: It really across the Plantweb Optics Platform it comes down to three different components, which are the data, the insights, and the actions. Providing a way to collect the data in that one central repository, which is the Plantweb Optics Data Lake, and then also providing the insights. Whether you’re using simple calculations and simple logic on that data, or if you need more advanced artificial intelligence and machine learning, then we have those capabilities as well with Plantweb Optics Analytics. And then the third one is about taking action. To really reach that prime operational performance, you need to be able to resolve issues quickly and have the tools to do that. We have tools with CMS integration and collaboration, giving a space for all of your users to have access to the data and work together on resolving those issues. And then other ones like the mobile app and augmented reality.

Jim: Wow, that makes sense. You collect the data, you bring it together, and get it to the right person to do something about it versus the way we kind of opened and the challenge of it being isolated and everything else. That sounds a lot like a transformation going on for people implementing it. Where do our users find the most success with implementing a tool like Plantweb Optics Portal?

Alessea: Really where we see the most success is where customers actually build this into their work processes. A tool like Plantweb Optics Portal, it gives you more visibility into the health of your assets. And it’s delivering information from all of these vase monitoring systems. Without it, you could go to these multiple different systems. You could have different people who are looking at that troubleshooting information and they could still technically do their job, but they would do it a lot slower. They might look at those systems once a week, once every two weeks or something, but it’s not a constant monitoring strategy. With my Plantweb Optics Portal, you’re elevating that by having that information readily available in the palm of your hand. You can get those notifications on your mobile app; you can see exactly when there’s alert that comes in so that you’re able to start troubleshooting it earlier.

Imagine sitting in whether it’s a daily or a weekly planning and trying to identify which assets need attention, so planning your maintenance around those different assets. Now you have a clear indicator of which assets are unhealthy, which ones have active alerts, and need to be looked at what’s their criticality. So of those assets, which are the most important, which ones are bad actors, they might have recurring issues. Really giving you a way to centralize all of the insights that you’re collecting in those monitoring systems, easily identify bad actors, and then take action on that.

Jim: It sounds like you’ve got the technology platform and using it changing the work practices out to make something happen. I guess can you share an example of how Plantweb Optics has been applied and some of the operational improvements seen?

Alessea: We have customers that are using Plantweb Optics Data Lake and all of the connectors to access the data from their different industrial applications. This data that was disparate and kind of breaking down those silos and getting it in one central location and also doing that at scale so having that scale-out across multiple sites and needing a way to do it efficiently. And those use cases that we typically see there, they can range from process and equipment reliability to safety, production optimization, so a variety of different ways to actually use the data and what we typically see. For one example, with that reliability use case, they’re using Plantweb Optics portal once they’ve collected the information in Optics Data Lake, they can use analytics to drive those insights and give them the root cause for issues as they’re coming in, give you the failure modes and effects analysis, so you’re able to see the bigger picture and understand why there’s an alert and what the root cause is, and then use Optics Portal to resolve those issues.

So having those push notifications and email, and really where we see the most benefit is just getting that information sooner. When you have these predictive alerts, they’re telling you not that a pump is already failed, but they’re telling you that a pump is going to fail. And they’re able to tell you the exact reason why that pump is going to fail. I know this is one of the examples we had in that video, just kind of tracing back the root cause for why a pump might not be performing at its best. Having those prescriptive actions and that diagnostic information delivered to you proactively and being able to solve the issues before they become bigger issues that impact production and costs downtime.

Jim: Yeah. It seems like if you’re getting a predictive alert, yet it takes you a long time to get to it, yet you may miss that window to do something about it before it affects production levels or something else. Well, let’s start winding this down. I always like to ask, you know, we think of some questions, but what haven’t I asked you that I should have asked you?

Alessea: One of the topics I touched on a little bit, but we didn’t get into too much details. We have another product Plantweb Optics Analytics. And this is where we really embedded a lot of our expertise. So, those failure modes and effects analysis, the root cause analysis, the artificial intelligence, and machine learning. So, a lot of those capabilities are built into Plantweb Optics Analytics and expanded on in there. That’s another really great part of the platform. And I know that you’ll probably be speaking to our colleague, Michael Tworzydlo on that one in an upcoming podcast. And that’ll also tie in very nicely with what we talked about today.

Jim: Yes. I will try to wrangle him here in a few weeks and put him on the spot. Like I’ve been doing you today, but you’ve been a fantastic guest for the podcast. Let me just close it out by asking, where can our listeners go to learn more and how can they connect with us on any specific questions they might have or go for more information?

Alessea: You can always find more information on our website, on the Emerson website. There’s a Plantweb Optics section, which has a lot of information about the topics we talked about today. We also have YouTube channels with some videos and, of course, LinkedIn, if anybody is ever interested in connecting and learning some more about Plantweb Optics, you can find me on LinkedIn.

Jim: Well, Alessea, this has been really great. You’ve imparted a lot of knowledge. I think I’ve learned a few things. Thank you so much for joining us today.

Alessea: Yeah. Thanks for having me.

-End of transcript-

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  • Alessea Lane
    Product Manager, Plantweb Optics | Digital Transformation

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