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Digital trends transforming capital-intensive industries: Part 3

Updated: Apr 26, 2023

In 2023, digital transformation in capital-intensive industries is accelerating faster than ever. As new technology trends constantly arise and new buzzwords pop up, it's hard to understand what will provide the most significant benefit to your organization. How can you discern which new technology will help you alleviate the common barriers to digital adoption? In this three-part series, we will look at the most important trends in technology that are working to alleviate barriers to digital adoption and help you fully embrace the opportunities of digital transformation.

  • In the first post, we introduced the six trends in tech working to alleviate common barriers to digital adoption and acceleration faced by capital-intensive industries.

  • In the second post, we reviewed those barriers in detail and focused on affordable data capture, cloud-first and cloud-native viewing, and data aggregation.

In this post, we’ll look at the final three current trends enabling faster digital adoption and acceleration by industry; data contextualization, machine learning, and data abundance. Then we’ll consider the opportunities of agile asset management with digital twin technologies.


4. Data contextualization

Data contextualization allows you to bring all aggregated data together and provide context back to the 3D visual or 2D visual of your site. This contextualization provides immediate visual understanding that helps reduce the time required to travel to site, freeing up time for more valuable planning and maintenance activities.

Some key value-drivers and capabilities of data contextualization include being able to:

  • See the real-time location and sensor data

  • Bring in the reality capture and view it in the context of the other business information systems

  • Have immersive virtual reality experiences with the data

  • Connect to any of the third party systems that the business has invested in, including work order management systems, IBM Maximo, SAP and more

  • Be more efficient and avoid Simultaneous Operations (SIMOPS) and other challenges in the field.


5. Data aggregation

Capital-intensive industries traditionally have multiple disparate, siloed data sources that result in numerous cost and time inefficiencies, including but not limited to:

  • Gathering all the data attributes and work packages that are normally present on siloed data repositories

  • Communicating with different personnel from maintenance and operations to understand equipment history, performance criteria, and more

  • Aligning schedules, avoiding Simultaneous Operations (SIMOPs), selecting optimal vendors and deciding asset locations requiring supplies, supply sequencing

  • Accessing real time and up-to-date information to create estimates, make forecasts, and keep track of inventories for production goals

  • Aggregating quality exhibits and construction completion records, including outstanding punch items and deficiencies

  • Locating, monitoring, recording, collecting, printing, scanning, and uploading information for further analysis and distribution

  • Producing reports, getting required approvals, and sharing information with concerned stakeholders

Data ingestion, aggregation, and unification is a core and critical benefit of a data rich digital twin. Business information systems and data that is locked in third party systems and applications can be unlocked and made available within the context of site enabling:

  • Easy access to and visualization of all unified data through a single web-based interface

  • Efficient, accurate, safe remote working conditions for the workforce

  • Business continuity

  • Real time monitoring of progress towards attaining reduction targets



6. Machine learning

Machine learning no longer requires years of effort to implement.

Machine learning helps asset owners quickly understand what’s in the field. This makes it easier to identify potential problems before they impact operations and safety, including potential emissions. We can train machine learning algorithms to recognize abnormalities based on ingestion of historical data that has been classified as normal or abnormal. After training and testing, the machine learning algorithm can monitor incoming telemetry and create alerts when it detects anything that falls outside of the learned normal range.

Some of the machine learning and deep learning algorithms may require high computational power not supported by your local machine or laptop. In these cases, a virtual machine on a cloud platform can provide you with the required power. Virtual machine learning enables the build of data rich brownfield digital twins and remote work solutions for teams.

Visual machine learning is available ‘out-of-the-box’ in many cloud applications, and visually labeling data to achieve business outcomes is easier than ever.


What’s next?


With an understanding of the common barriers to digital adoption, and knowledge of what’s happening in the digital tech landscape, we can now look at specific technologies that have an immediate and positive impact on capital-intensive industries.


Our next three-part series looks at the opportunities of agile asset management with digital twin technology, including increased efficiencies, optimization of production, improved safety, and corporate sustainability.



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