Aug 10, 2025
How simulations with cNode work — from data landscape to decision
by
Leonardo Bornhäußer
Introduction: why simulate rather than just forecast
Forecasts answer what is likely to happen. In uncertain markets, that’s not enough. Organisations also need: what if?
Example: “What happens to our liquidity if energy costs rise by 12% in Q4?”
cNode delivers precisely these answers. With simulations, teams can not only look ahead but also play through alternative paths — transparently, modularly and with clear explanations.
What are simulations in cNode?
A simulation is a data-driven, ML-based projection of alternative developments under defined assumptions.
The difference vs. forecasts:
Forecast → data-led, grounded in past behaviour
Simulation → assumption-led, interactive
Examples:
“How would higher churn affect cashflow?”
“How does our ESG score change if we invest in Scope-3 transparency?”
“Which business unit is exit-ready under different market conditions?”
Planning shifts from a static look ahead to an active steering instrument.
ML pipeline structure: Load → Univariate → Multivariate → Predict
The heart of cNode’s simulations is a multi-stage ML pipeline:
Phase | Purpose | Technologies / methods |
|---|---|---|
1. Load | Ingest & harmonise input data (ERP, CRM, external feeds, uploads) | REST API, Airflow, PostgreSQL + TimescaleDB |
2. Univariate | Isolated analysis & forecast per target (cashflow, costs, headcount) | XGBoost, ARIMA, Lasso, SHAP |
3. Multivariate | Link targets, context data & drivers into scenarios | Feature fusion, scenario trees, transfer learning |
4. Predict | Generate concrete scenarios incl. probabilities & KPI impact | Simulation engine, explainability layer (SHAP, LIME) |
Additional capabilities
Simulation parameters can be set via the Co-Pilot API using natural language.
Governance via compliance layer: audit trail, GDPR conformity, version control.
Types of simulations in cNode
Type | Objective | Example |
|---|---|---|
Sensitivity simulation | Test the effect of a parameter change | “+10% OPEX in Q3” |
Scenario comparison | Compare two or more development paths | “Best vs Base vs Worst Case” |
Risk-based simulation | Incorporate probability distributions | “Raw material price volatility” |
Target-value simulation (backcasting) | Define conditions for achieving a goal | “Break-even in Q2 — what revenue mix is required?” |
Output & visualisation
Simulation results are provided in multiple formats:
Delta KPIs (forecast vs simulation comparison)
Visualisations (time-series charts, impact matrices, SHAP explainers)
Text-based decision notes
All outputs are:
explainable (SHAP, LIME),
versioned (MLflow),
auditable (Haloframe).
Optionally, results can be embedded into BI tools or pushed via API into your ERP.
Conclusion: simulations as the core of modern enterprise steering
Simulations are more than forecasts: they make organisations resilient, action-ready and transparent.
With cNode, this capability moves from research (scenario planning, explainable AI) into practice — as a scalable decision logic for Finance, ESG, HR and more.
Next step: develop co-simulations across clusters — e.g., the interplay between OPEX, ESG and HR.
👉 Invitation: Which scenarios would you like to simulate with us?
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