Changes to maintenance, fleet, infrastructure or schedules rarely stay within one part of an operation. Their effect moves through connected assets, people, production systems and logistics.
TEAM applies GNOS when leaders need to understand those consequences before committing operational capacity or investment. We define the question with your team, build a digital twin of the relevant system and compare each option against agreed operating rules, data and assumptions.
The model identifies where constraints may emerge and how performance could change. It also makes the trade-offs between options visible.
Addressing a bottleneck can expose another elsewhere. GNOS represents connected parts of the system in one model, including production, maintenance, fleet movements and logistics where they influence the answer.
In a rail-linked supply chain, production volumes can be tested against rail capacity and port receival. Teams then see how a change to volume, scheduling or asset performance affects the rest of the system and where the next constraint is likely to appear.
TEAM brings engineering, operational and asset management expertise to the problem definition. We work with operators, planners, engineers and decision-makers to agree what the model must represent. The same process establishes which inputs can be trusted and which assumptions require testing.
A focused project may answer one defined question. Once the model has been validated and used, TEAM can extend it to other parts of the operation without rebuilding the original logic.
Where the work supports an asset management decision, the scope connects the modelling question, accountabilities and measures to organisational objectives. This approach is consistent with the principles of ISO 55001:2024.
GNOS combines discrete-event, agent-based and discrete-rate simulation within a single digital twin. These methods can represent individual asset behaviour alongside queues, material flows and changing system capacity within the same model.
Where physical performance affects the answer, the model can go deeper. In rail applications, GNOS models train dynamics against gradient, load and braking conditions. Track geometry, elevation profiles and signalling layouts can represent the physical network in the simulation.
GNOS can also test scenarios that have never occurred. Existing data and actual performance can be used to validate the model, then new operating ideas can be simulated before those conditions exist in the field.
Existing plans and operational data can come from SAP, planning tools, operational databases or spreadsheets. The results feed back into planning and decision-making. One mining project integrated GNOS directly with an existing product-movement planning tool.
GNOS combines discrete-event, agent-based and discrete-rate simulation within one digital twin. The model represents individual asset behaviour alongside queues, material flows and changes in system capacity.
GNOS represents physical detail where it affects the answer. For rail applications, the modelling accounts for train dynamics against gradient, load and braking conditions. The same simulation can include track geometry, elevation profiles and signalling layouts.
TEAM connects plans and operational data from SAP, planning tools, operational databases or spreadsheets. We use actual performance to validate the model, then test operating conditions that have not occurred in the field. The results feed into existing planning and decision processes. One mining project connected GNOS directly to the client's product-movement planning tool.
Compare production plans, dispatch rules and maintenance schedules against the constraints that govern execution. GNOS supports strategic, mid-term and execution planning.
Test higher demand, capacity changes or revised operating rules across the connected system. The model identifies where new constraints emerge and how they affect throughput or utilisation.
Compare maintenance strategies, shutdown timing and workforce requirements against production targets and asset availability. In a rail maintenance workshop, TEAM used GNOS to forecast demand and identify the staffing level at which additional labour produced diminishing productivity gains.
Test fleet, infrastructure and operating concepts against future production before the organisation commits capital. The model informs concept studies, capacity evaluations and long-term growth planning.
Depending on scope, a GNOS project provides a validated model, documented inputs and assumptions, scenario results, quantified measures, identified constraints and a comparison of the options tested.
TEAM interprets the output with the people responsible for the operation and sets out the implications for the decision. The measures follow the question. Analysis may compare capacity, cost, utilisation, energy, emissions, reliability or operational feasibility.
The scope may include model handover, training or ongoing support where the organisation needs to retain or extend the model.
Testing rail-to-port capacity against future demand.
A major African bulk rail operator needed to test whether its rail-to-port system could support close to twice its current demand.
TEAM used GNOS to identify the constraints and compared operational, fleet and infrastructure responses before the client decided where to invest.
Tell us which operating change, constraint or investment option you need to assess. TEAM will determine whether simulation suits the question and define what GNOS must represent.
