As a global society we rely on machines so much that it’s easy to take them for granted.
We rely on machines to ensure water comes out of our faucets, heat our homes and businesses, fill our cars with petrol or electricity, construct and maintain roads, transport people and goods, provide medical images, and manufacturing more machines. Businesses and consumers rely on machines so much that exponentially more will be built, and their designs and operational performance will need to last longer without compromising safety. There is plenty of data available to help make this happen as “machines now generate one million times more information in one day than all humans on this planet do in an entire year.”1
The ratio between man and machine
All industries use machines, yet the energy and resources sector including utilities, oil and gas, chemicals, and metals & mining industries is the most capital intensive.
In this sector, the ratio of gross plant, property, and equipment (aka. ‘machines’) published in any asset owner’s annual report compared to the number of employees (aka. ‘man’) on average is $2,000,000 of gross PP&E per employee. In comparison, all other industries average $200,000 of gross PP&E per employee. A difference in magnitude of 10x.
The ratio between ‘man & machine’ will continue to rise for all industries and especially across energy and resources sector because of the vast amount capital investment in new machines. Capital expenditures for the energy industry alone is estimated to grow 49% over the five-year period from 2021-2025 compared with the previous five years.2 Taking a much longer view, $3.5 trillion of annual capital investment is made in the energy industry today and by some estimates will grow to $9.2 trillion by 2050.3
Digital twins will play an increasing role in improving operational efficiency and safety
Humans require machines. And machines require humans. I’m passionate about this relationship having been spent a decade of my career in field services (the human side of the equation) in the energy sector and another decade in industrial process instrumentation (the machine side of the equation).
Having entered the software industry five years ago with a majority of that time with OpenText, it opened my eyes to the important role software and more specifically the information management software domain plays in connecting man and machine. Just as both humans and machines need to be managed to achieve their highest performance, so does the information that is generated or used by either one of them. The better that information is managed, the digital representation of the machine (or digital twin) and the workflows that directly support their operational performance will be more trusted, autonomous, and secured. Moreover, adding in the power of AI and LLMs, the value of digital twins can be maximized to confidently predict and safely act on machine performance.
Digital twins of assets, equipment, and supporting workflows will play an increasing role in helping improve operational efficiency, reduce costs, minimize risks, and predict machine failures before they occur. McKinsey & Co estimates that the global market for digital twin technologies are forecasted to grow at 60% annually over the next five years reaching $73.5 billion by 2027.4
Information reimagined, or in this case digital twins reimagined, is to extract the most potential out of both man and machine with i) trusted information with governance over large data sets ii) next generation autonomous cloud – utilizing software to safely automate mundane tasks and minimize human errors iii) AI and security everywhere – at scale, with enterprise strength. Creating digital twins at scale will be needed to safely manage substantially more machines per employee in the years to come.
Digital twins reimagined with information management
It’s often misconceived that a digital twin of a machine is simply a function of its real-time sensor streams and a predictive model that learns what good, bad, and cautionary performance looks. While a digital twin certainly should incorporate real-time measurements of its performance, there are many more aspects to creating a true digital representation of a machine.
Financial systems of record such as ERP contain the information to create a digital twin of the machine’s financial record over time. Asset maintenance systems of records such as EAM contain the information to create a digital twin of the machine’s maintenance record over time. GIS applications contain the information to create a digital twin of the machine’s geospatial record over time. These examples of digital twin characteristics are very important to creating digital twins, yet still contain information gaps to create digital twins at scale that are trusted, autonomous, and secure. These software applications are designed and specialize in structured data (organized in rows, columns, and tabs) within their respective domains and act as vertical threads of a digital fabric representing a digital twin.
Every fabric has horizontal threads and is true for digital fabrics as well. Below, are seven information management components that act as horizontal threads across a digital fabric and create a trusted, autonomous, and secure digital twin of a machine across its lifecycle at scale.
Knowledge reimagined
- Content: Equipment manuals, product data sheets, safety manuals, work orders, installation images, etc.
- GenAI: Intelligent assistant to quickly find answers to questions contained in asset documentation.
Connections reimagined
- Business Network: Secure sharing of machine sensor and EDI information between owner, manufacturer, and field service providers to automate supply chains and predict failures.
- GenAI: Virtual advisor to quickly access when spare parts, replacements, or service will arrive or surface any other information about the vendor transactions that impact a machine.
Decisions reimagined
- AI & Analytics: Enrich asset documentation, analyze asset imagery for hazardous conditions, and big data-analytics on machine performance.
- GenAI: Predictive machine analytics at scale.
Conversations reimagined
- Experience: Elevate the discussions around assets via drone videos, technical support call quality management, crowd sourced information for distributed assets, and more.
- GenAI: Create tailored content to inform machine owners on recommended service to meet SLAs.
CloudOps reimagined
- ITOps: Service management and network operations management to speed up device monitoring, configuration, and resolution time.
- GenAI: Virtual agent for quick issue resolution leveraging knowledge from service tickets on similar devices.
Security reimagined
- Cybersecurity: Defend against the most sophisticated cyberattacks on energy & resource infrastructure.
- GenAI: Behavioral based cyber threat hunting and detection.
DevOps reimagined
- DevOps: Streamline the deployment of software that enhances digital twin creation and representation of an asset.
- GenAI: Faster application delivery, development, and automated software testing to improve the quality, reliability, and scaling of your digital twins.
Are you ready to take action?
Learn more about OpenText solutions for Utilities, Chemicals, Oil and Gas, Metals and Mining and Engineering, Procurement and Construction that can help you work smarter.
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1 Versant: Decoding the OpenVerseTM, Mark Barrenechea
2S&P Global, Upstream capital expenditures outpace cleantech, but for how long?
3McKinsey & Co, Capital projects are critical for a green future.
