Digital Twin Technology & Platforms

Digital twins can be deployed across many diverse fields that involve complex processes and sophisticated machinery or systems — including chemicals, oil & gas, mining, power, nuclear, pharma, healthcare, automotive, shipping, renewable energy, and smart cities.

The type of digital twin deployed depends on the end-user's requirements and intended use cases. For complex machinery, for example, a digital twin can be used across the entire product lifecycle — from prototype and design evaluation, to improving manufacturing techniques, to monitoring asset performance and reliability — delivering benefits at every stage of a machine's life.

A model and a digital twin aren't considered the same thing. A digital twin can only come into existence once its IIoT-enabled physical counterpart is created. Without a physical twin, the digital entity is just a model — in other words, a physical entity is a prerequisite for a digital twin to exist.

At the product design stage, a digital twin only comes into existence once a physical prototype has been developed. Data gathered from the prototype can then be used to update the digital twin, simulate performance, and generate further data to refine the prototype design.

Traditional simulators are driven by complex mathematical models that attempt to realistically imitate a system, process, or piece of machinery that doesn't yet exist. Simulations are useful for understanding how a physical entity might behave in the real world, but their performance depends entirely on the accuracy of the underlying models and equations.

Digital twins, by contrast, are driven by real IIoT data that reflects how a system is actually behaving in the real world, and the model can be improved over time with real-world learnings. Basic and Operational digital twins mirror the performance of their physical counterpart, while Intelligent Digital Twins go further — running simulations powered by data analytics, AI, and machine learning to deliver valuable insights, predictions, and prescriptions that drive performance improvements and returns.

Traditional simulations are therefore limited in scope, typically useful only through the design phase, whereas an intelligent digital twin can serve the system throughout its entire lifecycle.

A Digital Thread connects data and information generated across the entire product lifecycle within a data-driven digital architecture, streamlining design, engineering, construction, operations, maintenance, and end-of-life processes. This lets organizations efficiently design, operate, and maintain complex processes and machinery over their entire useful life.

A Digital Twin, on the other hand, is a digital proxy representing a physical entity in its current operating condition, allowing stakeholders to monitor and enhance asset performance to improve overall efficiency.

While a Digital Thread can exist without a Digital Twin, it can also provide intelligence and performance improvements to an associated Digital Twin.

In petrochemical and oil & gas plant operations, digital twin technology lets multiple stakeholders monitor the performance of a physical asset through its digital counterpart. The digital twin analyzes sensor data from the physical asset to identify issues, run diagnostics, surface insights, generate predictions, and deliver real-time recommendations — addressing problems with minimal or no downtime. Interventions like these can be extremely valuable for manufacturers managing tight supply chains and highly demanding customers worldwide.

Digital twins can be highly effective in supply chain management for a process or manufacturing unit. As processes are optimized and lean manufacturing is implemented, the scheduling and supply of raw materials can be streamlined using recommendations generated by the digital twin.

Integrating enterprise resource planning software with procurement processes enables optimized inventories of raw materials and critical spares. Failure modes can be anticipated and critical spares arranged without human intervention, while customers can be forewarned of potential challenges and alternate supply chains activated to manage disruptions — delivering excellent service with the support of digital twins.

A facility or manufacturing unit shutdown can sometimes be unavoidable when resolving an issue. When an outage is needed to repair a physical asset, the digital twin can quickly provide maintenance planning and execution teams with the list of spares and consumables required, activate expert vendor services, manpower, and contractors, and supply maintenance procedures and commissioning instructions — all in a very short timeframe. This enables effective planning and execution of complex outages, with good planning leading to safer execution, reduced downtime, and greater customer satisfaction.

In a globalized world, manufacturing facilities are often spread across remote, inhospitable locations. Digital twin technology becomes especially useful when companies need to manage remote installations across challenging terrain and high-risk, hazardous facilities — including oil & gas installations, wind turbines, mining sites, offshore and deep-sea facilities, and nuclear plants — where expert help can't always be mobilized quickly for troubleshooting and repairs.

Digital twins effectively make complex installations "portable," bringing reliable data and intelligence to wherever experts and managers are located to support effective decision-making. This clearly improves safety and reliability outcomes. With economies of scale, large-capacity factories and plants make it cheaper to supply markets worldwide — but any unplanned outage at such large facilities can trigger supply chain shocks that reverberate globally. Digital twins provide a robust way for companies to manage and mitigate these risks.

A common misconception is that implementing digital twin technology is highly challenging. It's essential to demystify the technology and clear up these misconceptions — with the right context, it's easy to show industry executives that digital twin technology, expertise, and execution are actually within reach for most organizations. What matters most is a clear understanding of the technology and its ability to transform the business. Once pain points are matched with the right use cases, adopting and deploying digital twins becomes straightforward.

The processing-intensive nature of cognitive technologies like computer vision, machine learning, AI, deep learning, and immersive technologies leads many organizations to assume the required high-end IT infrastructure must be expensive. In reality, hardware manufacturing has grown rapidly over the past decade, delivering what the industry needs at a fraction of past costs.

Sensor costs — critical to any digital twin — have also dropped by more than 70% over the last ten years, giving organizations a significant cost advantage to take advantage of.

Memory errors have historically been a leading cause of system crashes, which understandably raises concerns about stability. Reliable memory technology is now well established and proven in the market, and ongoing development continues to deliver high-performance systems with strong processors and real-time graphics essential for successful digital twins. Improved fourth-generation hardware is now readily available and affordable worldwide.

Connectivity quality, bandwidth, and latency are still sometimes seen as a challenge, but while that may have been true five years ago, it's largely no longer the case — as evidenced by how widespread these technologies are today. Improved 4G and fiber networks are already sufficient for most common industrial digital twin applications, and 5G will play an increasingly important role in enabling more sensitive applications, such as autonomous driving and remote-operated robotic VR/AR surgeries.

It's important that company leaders and strategy executives aren't overawed by technical jargon and sophisticated buzzwords they come across. Industry experts can help demystify digital twin technology and processes, helping companies understand how digital twins actually work and providing appropriate, trustworthy guidance along the way.

Since a digital twin connects to critical equipment, operating and control systems, databases, and proprietary information systems, some concern around security and potential disruption is natural. However, establishing enhanced cybersecurity arrangements can effectively mitigate these risks with adequate protection.

There's a perceived challenge around attracting and retaining talent capable of managing sophisticated systems and making the most of the insights they deliver. However, this expertise is available worldwide, particularly in the post-COVID era as remote support technologies have matured. Companies specializing in talent acquisition can also help locate the right expertise from anywhere across the globe.

Since the physical assets that digital twins mirror often have long lifecycles in manufacturing, the obsolescence of sensors, components, and processing systems that make up the digital thread needs to be carefully managed and refreshed over time to ensure sustainable performance.

Guidance is available to help companies manage this effectively through asset lifecycle programs, with experts from various domains offering support along the way. As with the adoption of any new technology, apprehensions and misconceptions like these are natural — a trusted technology partner with industry experience can make the journey a rewarding one.

Some of the most common mistakes companies make while adopting digital twin technology include:

• Getting swept up in the hype around the technology without fully understanding its true capabilities
• Failing to choose the right use cases that deliver maximum benefit for their organization — identifying pain points and selecting the right use cases is essential for accurately quantifying and tracking ROI
• Selecting and engaging the wrong digitalization partners — partners need expertise in both the domain and the technology, but cost considerations sometimes lead companies to choose inexperienced partners instead
• Trying to save costs by implementing digital initiatives purely with in-house resources, hoping to build internal expertise for future projects along the way
• Failing to invest in analytics on existing data, which would otherwise support and accelerate the digitalization effort with partners
• Not involving all stakeholders — particularly end-users — at key stages of project development, execution, and operations, along with insufficient attention to knowledge transfer and training
• Underestimating the time and investment required, with cost projections not properly captured in capital budgets, leading to slower execution due to funding gaps
• Expecting unrealistically fast implementation and ROI, when the preparatory work for transformation often requires significant time and effort, supported by proper capital allocation and budgetary flexibility

With the growth of global trade and tighter integration of global supply chains, the importance of digital twin technology across manufacturing and service sectors will become increasingly evident. The drive to stay ahead of competitors is pushing more companies toward adopting these technologies, as they deliver strong ROI and help keep customers and stakeholders satisfied.

In the near future, more OEMs are expected to offer digital twins as a standard part of their machinery and control system packages — an integrated offering that will appeal strongly to reliability-focused companies keeping a close eye on performance and business results. Eventually, digital twins will encompass every aspect of a product's lifecycle and transform the way businesses are managed.