Digital twins: a primer for industrial enterprises — Part 1


By Steve Dertien – Chief Technology Officer, Jonathan Lang – Lead Principal Business Analyst, David Immerman – Business Analyst, PTC
Monday, 20 July, 2020

Digital twins: a primer for industrial enterprises — Part 1

In recent years there has been a great deal of buzz around the concept of digital twins, gaining prominent placement in analyst predictions.

According to IDC’s 2019 Digital Transformation FutureScape, 30% of G2000 companies will have implemented ‘advanced digital twins’ to optimise their operations by 2020, the vast majority of which will be industrial enterprises. While discussions of the digital twin concept have framed this technology as a modern business imperative, the first mention can be dated back to NASA in the 1960s with the Apollo 13 mission. For enterprises deep in multitudes of digital transformation initiatives, digital twins can be convenient to lump into the category of nice-to-haves to add to the end of the to-do list. The truth is, digital twin technology is available today — and if you’re an industrial enterprise, it’s very likely that your existing technology can start compounding additional value unified through a digital twin strategy. In doing so, enterprises can expect to achieve outcomes like differentiation for their products, improved operational effectiveness across processes and optimised productivity for their people.

But guidance for enterprises looking to develop digital twin strategies has been sparse — with most conversations occurring at the conceptual level. Complicating matters, there has not been a consensus in industry as to what a digital twin is, creating confusion and barriers to widespread adoption. In this article, we will unravel the concept for business leaders looking to bring their digital twin strategy from vision to value.

Why is the time right to develop a digital twin strategy?

Industrial businesses are feeling more pressure than ever before to deliver results and react to constantly changing market landscapes. Competitive and disruptive threats are reshaping product and service expectations to demand higher quality and greater flexibility. Global trade and cost pressures are presenting new risks that are demanding ruthless efficiency and lean, agile operational processes for industrial companies to compete at blazing speed. A looming skilled labour shortage is forcing companies to rethink the way they empower their frontline workers to remain productive and agile. Companies are undergoing digital transformation and applying technology to try to address these challenges — and they’re generating an incredible amount of data in the process, giving way to a new set of technology-based business challenges.

What is a digital twin?

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.

What started out with the capture and storage of enterprise data has slowly crept out of the server closet. Sensors are everywhere, and telemetry data is being created by not only smart, connected products, but for entire enterprise processes and systems, and with augmented reality and other people-centred technologies being adopted, there are even digital connections to people. The result is that today this data exists in siloes across organisations, each possessing limited context, which in turn limits its value. Companies have recognised the need to integrate this new data but have been struggling with the strategy to do so in a scalable and effective way. Systems integration spending is growing rapidly year on year, and accounts for increasingly large portions of industrial companies’ spend. One key objective of this spending is to map data from currently isolated technology silos as companies recognise the value of centralising and simplifying the process of information discovery and analysis. In addition to providing universal data access, companies also recognise that exploring the relationship between interrelated datasets offers higher order insights than any one set alone. This data unification is a necessary prerequisite to building a digital twin, and is often referred to as a digital thread.

Figure 1: Digital twins are digital models that virtually represent their physical counterparts.

Figure 1: Digital twins are digital models that virtually represent their physical counterparts. For a larger image click here.

With new technologies like augmented reality and IoT creating and demanding vast amounts of data from disparate sources, it is more important than ever that companies have a clear strategy as to how and why they will integrate their various operational and information technology. The digital thread is the essential groundwork that can turn the common dilemma of spiralling cost and complexity of digital transformation into an opportunity to enable faster time to value, greater agility in change management and more data-driven decision-making. A digital twin is a model for contextualising, analysing and realising the value of the digital thread in a way that enables it to be acted upon and scaled across multiple solutions and front-end applications. In other words, the value of a digital thread is manifest through the discrete use cases served by digital twins.

What enterprise outcomes does the digital thread deliver?

Defining the product and service experience

Customers of industrial product manufacturers are feeling pressure to ensure maximum return on their investments. It is an imperative that product and service providers become proactive in their contribution to customer success. Yet defining the customer journey end to end has proven to be a challenging and costly endeavour for enterprises of all kinds. For example, sales organisations often analyse customer purchasing history to understand new market opportunities. Separately, R&D may analyse smart, connected product usage data to identify utilisation of specific features for future development. Both seek to define the characteristics of successful customers, yet each is working with limited datasets. By mapping these streams of value-rich insight under a digital thread of the product or customer experience, transparency is created across roles and opportunities are unlocked to develop digital twins that proactively strengthen the customer relationship, improve the quality and value of products and tap in to new sources of service revenue.

Operational process visibility

Companies are struggling to keep pace and manage the necessary rate of change in their operational processes. By the time information becomes available to the necessary stakeholders in upstream and downstream functions in the value chain, risk has escalated, placing companies in a reactive state that is costly, wasteful and inefficient. Mapping operational process data utilising a digital thread for asset and process-related data, companies can develop digital twins to gain the transparency and operational predictability to simulate and orchestrate change or optimise operational processes with high speed and accuracy.

Data-driven people

Studies that show that employees spend upwards of 35% of their workday looking for and consolidating information. When you build, for instance, a digital thread of a production process, that thread can centralise data about asset utilisation, health and overall throughput, to provide workers with a holistic view of end-to-end operations. Analysing this data either in real time or in retrospect through a digital twin can improve decision-making not only at the management or total system level, but for the individual workers involved in that process. With the emergence of augmented reality, frontline workers can experience this value for the first time. The concept that digital twins centralise and present data in an actionable format is a key characteristic of their value.

A digital thread is the connection synchronising related upstream and downstream information. A digital thread creates continuity and accessibility to a common set of data, defining the product, process or people that it relates to across the enterprise functions. It solves a few key challenges of its own:

  • 360-degree views: By mapping related datasets through a common platform, true 360-degree views of products and processes become available, unlocking the opportunity for higher order insights delivered through a digital twin.
  • Single source of truth: Visibility is achieved upstream and downstream across roles by delivering real-time updates and channels for communication, driving a culture of agility and innovation.
  • Scalability: New products, processes and technologies are coming online every day. With a digital thread architecture, data is interoperable from the start — enabling scalable gains in efficiency and change management.

What use cases are creating value for industrial enterprises today?

Opportunities to create value from digital twins exist across industries, but their deployment is delivering most significant early impact in industrial organisations. This is due to the prevalence of connected products as well as connected-operational environments, and even the emergence of connected workers through augmented reality. Industrial companies may also have much of the requisite technology deployed today. The impetus for connecting the dots between these technologies to create a functioning twin is driven by functional business challenges and opportunities.

Once a twin is established, we see examples of digital twins driving value across the business and nucleating around a few key use cases led by functional champions in engineering, manufacturing and operations, as well as maintenance and service. 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. Multiple twins can also be combined to create system-wide visibility that provides broader insight for a total business function or system of twins.

Key digital twin use cases

A number of digital twin use cases can be found, impacting from the top management level right down to the service technician.

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

  • Corporate/CXO: For senior management, a digital twin can unlock new outcome- and usage-based business models that increase customer lifetime value and profitability.
  • Product engineering: For discrete manufacturers a digital twin enables a ‘voice of the product’ that replaces usage assumptions with facts, accelerating time to market with optimised features and designs.
  • Sales and marketing: Digital twins can help develop customer transparency and alignment that reduces cycle and lead times and provides opportunities for up-sell, cross-sell and relationship building.
  • Manufacturing and operations: At the plant operational level, digital twins allow production visibility and planning that improves operational agility, increases throughput and optimises process efficiency throughout the supply chain.
  • Customer and technician service: Digital twins create enhanced service delivery and offerings that improve customer satisfaction through increased uptime and quicker time to resolution.

In Part 2

In Part 2 of this article we will examine the three main digital twin use cases (engineering, manufacturing and service) and the high-level steps involved in building a digital twin.

Top image: ©

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