Risk Diagnostics maps the risk spectrum.
What could happen? · ranges, not a commitment
Your team can hand-build a few scenarios before a meeting — best, base, worst — then guess at everything in between. Risk Diagnostics runs hundreds of plausible futures, solves each, and shows you exactly where you’re exposed.
Drivers are shapes, not numbers.
The average hides the expensive days. A driver’s shape — bimodal, fat-tailed or tight — is what a resilient decision is actually defended against. Each is learned as a distribution from your own transaction history.
Two regimes — a fast lane and a slow lane. The mean lands in the empty valley that rarely happens.
Most days cluster early, but a long right tail of expensive days drags the mean past the peak — the average overstates the typical week.
Every future lands within a hair of the mean. Here the single number is honest — but you only know that once you’ve seen the shape.
Variability enters the engine in its true shape.
Before anything is decided, the world is read honestly. This is what changes in the first weeks of a deployment — and what every downstream range depends on.
Distributions, not values
Lead time, demand, yield and cost enter as the shapes they actually have — learned per item, supplier, location and customer — not as a single static number that is wrong a different way every week.
Order lines, not buckets
Demand and supply enter at the grain decisions are made on — order lines, histories, real partners — not aggregated into buckets that lose whatever intelligence the detail held.
Signals, not surveys
Short-horizon sensing, causal structure and ecosystem signals enter as first-class inputs, so the engine reads the forces reshaping demand now rather than extrapolating the past.
Every plausible future, mapped.
Risk Diagnostics learns the shape of every driver and risk, samples them into many futures, and solves each — one optimal plan per future, sweeping the risk probabilities. The result is the full spectrum of KPI outcomes.
- ·Calibrates variability from your own history
- ·Runs many scenarios — one optimal solve per future
- ·Produces KPI ranges, bands and resilience metrics
- ·Reveals which drivers move which KPIs
Risk Diagnostics doesn’t tell you what to do. It tells you everything that could happen — so nothing surprises you. The decision comes next, in Resilience Optimization.
Driver calibration, in four steps.
Take any numerical object in the model, learn its shape from your own history, then choose how it is applied to build every future.
- 01
Any numerical object
Supplier lead time, demand, scrap rate, capacity, energy cost — anything the model carries.
- 02
Learn from past history
Observed in the transaction history of your source systems — ERP, CRM, MES.
- 03
Driver variability histogram
Its real distribution, per item, supplier, location and customer.
- 04
Stitch into iterations
Choose how each driver is applied — replace, add, multiply — to generate rich, realistic futures.