Estimate cost of delay distribution

Translate probabilistic delivery delay into discounted contribution-value loss, permanent value decay, and governed penalties, including expected cost, tail cost, and the probability of material exposure.

Statistical audit & measurement Estimate CTOs Tech CEOs IT managers Team leads Investors

What it's for

Shows leaders and investors what schedule risk means in money—not another red ETA—and makes the value curve and tail assumptions visible.

What you give it

Every tool is a pure function: it computes from what you pass it and reads nothing on its own. Inputs split into evidence you supply from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

Field Type Role Required
delay_scenarios array of objects (4 fields) ≥ 2 items Evidence Yes
material_delay_cost number ≥ 0 Your calibration Optional
max_detail_rows integer ≥ 1, ≤ 500 Numerical control Optional
maximum_material_cost_probability number ≥ 0, ≤ 1 Your calibration Optional
on_time_period_values array of number ≥ 1 item Evidence Yes
period_discount_rate number ≥ -0.99, ≤ 10 Your calibration Yes
tail_probability number ≥ 0.001, ≤ 0.5 Your calibration Optional
value_decay_per_delay_period number ≥ 0, ≤ 1 Your calibration Optional

Each delay_scenarios record

Field Type Required
delay_periods integer (≥ 0, ≤ 10000) Yes
id string (non-empty) Yes
penalty number (≥ 0) Optional
probability number (≥ 0, ≤ 1) Yes
Example input 
{
  "delay_scenarios": [
    {
      "delay_periods": 0,
      "id": "on-time",
      "probability": 0.35
    },
    {
      "delay_periods": 1,
      "id": "one-quarter",
      "probability": 0.4
    },
    {
      "delay_periods": 2,
      "id": "two-quarters",
      "penalty": 50,
      "probability": 0.2
    },
    {
      "delay_periods": 4,
      "id": "four-quarters",
      "penalty": 150,
      "probability": 0.05
    }
  ],
  "material_delay_cost": 400,
  "maximum_material_cost_probability": 0.2,
  "on_time_period_values": [
    200,
    240,
    280,
    300,
    300,
    280,
    250,
    220
  ],
  "period_discount_rate": 0.025,
  "tail_probability": 0.1,
  "value_decay_per_delay_period": 0.05
}

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.

Example output 
{
  "assumptions": [
    "The on-time value curve is incremental contribution value, not gross revenue or activity volume.",
    "Delay shifts value within the fixed decision horizon and applies the declared permanent decay; values beyond the horizon are excluded deliberately.",
    "Scenario penalties are contractually or operationally governed and are not inferred from engineering activity."
  ],
  "configuration": {
    "maximum_material_cost_probability": 0.2,
    "period_discount_rate": 0.025,
    "scenario_count": 4,
    "tail_probability": 0.1,
    "value_decay_per_delay_period": 0.05,
    "value_horizon_periods": 8
  },
  "decision": "material_delay_value_exposure",
  "method": "discounted_cost_of_delay_distribution_v1",
  "scenario_diagnostics": [
    {
      "delay_cost": 1326.5718,
      "delay_periods": 4,
      "penalty": 150,
      "probability": 0.05,
      "scenario_id": "four-quarters"
    },
    {
      "delay_cost": 661.9221,
      "delay_periods": 2,
      "penalty": 50,
      "probability": 0.2,
      "scenario_id": "two-quarters"
    },
    {
      "delay_cost": 310.4983,
      "delay_periods": 1,
      "penalty": 0,
      "probability": 0.4,
      "scenario_id": "one-quarter"
    },
    {
      "delay_cost": 0,
      "delay_periods": 0,
      "penalty": 0,
      "probability": 0.35,
      "scenario_id": "on-time"

Truncated for display — the full payload is 59 lines.

How it works

Statistical audit & measurement — Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

  1. 1 Freeze one approved on-time incremental value curve, fixed decision horizon, delay scenarios/probabilities, discounting, permanent decay rule, and scenario-specific contractual penalties.
  2. 2 Shift and decay the value curve inside each delay scenario, subtract governed penalties, and price lost NPV relative to the on-time counterfactual without extending value beyond the horizon silently.
  3. 3 Aggregate expected, VaR, and CVaR delay cost, estimate the probability of crossing a material exposure, and report a probability-weighted marginal cost per delayed period.

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.

Assumptions & guardrails

  • Metric definitions, weights, aggregate grain, sampling, missingness, dependence, and comparison windows correspond to the management claim being audited.
  • The value curve is incremental contribution or cash value attributable to availability timing, not gross bookings or engineering output.
  • Delay scenarios reflect remaining work, dependencies, capacity, and current forecast vintage; penalties are contractually or operationally grounded.
  • Association, instability, or measurement quality is not a causal effect and must not be converted directly into an individual employment decision.
  • Cost of delay prices the submitted timing counterfactual; it does not establish causal revenue attribution or justify arbitrary urgency multipliers.

Minimum evidence

  • delay_scenarios: at least 2 rows/items
  • on_time_period_values: at least 1 rows/items
  • period_discount_rate: required and organization-defined

How to validate it

Validate on future periods or held-out aggregate units, compare with a simple baseline, and require stability across plausible metric definitions and decision thresholds.

Calibrating it to your org

Same for everyone

The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.

Specific to you

  • point-in-time delay scenarios with aligned probabilities and one incremental on-time contribution-value curve
  • value attribution, currency, horizon, discount rate, permanent decay, contractual penalties, material exposure, tail probability, and alert tolerance

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 20 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": "translate probabilistic delivery delay into discounted" }
  → finds "estimate_cost_of_delay_distribution"

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

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

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

Related tools

Allocate budget with CVAR constraint

Maximize expected portfolio return while keeping probability-weighted loss CVaR below a finance-owned tail-risk ceiling across aligned joint scenarios.

Constrained optimization

Calculate risk adjusted npv

Discount aligned scenario cash-flow paths, expose positive-NPV probability and loss VaR/CVaR, then apply an explicit finance-owned CVaR penalty to test a risk-adjusted investment hurdle.

Decision analysis

Construct deterministic project pareto frontier

Construct the exact practically nondominated frontier and successive Pareto layers across projects, products, vendors, or investments without hiding tradeoffs behind arbitrary score weights.

Statistical audit & measurement

Construct stochastic pareto frontier

Construct a stochastic Pareto frontier from aligned joint criterion scenarios using scenario-wise frontier membership and pairwise practical chance dominance rather than dominance of point estimates.

Statistical audit & measurement

Optimize budgeted initiative portfolio

Select the highest expected-value initiative portfolio under cash and multi-resource budgets while enforcing dependencies and mutual exclusions across aligned business scenarios.

Constrained optimization

Audit budget constraint binding

Audit whether a claimed budget constraint genuinely blocks value after dependency-feasible portfolio reallocation, separating current-plan inefficiency from scarcity with scenario CVaR and a discrete budget shadow price.

Constrained optimization

See every tool in Investment & portfolio choice →

Ready to See Your Engineering work clearly?

Request a free demo