At June’s virtual Institute of Asset Management’s (IAM) Global Conference, Progressive’s Laurent Graziano and Hexagon’s Kevin Price gave an insightful presentation on the subject of moving from legacy to modern and best-of-breed Enterprise Asset Management (EAM), introducing delegates to the innovative HxGN EAM asset management system. Their thought-provoking presentation clearly raised some important questions as a lively Q&A session followed with plenty of audience participation.

If you registered for the conference or are an IAM member, you can view a full recording of the presentation until the end of July.

Alternatively, please watch this video for an earlier recording of Progressive and Hexagon’s presentation. This features a Progressive HxGN EAM power utility case study and an overview of how Hexagon are harnessing the latest technological innovations to power the next generation in asset management – including IoT, predictive maintenance, AI, mobile, digital reality and SaaS.

We really enjoyed the Q&A session, and as it raised some important questions relevant to asset managers across a range of industries, we thought that it would be useful to share them with you here:

Q1: How long will it take for the next generation of EAM to be here?

Kevin: The next-generation topics that we’ve talked about today are already here, to an extent. We are tracking things around what our work looks like, and when we think about that, we think about the qualifications and certifications, we think about what skills we have for that work, in that labour pool. We also think about materials that are there and how can we get to them, and where they are stored. So, a lot of those things are already here in EAM.

Being able to incorporate the intelligence of the history of those types of assets is what’s important. For example, when you have a work view in the future, what you’re looking at is trying to predict perhaps the impact of your infrastructure on weather patterns. The impact of weather to a utility provider is very important; there’s different types of weather patterns that can really affect equipment. You are able to understand what condition the equipment is in because you’re routinely checking; you’re inspecting on a regular basis. Then you can take that information and try to predict failure points so that you have the right people available when that storm is coming. When you can understand what weather patterns are coming, you can actually predict where likely failures are going to occur, and you are able to plan and optimise resources that you already have.
So, to an extent, some of those capabilities are here – they are here today. But I do think that the asset history has to be built, and over time, through implementation with organisations like Progressive, you can take that history, and you can apply intelligence to it. You can apply Machine Learning that you’ve built into the system.

Sometimes components are in separate applications that have to be tied together through integration. What we’re going to start to see, as we start to evolve into the future, is those applications no longer being separated; they’re core and critical to the system. I believe that’s what you’ll see in the coming years: more of these third-party application capabilities start to evolve and consolidate in a more intelligent way into one application experience, one environment experience, even though they may be coming from different places. What matters the most is usability – getting to what the user needs to see and what the user needs to do.

Q2: The next generation of EAM seems to demand a lot of AI and/or Machine Learning (ML) – will the resources needed for implementing this be available in tomorrow’s market?

Kevin: The resources needed to be able to do this are two-fold:
1. The data – metadata, condition data and historical data that’s available and is already there.
2. The resources to understand the data and do something with it – that has to be developed. Interpreting data, the engineering that comes with it – that data science that has to be developed – that type of thing is starting to grow more common right now.
It’s difficult to find data science engineers that understand how to capture and apply data i.e. weather patterns, employee qualification data, and equipment data failure technique. But we are finding that data-scientist roles are starting to be filled.

The algorithms and data for these, are becoming more democratised, and they are coming out more as templates. We can start to apply these templates vertically, i.e. for utilities or energy, for example, applying overall equipment effectiveness ratings, these are ratings that have been around for a long time, and so some of their datasets are being templated. Resources to gather the data have historically been difficult to find and interpret, but as datasets are becoming more pervasive, the results more democratised, and engineers more technically aware, templates are becoming more useful. Technology partners like Progressive can create and apply these data templates for more expeditious implementations.

Laurent: As a company that implements EAM solutions – we have a team of experts that has knowledge of the sector, having previously worked in energy– it’s vital to have people who understand what the client needs in order to make the best decisions. We are also ensuring that we keep up with technologies; our team is training day to day on machine learning and other applications, acquiring new skills in the process.
30 years ago, the energy sector just required systems that would manage preventive maintenance, but now, it’s about using the data in data lakes and repositories to apply them to asset management performance and reliability-centred maintenance. To get those predictions that will increase the availability of assets while decreasing the cost – in order to maintain them to operative condition.

Q3: What are your learnings from change management required to make these systems a success? Infrastructure sectors typically have a wide age profile – how are you managing those people who aren’t too comfortable with new technologies?

Laurent: This can be a challenge in the energy sector, but it can be advantageous to have people on your team with a wide knowledge of the business and experience of these assets, and it’s great to be able to have those extremely experienced technicians being able to share that knowledge about what’s happening, what happened in the past and how they dealt with that issue. Previous knowledge can be captured and used in a best-of-breed EAM, especially when you cross this with the use of mobile devices to use out in the field. But the new technology must be user-friendly – ruggedised tablets offer easy access to new functionality. So, from Progressive’s perspective – what we implement provides tools that are easy to use and focus on the key aspects of their day-to-day operations.

Kevin: It’s about configurability; for HxGN EAM, we develop a high level of configurability that doesn’t require coding for the end-user. Even if there are varying levels of competency when interacting with different technology, whether browser, mobile, or tablet, you need to keep the user in mind. Because that data collection point requires forms to function in a certain way to work, and we understand that, but usability has to be there, so dropdowns and data entry are configured and implemented in a way that makes it a natural extension for the user. So, you can take those varying levels of competency, use the technology to capture the data and get that experience brought in and memorialised – made available to execute within a work process. We work hand in hand with implementation partners like Progressive to make this happen.

Q4: With a lot of discussion about transitioning to cloud – will the next generation of EAM  be SaaS only?

Kevin: Hexagon went completely to a SaaS environment back in 2001, so this all seems a while ago. We think the future will be almost completely SaaS. There are situations, countries and industries that still have on-premise solutions, but the ability to apply fixes and updates to these systems is a lot more difficult. Our application process has the same version on-premise that it does in the cloud, and the application experience is different from on-premise because it’s with different resources. What we find is as we’ve been working for the cloud for over 22 years, the application of those patches have to be done in a certain way that allows a user to experience no downtime, so they’re allowed to focus on the business and not necessarily on the computing environment, configurations and things like that. Many applications are now only releasing updates on cloud, so for better security and compliance in future, the only option may be the cloud option.

Laurent: I completely agree; before the pandemic, we started to see a real change to move from on-premise, and then the pandemic accelerated the cloud transition. So much so that in the past three years, we have not started an implementation that is not cloud.
There’s various reasons for this, people are scattered all over the world that need to access the system seamlessly, there’s also mobile solutions with connectivity enabled through the cloud, and finally, the global EAM solution that we implement can be integrated with other components in cloud, i.e. finance systems or weather data capture, where the forecast and data capture is in the cloud. So, we don’t even think we’re going to implement something that is not cloud-based in the next month or even next year. I think transitioning to cloud is not just a fashionable topic; it’s the reality.

Q5: Why is it important to have best-of-breed asset management?

Laurent: Progressive started out in the energy industry, and for this industry, it’s extremely important for safety – those functionalities around the safety of assets and resources need to be from best-of-breed systems. The energy sector is under extreme stress; what makes a company successful is having additional agility and additional capabilities that a best-of-breed solution provides. Crossing historical data with asset strategy and using that to make decisions in terms of reliability and spending is absolutely key to making you successful.
Kevin: With best-of-breed, there’s an evolution of the maintenance model so that it can incorporate Asset Performance Management (APM) models and methodologies. You can only evolve to APM with applications that are focused on that type of environment. More applications can also integrate together to make a full cross-system process – that’s another design of a best-of-breed system. These aren’t things that take long implementation times to get connected. These connectors can be built and ready to use to connect best-of-breed to best-of-breed. It’s a lower-cost of infrastructure as well because a lot of them are cloud deployments anyway, sometimes in the same cloud data centre environment. Monolithic ERPs are dying out, and there’s a rise of best-of-breed applications starting to knit together to produce something very specific for their environments.

Q6: What are the potential solutions out there that can be made bespoke to work with existing systems architecture to make them inter-operable and digitalise the whole asset lifecycle management process?

Kevin: There’s the view of the asset management world vs the physical world; we can now have a digitalised view of an asset with digital twin technology, whether that’s in tablature, visual, 2D or 3D – something that can be as close to real-time as what’s happening in the physical world as possible.
We can also incorporate what we know of that asset with elements of prediction mapped with historical data. It’s the understanding of that data and applying and overlaying into a digital twin so that we can see their potential failure points, including what will be affected, what will be impacted and what kinds of risks will be realised if they are not mitigated soon. As an extension, you can push asset management out to mobile solutions to make real-world decisions in the field.

These types of rules and decision points and machine learning algorithms will become more templatised, so you will see a lot more in terms of types of assets; for example, for pumps, we can see algorithms to choose from for compressors and actuators, and you can expect to bring these templates into your asset management mix.

Laurent: This is something that we have started to work with, especially for energy; there are common factors across the industry, so we are often considering data templates in deployment to our clients – so that they can use those sources of information for their strategic assets.

To learn more about HxGN EAM asset performance and lifecycle management, visit Progressive’s HxGN EAM page.