Digital twins: Simulation as a driver of innovation

Digital twins link real-world products, plant and processes to a virtual replica that evolves alongside them throughout their entire life cycle.

Combined with simulation, this enables robust decision-making – from the initial design through to virtual commissioning and ongoing operation.

The result: lower risks, faster approvals, higher availability and targeted improvements.
For industrial companies, the digital twin is therefore a strategic driver of innovation.

A digital twin is a virtual representation of a physical object or process. It comprises geometry, material and state data, behavioural models and simulation results. A key feature is the continuous synchronisation of data between the real world and the virtual model. If anything changes in the product, the production line or the application scenario, the digital twin is updated – and conversely, insights from the twin feed back into design, production and service. This creates a learning system that not only documents decisions but actively improves them.

The core idea: the integration of simulation and real-time data makes assumptions explicit, allows variants to be compared without risk and identifies conflicting objectives at an early stage. This improves the quality of decisions – and speeds up implementation.

Without simulation, the digital twin remains a static representation. It is only the ability to run through ‘what-if’ scenarios that unlocks its full potential. Typical questions that can be answered using digital twins and simulation:

• Design: Will the component withstand thermal and mechanical loads? How does the system react to tolerances and manufacturing variations?

• Production: What cycle time is realistic? Where do bottlenecks arise in the material flow? How do layout changes affect throughput and buffers?

• Operation: How does the condition of a plant evolve? Which parameters provide early indications of wear and tear? Which maintenance strategy minimises downtime?

The advantage: Decisions are not based on gut feeling, but on model-based, repeatable results.

A robust setup comprises three layers:

1. Domain models & multiphysics simulation
   : Disciplines such as structural, thermal, fluid flow and EM are modelled individually and in combination. This enables realistic load cases and boundary conditions to be represented.

2. Data platform & time series
   : State and event data are securely recorded, versioned and assigned to the digital twin in context (variant, batch size, load collective). This is the only way to keep simulation and reality in sync.

3. Governance & Security
   Roles, access rights, certificates, versioning and compatibility of the digital twin states are defined. This prevents siloed solutions, protects IP and enables auditability.

Concept & Design

• Simulate variants in a comparative manner (material, dimensions, tolerances)

• Define target parameters (e.g. weight, stiffness, thermal behaviour)

• Identify interdependencies (e.g. thermal behaviour ↔ service life)

Detailing & Verification

• Check couplings (structure/thermal/fluid) for boundary cases

• Compare simulation results with reference cases

• Manage change control via requirements ↔ model ↔ test

Process & production planning

• Simulate material flow, buffers, cycle sequences and layouts with a wide range of variants

• Prepare control logic for virtual commissioning

• Run through ramp-up scenarios and mitigate risks

Operation & Service

• Feed the performance twin with field data

• Derive condition forecasts and service windows

• Establish feedback loops in design and production

• Traceability: Requirements ↔ models ↔ results must be consistently linked and versioned.

• Validation: Each model is assigned reference cases and acceptance criteria; regular plausibility checks ensure the twin remains reliable.

• Security by Design: Role-based access, segmentation and certificate management are mandatory – particularly for twins spanning multiple sites.

• Compatibility: Naming and versioning conventions, interface standards and regression tests ensure seamless collaboration between different teams and systems.

• “A pretty twin without data”: Without reliable time series and contextual data, the benefits are lost. Plan the data pipeline first.

• Siloed twins: Product, production and performance twins must be linked end-to-end, otherwise there will be no feedback.

• Security considered too late: Access rights, certificates and versioning need to be addressed at the outset – not as an afterthought.

• No business fit: The use case needs clear KPIs; otherwise, the twin becomes a demo rather than a value driver.

• Lack of implementation: Results must be translated into layouts, work plans and service processes – including assigned responsibilities.

• Digital Twin Product Owner: responsible for the target vision, KPIs and roadmap.

• Domain experts (mechanics, thermodynamics, EM, flow): model and validate the simulation.

• Data/IT Team: responsible for data collection, storage, backup and provision.

• Operations/Service: uses the digital twin for decision-making; feedback is fed back into the model.

• Quality/Compliance: ensures traceability and auditability.

A two-track approach has proven effective in terms of process: an ‘Exploration Track’ for new models and use cases, and an ‘Industrialisation Track’ for transitioning to standard operation. This ensures the Digital Twin remains both innovative and stable.

Those who consistently implement digital twins alongside simulation will build a lasting competitive edge. Industry benefits from faster cycles, more robust processes and transparent decision-making – across design, production and operations. The key lies not in a single tool, but in the combination of reliable models, a clean data foundation and clear governance. With this foundation in place, the digital twin becomes a continuous driver of innovation.