Forecast vendor spend at risk

What it's for

Shows executives how much vendor spend can exceed budget in common bad states, which contracts dominate the tail, and whether apparent supplier diversification is real.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "correlations": [
    {
      "correlation": 0.8,
      "id": "shared-demand",
      "vendor_a": "cloud",
      "vendor_b": "data"
    }
  ],
  "seed": 83,
  "simulation_draws": 5000,
  "total_spend_budget": 200,
  "vendors": [
    {
      "coefficient_of_variation": 0.8,
      "contracted_minimum_spend": 20,
      "contractual_spend_cap": 500,
      "expected_unconstrained_spend": 100,
      "id": "cloud"
    },
    {
      "coefficient_of_variation": 0.8,
      "contracted_minimum_spend": 20,
      "contractual_spend_cap": 500,
      "expected_unconstrained_spend": 100,
      "id": "data"
    }
  ]
}

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "assumptions": [
    "Unconstrained vendor spend follows the submitted lognormal marginal distributions joined by a Gaussian copula on latent log-spend shocks; means, coefficients of variation, floors, caps, currency, tax, credits, timing and horizon are finance-reconciled and comparable.",
    "Pairwise correlations represent prospective common demand, price, FX, usage and dependency shocks after aggregation; omitted tail dependence, nonlinear pricing, tier interactions, renewal jumps, outages, mergers, renegotiation, termination and correlated caps can materially understate exposure.",
    "Incremental vendor contributions reconcile the excess of total-spend CVaR over expected spend under the portfolio's own tail draws; they are dependence-sensitive risk accounting, not causal blame or a procurement performance score.",
    "Vendors are aggregate counterparties, never proxies for named employees; spend-at-risk is not a forecast guarantee, legal interpretation, credit opinion, or automatic procurement, termination, investigation, or investment instruction."
  ],
  "configuration": {
    "correlations_apply_to_latent_log_spend_shocks": true,
    "maximum_budget_overrun_probability": 0.2,
    "maximum_single_tail_spend_share": 1,
    "maximum_total_spend_cvar": null,
    "seed": 83,
    "simulation_confidence_level": 0.95,
    "tail_probability": 0.1
  },
  "decision": "vendor_spend_exceeds_governed_budget_or_tail_risk_gate",
  "method": "gaussian_copula_lognormal_vendor_spend_at_risk_v1",
  "summary": {
    "budget_overrun_cvar": 322.6949,
    "budget_overrun_probability": 0.3714,
    "budget_overrun_probability_interval": [
      0.3580072,
      0.3847928
    ],
    "expected_budget_overrun": 51.3173,
    "expected_total_spend": 199.7911,
    "incremental_tail_spend_contribution_reconciliation_residual": 0,
    "maximum_tail_spend_share": 0.5064,
    "passes_budget_overrun_probability_gate": false,
    "passes_tail_spend_concentration_gate": true,
    "passes_total_spend_cvar_gate": true,
    "simulation_draws": 5000,
    "tail_spend_concentration_hhi": 0.5001,
    "total_spend_budget": 200,
    "total_spend_cvar": 522.6949,
    "total_spend_value_at_risk": 379.4761,
    "vendor_count": 2
  },
  "truncated_vendor_count": 0,
  "vendor_diagnostics": [
    {
      "cap_binding_probability": 0.0044,
      "contracted_minimum_spend": 20,

How it works

Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

1. 1 Freeze finance-reconciled expected unconstrained spend, coefficient of variation, contractual floor and cap for each vendor, then specify a positive-semidefinite latent correlation matrix for common usage, price, demand, FX and dependency shocks.
2. 2 Simulate correlated lognormal spend, apply enforceable contract bounds, and calculate expected spend, VaR, CVaR, budget-overrun probability with simulation precision, expected and tail overrun, and concentration.
3. 3 Use the portfolio's own worst spend draws to attribute tail-conditional and incremental tail spend by vendor, reconcile contributions exactly, and apply budget, absolute CVaR and single-vendor concentration gates.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "forecast correlated vendor spend with lognormal" }
  → finds "forecast_vendor_spend_at_risk"

gitrevio_capability_describe
  { "capability_id": "forecast_vendor_spend_at_risk" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "forecast_vendor_spend_at_risk", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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