Digital twin of your manufacturing and supply chain. Web-based execution.

Most simulation tools require extensive manual setup, lack integration between manufacturing and logistics domains, and focus on visualisation over operational accuracy. GNOS runs detailed models through a web browser with distributed parallel execution, eliminating desktop software installation and enabling rapid scenario comparison. Our in-built AI-assisted configuration supports model setup by suggesting parameters, pre-populating operational logic, and flagging missing constraints before simulation runs.​

Compare alternative replenishment strategies in parallel and evaluate trade-offs between inventory investment and production continuity risk. Scenario outputs quantify cost implications and operational resilience under different supply chain configurations to support data-driven decisions on inventory policy and supplier management.​

GNOS gives you the capability to test recovery speed and supply chain resilience when disruptions occur. Simulations reveal how quickly production resumes when suppliers resume shipments, when alternative components become available, or when emergency routes bypass congested logistics nodes. You can also refine recovery triggers e.g. reorder thresholds, alternate supplier activation rules, or network rerouting logic; then measure how each recovery strategy affects total disruption duration and recovery costs.

Compare scenarios to find throughput gains without expanding floor space or adding major equipment. Measure line balancing effectiveness and work distribution to determine capacity improvements before implementation. Outputs quantify expected throughput increases, labour efficiency gains, and equipment utilisation improvements across each tested configuration.

Engineers set processing rates, failure intervals, and scheduled maintenance windows for each asset, then configure material handling logic for conveyors, AGVs, and automated storage systems. Import facility layouts and equipment rules from existing CAD or planning formats to preserve operational logic while testing alternative configurations. Adjust conveyor speeds to reduce station bottlenecks or resequence AGV paths to eliminate transfer point delays. GNOS adjusts task assignments and dispatch based on real system states, not just predefined scripts, ensuring simulations reflect dynamic operational responses.

GNOS models daily and weekly order flows with variations in stock, staffing, and delivery capacity to measure impact on customer lead times. Test delivery schedules, route combinations, and warehouse locations across national networks to reveal how placement decisions affect travel times, fulfilment consistency, and cost per delivery. Coordinate loading operations, vehicle dispatch, berth scheduling, and container handoffs to identify where delays occur and which resource changes improve throughput. Visualisations show cascading effects throughout the network—when a delivery delay causes unloading to be postponed, the simulation traces how congestion propagates through downstream operations.​

Scenario outputs quantify whether congestion originates from warehouse picking, dispatch timing, or fleet availability. Compare configurations under different demand patterns to validate infrastructure investments before capital decisions are made.

Time usage models show where resources wait for tasks, process materials, or transition between operations, revealing bottlenecks hidden in aggregate performance data. Measure throughput rates, cycle times, queue lengths, and storage levels at every timestep to identify whether constraints originate from equipment capacity, task sequencing, or resource allocation.​

Compare utilisation patterns across scenarios to validate which operational changes deliver measurable improvements. Outputs quantify expected gains before physical implementation, supporting data-driven decisions on equipment investment, maintenance scheduling, and workflow redesign.