The digital transformation of infrastructure asset industries including power and energy, oil and gas, and chemical processing, involves managing an overwhelming volume of dynamic data associated with designing, building, operating, and refurbishing complex engineered systems. Because this data is scattered across the organization and siloed within various departments, it can be challenging for these businesses to drive collaboration, identify improvement opportunities, and create overall business value. In many cases, teams end up piecemealing data together throughout the product lifecycle, only to find that they must do it all over again when they make a single change to the product or produce a variation.

Many of these businesses are turning to a digital thread approach to solve the challenge of managing complex assets. A digital thread connects data across the product lifecycle to make it interoperable, reusable, and traceable. When done correctly, a digital thread can scale up and centralize an entire system of assets, making it an invaluable practice for capital-intensive businesses.

In our latest episode of Pulling the Digital Thread, we brought together five experts to discuss the unique challenges and opportunities these capital-intensive industries face as they implement a digital thread approach. The speakers included:

  • Guy Bursell, business strategy leader at Microsoft
  • Philippe Gautreau, managing director – Industry X at Accenture
  • Dr. Philipp Ruffing, head of Offshore System Design at Amprion GmbH
  • Greg Pada, VP head of Engineering Business at AVEVA
  • Jason Kasper, senior director of Product Marketing at Aras

Read on to catch a few highlights from their invaluable conversation or skip straight to the on-demand recording to hear the entire presentation.

Data connectivity helps companies adapt to change

During previous conversations, we mentioned why a digital thread is critical to a manufacturing company’s present and future success. But why does interconnected data matter to asset industries specifically? The speakers mentioned a few reasons these businesses especially need to consider a digital thread strategy.

Greg Pada stressed the importance of transitioning from a document-orientation to a data orientation. He says, “I’ve been on projects where you have hundreds of document binders that get handed to an operator who then has to go make sense of these things to operate a facility… and then that facility will only look like that for about a minute before somebody makes the first change to it. And then they can’t remember why they did it the way they did it and what the requirements looked like. Plus, if somebody wanted to duplicate the process, they’ve got no idea exactly where to start from either. So, all of the value created during the design phase gets lost.”

A digital thread approach also drives knowledge sharing, which improves operational efficiency and facilitates company-wide innovation. Dr. Philipp Ruffing explains, “Once you need certain expertise in another project, it’s very important to have all the information on how [the original product] was designed and which decisions were made throughout the project.”

The challenge of implementing a digital thread at a large scale

Implementing a digital thread approach for a large-scale system of assets can be challenging. Philippe Gautreau brought up four common challenges that his team at Accenture notices when speaking to clients:

  1. Ongoing energy transition initiatives, in which companies must rapidly transition to new facilities while maintaining aging infrastructure in the background.
  2. Complex operations, in which companies must deal with a wide variety of data types and constant change but cannot keep up with everything.
  3. Lack of digital maturity, in which companies must move to a data-centric approach but cannot properly access all of their data to make this change actually happen.
  4. Lack of cross-functional alignment between operations and maintenance, which makes it challenging to establish a foundation of data interoperability and traceability.

For these capital-intensive organizations, it can be one step forward and two steps back as they work to address these challenges but face new changes and setbacks along the way.

How to answer these challenges

To answer these changes and successfully build a digital thread foundation, capital-intensive businesses must lean into two key tenets:

1. Data context

Organizations should consider how to keep the data and its context together in an organized way. A digital twin accomplishes this by telling the complete story of every asset and managing configuration changes along the way.

Jason Kasper explains, “A digital twin for specific assets in the plant gives you the complete story of each asset, as well as the plant itself…The example that I always hear used is, ‘Is [the problem] with a valve or the plant’s design?’ Then engineering says, ‘Maintenance, it’s your fault because you don’t have an effective maintenance strategy.’ Then maintenance says, ‘Well, I keep fixing it, and it keeps breaking because there’s a problem [earlier in the process].’ They can never trace that information far back enough to have a valuable conversation and maybe change the strategy at the plant. A digital twin becomes that capability to communicate throughout the organization.”

However, getting to a point where an organization can use AI-driven tools takes some level of data connectivity and centralization first. Kasper says, “Our customers are looking at [digital thread] beyond the collection of data to creating the context with the data, which ultimately supports AI strategies.”

2. AI-driven tools

As companies move towards a digital thread approach, AI tools play an important role in synthesizing and using product data. Bursell says, “Many of our customers say, ‘We’ve got these binders full of schematics, or ‘we’ve got PDFs.’ Generative AI is opening up a really interesting value proposition for extracting insights and knowledge out of those papers, schematics, and 2D PDFs.”

Ruffing also mentions that AI can play a role in pulling requirements out of static documents. He says, “Let’s imagine you have a contract with clauses and requirements. If you printed those thousands of pages, it would be really hard to know what’s in there or…to see how those clauses and requirements intertwine. That is something that generative AI can support very well.”

However, getting to a point where an organization can use AI-driven tools takes some level of data connectivity and centralization first. Kasper says, “Our customers are looking at [digital thread] beyond the collection of data to creating the context with the data, which ultimately supports AI strategies.”

Learn more about digital thread for systems of assets

While creating a digital thread for an asset-intensive product lifecycle can seem overwhelming, there are ways to overcome these challenges. Building out stronger data context and leaning into AI strategies can move your business toward a successful digital thread approach.

To learn more about how these strategies work in capital-intensive industries, tune into the entire conversation, Revolutionizing Capital-Intensive Industries, today.