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Enabling Digital Twins with Systems Engineering

Adam Lancaster
8 Jun 2022

Mirroring the real world to create better and more sustainable products, using Digital Twins is a common aspiration for many companies. We explore how to enable these Digital Twins across the full lifecycle, using Systems Engineering techniques.

The world around us continues to grow more complex due to increased data availability and enhanced inter-connectivity, therefore the need to achieve predictability and stability in delivery of products and services is fundamental to business success. At the same time, we need to reach beyond our current value chains and deliver a Digital First, real-time, customer-centric future. The environment changes too quickly for point solutions, so these need to be robust, align with future developments and agile enough to be relevant and provide value years from now.


Systems, and user interactions with those systems, grow more complex by the day, with novel technologies building on existing legacy networks, increased data availability, enhanced inter-connectivity between systems and the software driving those system, all coupled with an enhanced expectation about the service provided. This provides engineers with a problem that often results in engineering projects having complex designs, with equally complex plans, work breakdowns, commercial relationships, and multiple critical paths to manage. Within this paper we attempt to address the challenges around:

  1. Managing complexity
  2. Defining the technology evolution
  3. Applying the technology to a Systems Engineering approach

Managing Complexity

Figure-1 Capability Jigsaw

Many of the systems that we develop are very rarely novel and brand new, they are building on already established requirements. However, those requirements, the interactions now required between systems, and the evolving certification requirements for a product, cause an increase in complexity meaning that these systems need to be designed to ensure the best quality product. Complexity comes in two variations: “Essential” and “Accidental” (Prof J Holt, 2021 ). As systems engineers, we attempt to tackle the inherent complexity within a product (“Essential”), while struggling to address the cultural and organisational complexities (“Accidental”). The “jigsaw” presented here, Figure 1, outlines the capabilities to manage and support a digital first ecosystem, extending to several other key aspects to enable the complexity of products to be managed throughout the entire lifecycle whilst truly connecting organisations digitally.

Defining the Technology Evolution

There are many competing and sometimes contradictory definitions for many of the technologies that are suggested as solutions to our complexity problem, creating a marketplace that is both confused and demanding. Digital Twins and Model-Based Systems Engineering (MBSE) are terms often used to define a new technology seen as the answer to managing complexity, while decreasing time to market with increased quality of service. These new technologies are often quoted with other wider technologies such as Metaverse, further complicating the landscape. However, many organisations struggle with defining what these terms mean to their organisation and the resulting value to be gained.

Model-Based Systems Engineering (MBSE)

MBSE is usually defined as the formalized application of modelling to support system requirements, through its design lifecycle, into verification and validation activities, while also supporting the product through life. Introducing the term “Model” complicates the message and many clients suggest they are already undertaking MBSE.

Figure- 2 Client view of MBSE

However, upon investigation, really what they are doing is Model Based Design, or even more simply, using a model to run a test where the result is document in a traditional manner. MBSE is far more than this – it is the linkage of models to create a system that understands its boundaries, its contradictions, and can communicate the reasons for its design changes. It is the bridge between requirements and low-level high-fidelity technical models created by domain specialists. It also acts as the enabler for a wider Digital Twin to be implemented across the system lifecycle.

Digital Twin

It is a common misconception that Digital Twins only come into use once the physical system has been created. Even though most system design decisions and project errors are introduced within the requirements stages of the project. Meaning that once the product Digital Twin has been created, most system errors are already embedded within the product, limiting the ability to optimise for performance and financial success. To take full advantage of the benefits that Digital Twin technology has to offer, we must first shift our focus towards developing Digital Twin technology which can be utilised across the whole system lifecycle.

Based upon this, and considering the plethora of literature on the topic, a more appropriate definition of a Digital Twin has developed to outline the ‘Different virtual representations at different stages of a products lifecycle’ [Grieves & Vickers, 2017]. This definition can be further broken down into three proposed categories, each corresponding to a different stage within the products lifecycle, shown in Figure 3.

Figure 3 – Digital Twin Evolution – A Proposed skeleton

Applying the technology to a Systems Engineering Approach

A holistic transformational journey is required to enable the adoption of MBSE and Digital Twins across the system lifecycle, ensuring all benefits are realised. Understanding where your organisation is today, and where you want to get to, enables a global transformation addressing the people, processes and technologies required to deliver the capability. Systems Engineering, MBSE & digital technologies are key in achieving digital continuity and rapid information flow across the organisation. Combining that with a diverse transformation team with expert knowledge across the full product lifecycle, including strategic development and application of methodologies, leads to the effective implementation of Digital Twin capability, able to be used across the lifecycle.

Our approach to implementing Digital Twins

With ever increasing system complexity, and expanding stakeholder needs, there is a growing push towards improving the quality, whilst reducing the cost, of system design activities. To overcome these challenges, success comes when we balance the Digital Twin, Systems Engineering process, infrastructure investment and the integration of people into the solution. It is key to choose a solution and service partner who brings experience embedding enablers, while maintaining a balance between technology and investment.

Our ambition at Capgemini Engineering is to reinvent the Digital First mantra while enabling a systemic adoption of Digital First methodologies. We define the technology evolution and utilise a structured Systems Engineering approach to enable the adoption of digital technologies. We work with clients to develop and implement a capability roadmap, presenting the enablers and resources required to deliver a global transformation, helping to realise the benefits from a Digital Twin across the full lifecycle.

To read more blogs in the Intelligent Industry: Journey to Farnborough International Airshow series, see quick links below:

A Quantum of Intelligent Industry – Mike Dwyer considers the potential impact that the world of quantum computing, sensing and communication could have on our ability to create new intelligent products and services.

Innovation at Speed: What Intelligent Industry can learn from Formula One’s data driven innovation – Ashish Padhi delves into the data driven rapid innovation process of Formula One aerodynamic design to prise out lessons for Intelligent Industry.

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