Research on digital twins from 660 executives reveals 19% cost savings and revenue gains. This podcast explores their strategic role in asset-heavy industries and the shift to Smart Digital Reality. Explore the profound impact of digital twin technology on business value across 11 industries globally by downloading Hexagon’s Digital Twin Industry Report here.
BK: Hello and welcome to today’s podcast episode brought to you by Petroleum Economist. Joining us are Hans Kouwer, Director of Industry Consulting, and Giulio Cattarin, senior industry consultant from Hexagon’s Asset Lifecycle Intelligence Division. In this episode, we’ll explore how the next generation of digital twins is set to play an even more strategic role in tackling critical challenges faced by asset intensive industries. This podcast series is presented in collaboration with Petroleum Economist and is also featured on PE Live broadcasts. We hope you enjoy this episode.
Host: Welcome to the Petroleum Economist podcast, where we explore cutting edge technologies shaping our future. Today we’re diving into digital twins, a technology that generate a lot of interest in the last years.
Joining us are two experts, Hans Kouwer, industry consulting director for the Hexagon Asset Lifestyle Intelligence Division in the Europe, Middle East, India, and Africa region. Hans has over 30 years of experience in the process industry and joined Hexagon 16 years ago, after many years of industrial experience. Hans advises business organizations on how their day to day business processes can benefit from effective information management and digital transformation.
Our second guest, Giulio Cattarin, has seven years of experience in digital technologies for industrial operations and focuses on solutions for information management, connected workers, and asset performance management. He holds a master’s degree in mechanical engineering and a Ph.D. in energy efficiency and buildings.
Together Hans and Giulio will unravel what digital twins are, their practical applications, and their impact on business and society.
So without further ado, let’s start to explore today’s topic. First of all, Giulio, what do we mean by digital twins? Are we talking about the same thing here?
GC: Thank you very much for the introduction, Paul. It’s a pleasure to be here.
So the term digital twin has been used in many areas and has become a catchall. A digital twin is usually defined as a virtual representation of a physical asset. But the definition is open to interpretation. It depends on which aspects of a physical asset we need to represent to serve a specific business purposes. For example, a chemical company may want to model their production processes while a gas distribution company may want to optimize the way they dispatch maintenance teams and be able to react quickly to volatile gas prices.
Host: Okay. So is it correct to say that there are various angles that you can look at digital Twin from?
GC: Exactly. We can have all these twins intertwined, the physical twin, the process twin, the geospatial twin and so on. That’s why at Hexagon we are moving from the classic definition of the digital twin to what we call a smart digital reality, which draws data from both the real world and the digital world. The real world is described by data acquisition devices such as laser scanners, photogrammetry technologies and the real time data from process historians.
On the other side of the equation, the digital world comprises engineering data from 1D data sheets to the documents and drawings and the 3D models.
Host: In your examples, you mentioned chemicals and utilities. Hans, do you have other examples for us? Which other industries have adopted these technologies?
HK: Hi, Paul. Yes. Well, of course digital twin technologies have been around for over 20 years and until recently they were mainly adopted by some large players in the oil and gas and the chemical and power industries who needed to process large amounts of structure and unstructured data. But also they had the resources to devote to information management and data governance. So today the need to adopt these technologies is being felt in many other industries and by companies of all sizes, including EPCs and general procurement construction companies, as well as owner operators and utilities, pharmaceuticals, metals and mining, food and beverage and many other industries.
Host: And why are we witnessing this wider adoption of digital twins? What are the main drivers here?
HK: Okay, I can see multiple drivers, so that’s divided up into internal and external divide essentially. And driver. So if we start with the internal drivers, I don’t think I need to remind anyone of the massive acceleration on how much data we create. Many companies are actually getting overwhelmed by the amount of data and a variety of data types. The digital twin or small digital reality, provides easy access to transfer for managed information and context, which is retrieved from this data. So this information is needed to support the decision making and to support activities. So what’s more, companies are losing knowledge as the experienced workforce retires and they struggle to attract new talent so that that’s another driver. So they need to change quickly the way they work and the way they retain knowledge within the organization.
So those are internal factors, but also mentioned the external factors. So companies need solutions to stay competitive and comply with regulations. And also, of course, carbon reduction targets. So environmental objects are no longer a luxury. Now they’ve become a necessity to attract investment. And also for this, having evergreen up to date information is absolutely key.
Host: On this point I saw that Hexagon recently published the results of a survey on digital twins. Could you tell us what emerged from this?
GC: Yeah. We recently surveyed 660 senior executives across 11 industries to better assess the market’s predisposition towards digital twins. What we found is that eight out of ten respondents see digital twins as a key way to reduce carbon emissions. And by the way, investing in digital twins is not purely driven by sustainability objectives. The ROIs are very healthy, with half of respondents reporting ROIs over 20% and often as high as 40%.
Host: Interesting. How do digital twins help support sustainability initiatives?
GC: Digital twins can help identify problems early in the design process and avoid the rework and material waste later in the project. For example, by automatically comparing engineering data from different disciplines. The thing is, current practices of concurrent engineering do shorten their project lifecycle, but they also increase the risk of errors and miscommunication between the different teams involved. A valve may be represented as four inches on a BNID, but six inches on a 3D model.
So what we enable design teams to do is to highlight and resolve these inconsistencies before they can impact the fabrication, construction and installation phases. By centralizing all the engineering information on the digital twin, EPCs can work more efficiently and get it right the first time.
Hans, maybe you have other examples in mind as well.
HK: Oh, yeah, Giulio, I do indeed. You have given us an excellent example concerning the design phase of the project. Digital twins also support sustainability and operations, obviously by reducing the need for paper documents and visits to remote sites to plan maintenance activities or to train staff. Think of offshore oil and gas platforms, wind turbines, remote mining sites or any other geographically dispersed assets. How often do maintenance technicians arrive on site only to discover that they haven’t brought the right part or the right tools with them? So digital twins give operations and maintenance teams access to reliable information on how an asset is configured, including all model and parts used, which also helps to promote a repair rather than replacement of equipment.
Host: Okay. So I guess a digital twin would need to collect information from many sources, right?
HK: Absolutely true, Paul. So digital twins can’t exist in isolation. They are linked to many other enterprise systems such as EOP systems, CRMS, EAM, APM and operational excellence suites. And all these systems, they rely heavily on having access to up to date facility configuration information. But yeah, they are not necessarily the best in managing these data and documents themselves. Therefore, interoperability and easy access to the digital twin and the smart digital reality is very important. And this all in support of organizational business goals like obviously productivity, efficiency, quality, safety and of course sustainability, and that all these systems play obviously a part in reducing the carbon footprint. For example, make the systems help to monitor and identify early warning signs of potential asset failure and to avoid catastrophic failures that can endanger people and the environment.
Host: Right. But isn’t the digital ecosystem becoming too complex for users, too many applications to master?
HK: Well, yeah, that’s a good point. But yeah, digital twins address exactly the challenge of mastering many applications. So then the solution to that, they offer a single point of access to a company staff of information file that clients so users don’t need to install several applications on their workstation in order to familiarize themselves with several user interfaces. So all the information is presented in a role based single pane of glass information at the user’s fingertips without having to worry about where the information is actually coming from. So digital twins provide access to engineering data and documents to everyone in the organization instead of only to the engineers and the designers.
Host: And Giulio, can I bring you in? Can we say that digital twins are visualization tools? Is there more to them?
GC: Yes, there’s much more. The apparent simplicity of the digital twins navigation experience hides a sophisticated data model that links all the objects in the system. Equipment, tags, documents, 3D models, point clouds, work packages, change requests, you name it. And without a rock solid data model, digital twin platforms could be just another adapter like where information is dumped and becomes impossible to search and navigate. What’s more, true digital twins are not limited to a point in time snapshot of a plant. They must allow users to go back in time and consult the complete history of each object and each document.
HK: Yeah. And that’s indeed a very important point here, Giulio. So, yeah, the digital twins must become a real system of records and include all the data and documents describing the facilities configuration. Right. And this is this is different from a more traditional document management system. Industrial plants, they are living organisms. And even after commissioning, there are so many changes happening all the time that need to be properly documented and managed. So a digital twin must be able to sustain the change over the whole lifecycle of the plant. Changes like maintenance, modifications, plant expansions, eliminating bottlenecks, a change to the layout. All that requires change and all the information generated must remain available, obviously for traceability and auditing purposes.
Host: Fascinating. So is it a realistic goal to keep the digital twin always up to date, especially when external parties are involved?
HK: Oh, yeah. It’s absolutely a realistic goal. Of course, if approached correctly. So I’m not saying that it’s always easy or that it can be magically solved by software. But software solutions can transform work processes in a way that drastically reduces the manual effort required. As an example, for many years, EPC had to print all the project deliverables and send it to the owner operators via paper binding. So imagine the amount of paper gathering dust and all those physical archives and when one needed the specific BNID or a data sheet, it was nowhere to be found. So the same also for and instead of paper PDFs were created and those were stored on network drives. That didn’t really solve the challenge to not being able to find the latest and up to date version of the documents again.
GC: True. And information handover has always been a tough challenge. I remember reading that it could represent between 1 and 4% of the total project budget. But there are ways to improve the situation, as Hans mentioned. Digital twins can help you orchestrate the handover process so that instead of moving paper binders, you can simply merge the project data with the as built representation of the plant without ever leaving the digital environment. You can also deliver information incrementally as commissioning is completed and the responsibility for care, custody and control is transferred from project to operations. So there are ways to keep a digital twin up to date. Ideally, the contractor should use a design suite that is fully integrated with the digital twin. But failing that, you can digitize and smartify the documentation created with any authoring tool.
Host: You mention the word smartify, Giulio. Could you unpack this term for us?
GC: Yeah. It sounds a little esoteric, doesn’t it? Well, documented smartification is the process of taking a static, unstructured file like a PDF and making it intelligent by contextualizing it and creating meaningful relationships with other pieces of information. For example, a piping and instrumentation diagram or BNID contains a large amount of information in the form of symbols, tags, relationships with the breakdown structure of the system and the metadata such as description, classification, revision and so on.
Host: It sounds like a lot of manual work. Don’t we live in the age of artificial intelligence? Can AI help us with this process?
GC: Yes. In recent years, artificial intelligence has matured to the point where it can help automate many of these tedious tasks such as classifying documents and extracting tags and metadata or even detecting patterns in real time process data to alert operators to potential asset failures. The industrial world is obviously a little slower to adopt these technologies due to concerns about the quality, reliability and the confidentiality. And this is perfectly understandable when you consider that critical decisions can have serious consequences for the personnel, the equipment, the environment and the company’s finances.
HK: Yeah. That’s really a good point, Giulio. AI shouldn’t be a black box in industrial settings, right? You should always be able to verify the data sources that the AI used to make the recommendations. We are working on the conversational user interface, which not only allows you to query the digital twin and natural language, but also it responds by indicating the source documents used to construct a response. So, yeah, that’s an important difference from publicly available generative AI platforms such as ChatGPT.
Host: Hans, you talked about the application of AI as interacting with the digital twin using natural language. But do you think the human will still be needed? Won’t AI replace most of us in the industrial workforce?
HK: Well, we think that artificial intelligence will accompany humans for years to come, but I don’t think it’s going to replace them, or at least not for the most part. There are companies that have already reached an advanced stage of digital maturity that allows them to operate some of their plants unmanned. But this, yeah, that’s at the moment the exception rather than the rule.
Host: True. I guess there’s always been those scare stories around digitalization. I guess it’s a very gradual process, right?
HK: Yeah. Most industrial plants are indeed, they are aging infrastructures. They are built 20, 30, 40 or even longer ago. And that was long before we had digital solutions, even to mention AI available. Right? So in the report that Giulio mentioned earlier on, we found also that that sectors for AI a lot when it comes to adopting AI, so the automotive and the EPC sectors are far ahead of industries like, for example, mining and petrochemicals. So, yeah.
We at Hexagon, we take a pragmatic approach to digital transformation and help our customers identify where automation can bring real value, easing the workload and reducing the risk. And artificial intelligence has an important role to play, but it’s not the be all and end all of technological innovation.
GC: Yeah. I totally agree. In conversations on digital transformation, we sometimes forget to talk about the importance of a solid data foundation, which is what enables not only AI but also more efficient communication and collaboration, better processes, more user engagement, safer workplaces, and so on. Companies that can offer their employees and their partners access to centralized, contextualized and up to date data are already ahead of the competition.
Host: Okay, so if a company wants to get started, at what stage should a digital twin be established?
GC: Well, as early as possible, ideally during the conceptual design phase. The reason for this is to ensure that data is captured early and validated and also to avoid the recreating data and documents due to suboptimal handovers between various project phases. But as we said, the digital twins also allow existing documentation to be collected and structured, so it’s never too late to start, really.
Host: And Hans, would you have some final words for our listeners?
HK: Oh, sure, yes. Well, digital twins are obviously very well established and have proven their business value. So we hear that in many conversations with our customers and prospects. And we don’t hear them any longer speaking about if but rather how to implement the digital twin. And as Giulio said, digital twin initiatives can begin at any point during the asset lifecycle. So yeah, companies can start small and extend the scope of their digital twin as new use cases and opportunities present themselves.
Host: Thank you so much, Hans and Giulio, for joining us today and offering your insight on digital twins. Thanks.
