AI-driven Simulation
for Industrial Decision Intelligence.
Understand complex industrial systems, simulate operational scenarios and identify optimal decisions before changes happen in the real world.
By combining physics-based simulation, scalable HPC and AI-driven optimization, industrial companies can transform simulation from an engineering capability into a platform for operational decision intelligence across the industrial lifecycle.
SIMULATION MEETS INTELLIGENCE | AI-POWERED AND HPC-ACCELERATED
The Superpower for Industrial Innovation and Efficiency.
In an increasingly connected and data-driven industry, simulation is becoming a critical capability for understanding complex systems and making better decisions.
Combined with artificial intelligence (AI) and high-performance computing (HPC), it enables new ways to develop products, optimize production and improve industrial performance.
20%
reduced manufacturing costs
15%
increased innovation rate
35%
reduce failure rates
AI-driven SIMULATION
From Engineering Simulation to Industrial Decision Intelligence.
Simulation has long been an essential capability in engineering and product development. However, in many organizations it remains isolated within specific engineering teams and is rarely connected with operational or business decision processes.
The real value of simulation emerges when it is integrated with operational data, scalable computing infrastructure and AI-driven optimization. This enables companies to move beyond isolated engineering analyses and instead explore multiple operational scenarios digitally before implementing changes in real production environments.
As a result, simulation evolves into a foundation for data-driven decision making across engineering, operations and management.
What this means in practice.
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Simulation is no longer limited to engineering teams
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Simulation results become available to operations and management
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Operational scenarios can be evaluated before changes are implemented
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Engineering knowledge becomes reusable across the organization
Connected Data Across the Industrial Lifecycle
Industrial Data, Simulation & AI.
AI-driven simulation requires a consistent data foundation across the industrial lifecycle. Engineering data, operational process data and business information must be connected to create meaningful insights.
Engineering data such as product configurations and design parameters provide the structural foundation for simulation models. Operational data from IoT systems, production processes and equipment enables the calibration and validation of simulation scenarios. Business data from enterprise systems adds commercial and operational context.
When these domains are connected, simulation and AI can analyze complex relationships between process parameters, materials, equipment behavior and operational outcomes.
Industrial Data Domains.
Engineering Data.
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Bill of Materials (BOM)
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product configurations
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design parameters
Operational Data.
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IoT and process data
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production parameters
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equipment and asset data
Business Data.
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ERP systems
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commercial data
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customer requirements
Why This Matters.
When engineering, operational and business data are connected, companies can analyze how design parameters, process conditions and production strategies influence operational performance.
This enables a deeper understanding of complex industrial systems.
From Data to Operational Decisions
Enabling Better Industrial Decisions.
When industrial data and simulation are combined, companies gain the ability to evaluate operational scenarios before implementing changes in real production environments.
Engineers and operators can analyze how process parameters influence energy consumption, product quality and operational stability. Management teams can better evaluate potential improvements, investment decisions and operational strategies.
Instead of relying on trial-and-error in the plant, organizations can explore different scenarios digitally, reduce operational risks and accelerate optimization across engineering, production and business operations.
Decision Intelligence Capabilities.
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Scenario analysis
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Risk evaluation
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Parameter optimization
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AI-supported recommendations
SCALEABLE COMPUTE & AI POWER
The Foundation for AI-Driven Simulation.
AI-driven simulation requires scalable computing infrastructure capable of processing large volumes of industrial data and executing complex simulation models efficiently. Cloud-based high-performance computing (HPC) provides the foundation to run large numbers of simulation scenarios, analyze system behavior and accelerate optimization processes.
By combining cloud infrastructure, advanced analytics and AI capabilities, industrial companies can transform simulation into a scalable platform for operational decision intelligence. This enables engineering teams, operations and management to explore complex scenarios and identify optimal operating conditions faster and more reliably.
Core Capabilities.
Cloud Infrastructure & HPC.
Scalable cloud infrastructure enables the execution of computationally intensive simulation models and large-scale scenario exploration without the limitations of traditional on-premise systems.
Simulation & Optimization.
Physics-based simulation models and optimization algorithms allow companies to explore multiple operational scenarios and identify optimal process parameters.
Agent Orchestration.
AI agents can automate complex workflows by orchestrating data analysis, simulation runs and optimization processes, enabling faster and more autonomous decision support.
Data Analytics.
Advanced analytics helps identify patterns and relationships across engineering, operational and business data, enabling deeper insights into complex industrial systems.
AI Assistants & Applications.
AI-powered assistants and decision applications provide intuitive access to simulation insights and operational recommendations for engineers, operators and decision-makers.