Enterprise resilience / Overview

See risk before it cascades.

Visualize how disruptions spread across operational dependencies and identify your most exposed business nodes.

Dependency exposure map

Directed links show who depends on whom. Select a node to inspect.

LIVE MODEL

Low impactElevatedSevere→ Dependency direction↔ Swipe on mobile

Exposure by business function

Illustrative financial-at-risk allocation

Active shock controls

Adjust assumptions to immediately update the model

Top affected entities

Ranked by simulated impact

Simulation, not a risk prediction

All sample organizations and financial exposures are fictional. Customize the network, dependency weights, and resilience scores before applying insights to a real setting.

Scenario engineering

Stress-test the system.

Choose a shock source, tune propagation parameters, and compare outcomes across the same dependency graph.

Shock scenario

Select one of four synthetic disruption profiles

Propagation model

Modify scenario severity, transmission, and impact horizon

Monte Carlo uncertainty band

300 seeded variations of severity and transmission

SIMULATED

Scenario outcomes

Relative modeled exposure, not observed loss

What propagates?

Top nodes after iterative transmission

Assumption: propagation is directional and bounded. Resilience dampens exposure, and repeated links do not generate additive double-counting. No probability of real-world failure is estimated.

Entity & dependency registry

Shape the network.

Edit critical services and supplier links. Each change updates exposure estimates immediately and remains on this device.

Entity registry

Resilience (0 to 100) is a synthetic damping indicator

Directed dependencies

Source → dependent, with transmission strength

Decision intelligence

Turn shocks into insight.

Review concentration, compare scenarios, and export a transparent snapshot of modeled risks.

Scenario comparison

Identical settings, different seeded disruptions

Dependency concentration

Direct downstream relationships; not a causal importance guarantee

Recommended reviews

Heuristics, not automated operational decisions

Export and portability

Download your scenario assumptions and model output

Data stays in your browser, unless you explicitly export or import a file. Browser storage can be cleared by your device or settings.

Transparency and limitations

Understand the model.

Inspectable, deterministic simulation assumptions to support learning and scenario conversations.

How propagation works

Inject a disruption

Shock profiles define fixed affected source entities and initial severity.

Transmit along dependencies

For each link A → B, incoming impact is impact(A) × link strength × transmission × vulnerability(B).

Bound and repeat

Each entity keeps the maximum impact seen so far. Five passes capture multi-step cascades without additive amplification.

Estimate illustrative exposure

Daily-at-risk is the sum of each entity's modeled impact fraction multiplied by its declared daily exposure. Horizon is a linear multiplier.

Mathematical specification

For each directed edge i → j:

vulnerability(j) = 0.25 + 0.75 × (1 − resilience(j)/100)
transfer = impact(i) × strength(i,j) × transmission × vulnerability(j)
impact(j) = max(previous impact(j), initial shock(j), transfer)

All impact values are capped at 100. Weighted values must be treated as illustrative units, not actuarial forecasts.

Model boundaries

No live data, forecasting model, insurance computation, automated control of infrastructure, or real-time security monitoring. Directed-edge topology and manually assigned weights may materially change results. Independence and uncertainty distributions have not been empirically calibrated.

Privacy & implementation

Pure HTML, CSS, and JavaScript. No external scripts, authentication, servers, cookies, paid compute, or outbound APIs. State uses localStorage and can be exported to JSON.

CascadeLab v1.0 · Hugdrigo portfolio · MIT licensed · Educational scenario intelligence