Computational Modelling
Unlock Nature’s Code
Computational models that learn, adapt, and predict the behaviour of complex natural systems.
Computational modelling uses physics-based simulations to predict how complex systems behave under different conditions, enabling you to test scenarios virtually before committing to physical prototypes or real-world deployment. At Neorick, we apply advanced computational methods including computational fluid dynamics (CFD) for fluid and gas flow analysis, finite element analysis (FEA) for structural mechanics and thermal behavior, multi-physics simulations that couple multiple phenomena, and multi-scale modelling that connects behavior from molecular to system level. These capabilities help organizations across aerospace, energy, manufacturing, and infrastructure optimize designs, reduce testing costs, accelerate development cycles, and solve problems that are too expensive, dangerous, or physically impossible to test in reality.
Modern computational modelling increasingly integrates with AI and data-driven approaches to enhance accuracy and speed. Machine learning accelerates model calibration and parameter optimization, surrogate models enable rapid design exploration across thousands of configurations, and hybrid physics-AI approaches combine simulation fidelity with data-driven corrections. We build computational workflows that connect simulation tools with your data infrastructure, enabling automated design optimization, real-time decision support, and continuous model refinement based on operational feedback. Whether you're optimizing aerodynamic performance, predicting material failure, analyzing thermal management, or designing complex fluid systems, our computational modelling capabilities transform engineering intuition into quantified insight and competitive advantage.
Digital Twins
Mirroring Reality. Multiplying Possibilities.
Building Living Systems That Learn, Adapt, and Redefine Performance
Digital twins are virtual replicas of physical assets, processes, or systems that update in real-time using sensor data, enabling continuous monitoring, predictive maintenance, and scenario testing without disrupting operations. At Neorick, we build digital twins for manufacturing equipment, energy infrastructure, buildings, vehicles, and entire facilities by integrating IoT sensors, operational data, and physics-based models. Our implementations combine 3D visualization, time-series analytics, machine learning for anomaly detection, and simulation engines for scenario testing, all integrated with existing systems including SCADA, ERP, and maintenance platforms. Digital twins transform reactive maintenance into predictive strategies, reduce unplanned outages, extend asset lifespans, and provide the visibility and intelligence needed to maximize performance, reduce risk, and continuously improve operations with measurable ROI.
Discover
We identify high-value use cases for digital twins based on asset criticality, failure costs, and optimization potential. This assessment ensures digital twin investments target assets and processes where predictive insights deliver measurable ROI.
Design
We develop accurate virtual models incorporating asset geometry, physics-based behavior, operational parameters, and historical performance data. These models enable scenario testing, what-if analysis, and predictive capabilities tailored to your specific assets.
Deploy
We establish real-time connectivity between physical assets and digital replicas through IoT integration, data pipelines, and system interfaces. Implementation includes sensor validation, data quality assurance, and integration with existing operational platforms.
Drive
We enable continuous improvement through automated analytics, alert systems, and feedback loops that refine model accuracy over time. Successful implementations are scaled across similar assets, multiplying value and building organizational digital twin capabilities.
Scenario Analysis
Anticipate Every Possibility
Where intelligence meets imagination to model decisions before they are made.
Scenario analysis helps organizations test strategies and decisions in virtual environments before committing resources in the real world. At Neorick, we build scenario models that simulate how complex systems respond to different conditions, decisions, and external factors. This enables leaders to evaluate strategic options, assess risks, identify opportunities, and stress-test plans against uncertainty. Our scenario analysis capabilities span operational planning (supply chain disruptions, capacity decisions), strategic planning (market entry, investment prioritization), risk management (crisis response, resilience testing), and policy development (regulatory impacts, long-term infrastructure planning).
We combine computational simulation, statistical modeling, machine learning, and system dynamics to create scenario environments that capture real-world complexity. These models integrate historical data, operational constraints, market behaviors, and external variables to generate realistic projections of how decisions cascade through your organization and ecosystem. As scenarios run, we identify sensitivity points, trade-offs, and non-obvious relationships that inform better decisions. Our iterative approach means models improve with each use, incorporating new data and learnings to increase accuracy and relevance. Whether you're optimizing resource allocation, preparing for market volatility, or evaluating infrastructure investments, our scenario analysis transforms uncertainty from a barrier into a strategic planning advantage.
Define
We establish the decision context, key objectives, critical uncertainties, and relevant variables that will shape scenario design. This scoping ensures models focus on factors that materially impact outcomes.
Model
We build computational representations that capture system behaviors, relationships, constraints, and dynamics using appropriate modeling techniques from statistical methods to agent-based simulation.
Simulate
We run multiple scenarios across different assumptions, conditions, and decision paths to explore outcomes, test sensitivities, and reveal risks and opportunities before they occur in reality.
Refine
We incorporate results and feedback to improve model accuracy, expand scenario coverage, and enhance decision-support capabilities through continuous learning and iteration.
Process Optimization
Detect. Decide. Deliver.
Where intelligent systems refine themselves, and progress becomes perpetual.
Process optimization identifies and eliminates inefficiencies in how work gets done, enabling organizations to deliver better results with fewer resources. At Neorick, we apply advanced analytics, simulation, and AI to optimize complex processes across manufacturing, logistics, service delivery, and operations. Our approach goes beyond traditional process improvement by combining multiple techniques: discrete event simulation to model workflow dynamics, constraint optimization to balance competing objectives, predictive analytics to anticipate bottlenecks, and prescriptive algorithms that recommend optimal decisions in real-time. We help organizations reduce cycle times, lower costs, improve quality, increase throughput, and enhance resource utilization through data-driven process redesign.
Our process optimization methodology integrates computational modeling with operational data to create accurate representations of how processes actually perform under various conditions. We identify dependencies, constraints, and trade-offs that aren't obvious from process maps alone, revealing where small changes create outsized impact. By building optimization models that consider multiple variables simultaneously—capacity, demand variability, resource availability, quality requirements, cost constraints—we find solutions that balance competing priorities effectively. For processes requiring ongoing adaptation, we implement feedback loops and automated decision systems that continuously monitor performance and adjust operations dynamically. The result: processes that operate closer to theoretical capacity, respond intelligently to changing conditions, and free your teams to focus on value-added activities rather than firefighting operational issues.
Are you making critical decisions based on assumptions or validated simulations?
The difference is engineering certainty into uncertainty. We build computational models, digital twins, scenario analysis frameworks, and process optimization systems that reduce risk, accelerate innovation, and turn complexity into competitive advantage. Let's discuss how simulation can transform your decision-making from reactive to predictive.