Digital twins: a primer for industrial enterprises — Part 2


By Steve Dertien, Chief Technology Officer; Jonathan Lang, Lead Principal Business Analyst; David Immerman, Business Analyst, PTC
Tuesday, 11 August, 2020

Digital twins: a primer for industrial enterprises — Part 2

Many industrial enterprises are deploying digital twins and reporting significant benefits today.

As we saw in Part 1 of this article, digital twins are digital models that virtually represent their physical counterparts. This virtual representation of a physical product, an operational process or a person’s task is used to understand or predict the physical counterpart by leveraging both the business system data that defines it and its physical world experience captured through sensors.

While a unique product, process or person may have a common digital twin, use cases are delivered through ‘lenses’ into this digital twin that are specific to the role and task. Three common use cases for digital twins fall under the areas of product engineering, manufacturing operations and customer service.

A product lens for engineering

Engineers have been discussing the concept of closed-loop product lifecycle management for decades. Until relatively recently, with the advent of smart, connected products, this has been limited to academic discussions. Now, with the new real-world data created by these connected products, the opportunity exists for the first time to understand product usage and behavioural characteristics of individual products or systems of products after they leave the factory. As companies seek to leverage this new source of product insight, they are discovering that digital twin architectures are the best way to create meaningful value from the data. By bringing in real-world data and analysing it in the context of physics-based engineering simulation, product designs and user experiences can be improved according to real-world facts, rather than assumptions.

Key business outcomes

Key outcomes from digital twins in relation to product engineering are:

  • Engineering excellence: Real-world usage data combined with product simulation enables engineers to adapt to changing markets and to optimise future product designs for higher quality, and reduce engineering costs while accelerating time to market.
  • Downstream efficiency: Extending the digital thread to downstream stakeholders can enhance cross-functional collaboration, enabling efficient change management in manufacturing and service processes, eliminating scrap and rework, and reducing lead times. The twin can also be adapted to generate manufacturing and service instructions that can be paired with the product inside and outside the organisation.
  • Success-driven design: Being able to analyse the as-designed versus as-used product data to optimise product requirements can help to better fit customer needs and bring differentiated product enhancements to market before the competition.

A process lens for manufacturing and operations

Operational transparency is a costly endeavour exacerbated by the proliferation of information systems. Insights often go undetected due to the inability to connect the dots between disparate information systems. After building a digital thread of an operational workflow, a process lens can be deployed to combine, analyse and deliver operational insights that blend real-time updates from connected assets and workers with production expectations. The process lens often leverages multiple digital twins to provide a system-wide view of a total operations or manufacturing environment. Through analysis, businesses can adapt and orchestrate operational processes for greater forecasting accuracy and improved operational effectiveness.

Key business outcomes

Key outcomes from digital twins in relation to manufacturing and operations are:

  • Agile change management: It is possible to access customer order and supply chain data and analyse it against current operational configurations to adapt processes — in order to develop production plans optimised for speed and agility. These plans can also be shared upstream and downstream to improve efficiency across the supply chain.
  • Reduced operational risk: Predictive analytics can be enabled to simulate the impact of unplanned operational changes, reducing risk.
  • Optimised production: Combining KPIs and operational insights across production environments creates enterprise-wide reporting consistency and benchmark best-in-class processes to ensure lean operational excellence.

A ‘customer success’ lens for service

The cost of downtime and production delays can absolutely cripple industrial enterprises’ relationships with their customers and severely harm their bottom line — both for producers of industrial equipment as well as users. It is no secret that downtime costs global industries millions of dollars in lost revenue every year. Combining the digital thread and real-time sensor data from connected products, digital twins for service and customer experience help manufacturers move from being reactive to being prescriptive in the way they deliver experiences and outcomes for customers. Machine learning, remote diagnostic capabilities and physics-based simulation all help to drive a greater level of understanding of how a product or service and expected outcomes are experienced by the customer. A digital twin of a product or service procedure in a customer environment unlocks new revenue opportunities and strengthens customer relationships.

Key business outcomes

Key outcomes from digital twins in relation to customer service are:

  • Technician success: Consolidating enterprise, engineering and service-network data into role-based views enables providers of products and services to optimise service process experiences and deliver new self-service capabilities to their customers.
  • Reduced downtime: Proactively identifying machine service and maintenance needs by simulating historical patterns or design expectations of machine performance against real-time sensor data helps to reduce unplanned downtime and maximise asset utilisation and customer value.
  • New business models: It may be possible to unlock new usage- or outcome-based service contracts that leverage usage, availability and operational data to simulate and orchestrate product parameters and deliver remote updates and value-added services.

Building your digital twin

As we’ve seen, there are no shortages of opportunities to develop digital twins to improve business outcomes and decision-making. While identifying opportunities can be overwhelming, the aforementioned use cases are proven in the market to be the most simple and straightforward to set up, delivering a quick return on investment. From there, these use cases can be adapted and extended to further differentiate product and service offerings and drive operational effectiveness in your processes.

The time is now for industrial enterprises to build out their digital twin strategies. With the maturity of the enabling technologies and digital thread initiatives reaching critical mass, many companies are taking stock of their current capabilities and moving quickly to fill the gaps. The way digital twins are delivered through various lenses can vary greatly based on the specific use case being pursued, but core considerations should be addressed based on necessary capabilities. Companies seeking to advance their digital twin strategy will benefit from organising current and future capabilities into the following framework. From there, specific use cases can be plotted to organise requirements and develop a plan of action.

Figure 1: A framework for digital twin development.

Figure 1: A framework for digital twin development. For a clearer image, click here.


A digital twin requires you to combine the digital definition from related business systems with real-world data and insights from the physical world via sensors. Companies must decide what source data they will include, for example, manufacturing process plans or operating procedures that define a process combined with the real-world telemetry and sensor data from manufacturing and production environments. Adding additional sources to its definition, for example, supply chain data from an MES system, can drive increased overall context for the twin as well as unlock additional use cases without rework. Additional technologies continue to add to potential sources of insight. In the future, with the bounty of sensor data coming online through AR devices, people and the spaces they inhabit (factories, buildings, etc) will be defined and integrated into twins as well.


Digital twins give unified insight into the data connected by the digital thread. Once a use case is understood, unique identification and organisation of data surrounding an individual product, process or person can be mapped and organised to inform the twin model. It could be contextualised into an overall process, enhanced with behavioural data or used to align to desired KPIs. Understanding the overarching goals of the twin will help to contextualise it into the type of digital twin model that makes the most sense.


Analytics can be used to add value for certain use cases to inform business decisions with greater accuracy and unlock hidden insights, or the value can be self-evident. Analytics could be applied on the mapped data to answer questions along common frameworks, for example, descriptive answers to questions like how a product is performing, to diagnostic questions around cause of failure, and predictive or prescriptive questions that simulate potential scenarios and optimise performance outcomes.


Orchestration is where these insights are put to task. Triggers can be created that automate or direct actions based on the result of the answer or analysis. For example, a process trigger could be put into place to dispatch a technician or create a customer service ticket for a product failure. You could automatically propose remote configuration updates based on performance characteristics. It is also possible to deliver updated KPIs and worker priorities based on customer or supply chain activities. Whatever questions you seek to answer, a corresponding action can be orchestrated to react, and measurable KPIs and outcomes can be captured.


Digital twins are delivered or interacted with through a frontend UI or ‘lens’ that is role or task based and specific to a given use case. They can be delivered through interfaces such as desktop and mobile devices, and emerging technologies like augmented reality provide additional options. In fact, augmented reality provides the capability to capture spatial data related to environments and workers, and to eventually develop digital twins of these previously undefined spaces and processes. These technologies also offer enhancements to the fidelity and user experience of digital twins and make use cases accessible to new stakeholders such as deskless workers.

Getting started: where to begin your digital twin journey

Many business leaders may be surprised to learn that if you’re already gathering data from your products, processes or people, little additional investment is needed to stand up a basic digital twin model. In addition to the requisite digital thread connecting silos of interrelated data, you may even have the enhanced front-ends and analytical capabilities in place. For companies looking to extract real-world insights to improve product design, for instance, they need only to securely deliver that product sensor data back to product engineering systems in a compatible format for simulation.

Below are a few practical actions to take today to advance your digital twin strategy:

  • Assemble cross-functional teams to understand pain points or use cases that will break down silos and deliver value to multiple teams, producing the greatest ROI and enabling the greatest flexibility to adapt and mature the digital twin over time.
  • Inventory your technology initiatives and the goals they seek to achieve to determine where digital twin use cases can be enabled with minimal investment and predefined success metrics.
  • Identify business partners whose technology ecosystem integrates easily across your entire technology footprint and who possess the requisite capabilities. Consider not only what can be done today, but how your twin might evolve over time to avoid costly and challenging integration projects down the line.

Gone are the days of digital twins being a concept to daydream about in the future. Many industrial enterprises are deploying digital twins and reporting significant benefits today. Many more have the building blocks in place and are missing out on untapped value — all they require is the unified vision and partnerships to construct the model that fits best to solve their unique challenges. Successful implementations will require executive-level buy-in, the right mix of technology capabilities and domain expertise often stemming from partner ecosystems.

The confusion surrounding digital twins is rooted in their nearly unlimited potential coupled with varying, and often narrow, schools of thought in industry. The time is now for industrial businesses to create a digital twin strategy, to take a step back and to view the mosaic of technology initiatives that exist today and to connect them to executive-level business objectives.

Top image: ©

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