Seeing the Whole System: How Dassault Systèmes Is Advancing Rail Through Systems Thinking

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For the rail industry, complexity is a given. Every train movement, maintenance schedule, infrastructure upgrade and passenger service is part of an interdependent network of systems that must operate in harmony. When one link falters, the ripple effects can be felt across entire networks, disrupting timetables, budgets, and passenger confidence alike. 

It’s a challenge that demands a new way of thinking — systems thinking. Popularised by organisational theorist Dr Russell Ackoff, systems thinking provides a blueprint for understanding and improving complexity. 

Ackoff argued that to improve any system, you must first understand how its parts interact, not just how they perform individually. In rail, that means recognising that rolling stock, signalling, energy management, and passenger experience aren’t isolated disciplines but interacting components of a single living network.

This principle underpins Dassault Systèmes’ approach to rail service delivery. The company has long championed a model-based, lifecycle-driven view of infrastructure and operations — one that connects the digital and physical worlds. By applying systems thinking through advanced engineering and data integration, Dassault Systèmes is helping the rail sector move from reactive problem-solving to proactive, end-to-end optimisation.

Traditional rail management has often suffered from fragmentation. Engineering departments plan upgrades in one tool, operators monitor performance in another, and maintenance teams rely on spreadsheets or legacy software. This separation not only slows decision-making but obscures how changes in one area affect the rest of the system. 

Dassault Systèmes’ integrated rail service delivery model is designed to close those gaps. At its core is model-based systems engineering (MBSE) — a discipline that replaces static documentation with dynamic, data-driven models. Within the 3DEXPERIENCE platform, every stakeholder — from planners and engineers to operators and regulators — works within a shared digital environment. 

This ensures decision traceability across the entire asset lifecycle. Design intent, operational data, and maintenance insights remain connected, enabling continuous improvement and regulatory compliance without duplication or data loss. The result is a truly interoperable ecosystem where rail projects, assets, and operations evolve in sync rather than in isolation. 

If systems thinking provides the mindset, virtual twins provide the operational muscle. In Dassault Systèmes’ framework, virtual twins are not static 3D models but living, data-connected replicas of assets and systems. 

These virtual environments integrate real-time data from sensors, schedules, and energy systems, allowing operators to simulate and test scenarios — from minor timetable adjustments to large-scale infrastructure changes — before implementation. By modelling asset behaviour under different conditions, teams can predict the consequences of a decision across the network. 

This is particularly powerful for predictive maintenance. Instead of responding to failures, virtual twins enable operators to anticipate them. Machine learning models analyse patterns of wear, vibration, and temperature to forecast when a component will need attention.  

Through this approach, virtual twins shift from being visual tools to becoming predictive decision-support systems that unify engineering, operations, and sustainability objectives. 

Examples of how Ed Jeffery Ltd can tailor the performance modelling process include:

• Matching the level of detail to the project stage to ensure the optimal trade-off between timescales and required outputs
• Carefully choosing a geographic scope that finds the ideal balance between model size/accuracy and timescales
• Using data-driven analysis to quickly and accurately produce outputs to meet customer requirements

​​​​​​​The shift to integrated digital operations also enables a more data-driven approach to decarbonisation and energy management. Rail operators can model and monitor emissions at every stage of the lifecycle — from material sourcing and manufacturing to real-time energy consumption. 

Dassault Systèmes’ platform makes it possible to evaluate trade-offs between performance, cost, and environmental impact before implementation. By connecting these insights to long-term planning, operators can design rail systems that are inherently more efficient, sustainable, and resilient. 

As rail networks expand to meet decarbonisation targets and rising passenger expectations, the sector faces growing pressure to deliver more capacity, reliability, and sustainability — often with fewer resources. Meeting those challenges requires the same systems-based mindset that reshaped aerospace and automotive industries. 

Rather than simply pursuing incremental gains, such as faster trains, cheaper maintenance, cleaner energy, operators can now optimise the entire ecosystem. 

Dassault Systèmes is proving that the big opportunity in rail lies between the components — in the relationships that turn data into intelligence and infrastructure into insight. The future of rail will belong to those who see the system whole.