Digital twins
Process models connected to data and a scenario interface.
Current state · target scenarios · API
Digital decision engineering
Test complex business decisions before implementation
Digital twins, predictive analytics, optimization and controlled AI assistants for manufacturing, logistics and complex operational systems.
Solutions
From data to a verified action
Predictive analytics
Demand, workload, risk and event forecasts linked to clear actions.
Forecasting · risk · monitoring
Optimization
Best capacities, schedules, inventories, routes and operating policies.
MILP · CP-SAT · heuristics
AI assistants
Controlled assistants for analytics, knowledge, scenarios and workflows.
RAG · agents · human approval
Business first
We do not sell a platform or a polished animation
Technology follows the management question, available data, security constraints and the future operating model.
Principle Decision first — technology second
What the client receives
- 01Agreed scope and success measures
- 02Verified current-state model
- 03Target scenarios and trade-offs
- 04Management recommendation
- 05Working interface or assistant
- 06Transparent assumptions and limits
Industries
Systems where everything is connected
01ManufacturingCapacity, equipment, buffers, changeovers and investment.↗02Warehouses and terminalsFlows, zones, workforce, equipment and automation.↗03Supply chainsNetwork, inventory, service, transport and resilience.↗04TransportRoutes, schedules, fleet, infrastructure and disruptions.↗05Industrial assetsReliability, maintenance, failure risk and operating modes.↗06Service systemsCustomer flows, service levels, staffing and queues.↗
Approach
From a question to a working tool
- 01
Discovery
OutcomeProblem statement, scope and success measures
- 02
Data
OutcomeSources, gaps and assumptions register
- 03
Concept
OutcomeSolution architecture and validation plan
- 04
Current state
OutcomeVerified model and discrepancy report
- 05
Target scenarios
OutcomeOptions, risks and recommendation
- 06
Delivery
OutcomeWorking tool, documentation and training
- 07
Evolution
OutcomeSupport process and new versions
IdealAgile sprints
Short cycles, visible intermediate results and regular refinement based on data and feedback.
AvailableWaterfall project
Fixed stages and gates when requirements, data and approval procedures are already stable.
Technology map
Technology selected for the problem
We choose the architecture according to data, security requirements and the future operating environment.
Simulation
AnyLogic · anyLogistix · Amalgama · SimPy
Optimization
MILP · CP-SAT · OptQuest · heuristics
AI and agents
LLM · RAG · tools · evaluation
Data
Excel · SQL · ERP · WMS · MES · API
Delivery
Web interfaces · dashboards · private cloud · API
Human control
AI prepares and explains decisions
Agent active · critical actions require approval
Agents can collect context, prepare scenarios, run approved calculations, compare results and draft reports. A responsible person remains in control of critical actions.
Discuss an AI assistant →About SIMUNITY
Engineering confidence through evidence
We combine simulation, data, optimization and controlled AI. Trust comes from explicit responsibilities, reproducible calculations and knowledge transfer.
- 01
Transparent scope
Every conclusion remains connected to evidence and the original management question.
- 02
Documented assumptions
Every conclusion remains connected to evidence and the original management question.
- 03
Verified current state
Every conclusion remains connected to evidence and the original management question.
- 04
Reproducible scenarios
Every conclusion remains connected to evidence and the original management question.
First step
Describe the decision you need to test
We will define the smallest useful study and tell you if simulation is not the right tool.