Calculate earned value forecast
Turn period-level planned value, accepted earned value, and actual cost into a correlated Bayesian CPI/SPI distribution for final cost, completion period, budget overrun, and deadline miss, with classical EAC cross-checks and an early-progress abstention gate.
What it's for
Upgrades familiar earned-value reporting into an uncertainty-aware executive forecast that shows the probability of budget and deadline failure instead of one deterministic EAC number.
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.
| Field | Type | Role | Required |
|---|---|---|---|
| budget_at_completion | number > 0 | Your calibration | Yes |
| deadline_period | number ≥ 0 | Your calibration | Optional |
| elapsed_periods | number ≥ 0 | Your calibration | Yes |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| maximum_budget_overrun_probability | number ≥ 0, ≤ 1 | Your calibration | Optional |
| maximum_deadline_miss_probability | number ≥ 0, ≤ 1 | Your calibration | Optional |
| minimum_earned_fraction_for_forecast | number ≥ 0, ≤ 1 | Your calibration | Optional |
| periods | array of objects (5 fields) ≥ 4 items | Evidence | Yes |
| planned_duration_periods | number > 0 | Your calibration | Yes |
| prior_cpi_mean | number ≥ 0.01, ≤ 20 | Your calibration | Optional |
| prior_effective_periods | number ≥ 0.1, ≤ 10000 | Your calibration | Optional |
| prior_log_standard_deviation | number ≥ 0.001, ≤ 3 | Your calibration | Optional |
| prior_spi_mean | number ≥ 0.01, ≤ 20 | Your calibration | Optional |
| seed | integer ≥ 0, ≤ 4294967295 | Numerical control | Optional |
| simulation_draws | integer ≥ 1000, ≤ 200000 | Numerical control | Optional |
Each periods
record
| Field | Type | Required |
|---|---|---|
| actual_cost_increment | number (> 0) | Yes |
| earned_value_increment | number (> 0) | Yes |
| id | string (non-empty) | Yes |
| period | integer (≥ 1) | Yes |
| planned_value_increment | number (> 0) | Yes |
{
"budget_at_completion": 300,
"deadline_period": 10,
"elapsed_periods": 6,
"periods": [
{
"actual_cost_increment": 26,
"earned_value_increment": 31,
"id": "delivery-period-1",
"period": 1,
"planned_value_increment": 26
},
{
"actual_cost_increment": 27,
"earned_value_increment": 30,
"id": "delivery-period-2",
"period": 2,
"planned_value_increment": 25
},
{
"actual_cost_increment": 25,
"earned_value_increment": 31,
"id": "delivery-period-3",
"period": 3,
"planned_value_increment": 26
},
{
"actual_cost_increment": 26,
"earned_value_increment": 30,
"id": "delivery-period-4",
"period": 4,
"planned_value_increment": 25
},
{
"actual_cost_increment": 27,
"earned_value_increment": 31,
"id": "delivery-period-5",
"period": 5,
"planned_value_increment": 26
},
{
"actual_cost_increment": 25,
"earned_value_increment": 30,
"id": "delivery-period-6",Truncated for display — the full payload is 53 lines.
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": [
"Planned value, earned value and actual cost use one immutable scope baseline, currency, accounting perimeter and period cadence; scope changes are formally rebaselined rather than retroactively rewriting performance history.",
"Future CPI and SPI are represented by the submitted organization-specific log-performance prior plus current period variation; the bivariate lognormal predictive model preserves observed cost-schedule dependence but does not model a structural break, discrete milestone, dependency, financing or capacity regime unless reflected in calibration.",
"Earned value must represent completed scope under an acceptance rule, not commits, story points created, hours logged or percent-complete opinion; the forecast is not causal evidence, a delivery guarantee, revenue recognition or an accounting estimate.",
"The risk gates support an aggregate project review. They do not authorize employment action, blame, budget change, customer commitment or cancellation without scope, dependency, quality, financing and accountable-owner review."
],
"classical_cross_checks": {
"eac_cpi": 255.7377,
"eac_cpi_times_spi": 239.3873,
"eac_remaining_at_budget_rate": 273
},
"configuration": {
"budget_at_completion": 300,
"deadline_period": 10,
"elapsed_periods": 6,
"maximum_budget_overrun_probability": 0.2,
"maximum_deadline_miss_probability": 0.2,
"minimum_earned_fraction_for_forecast": 0.05,
"planned_duration_periods": 10,
"posterior_log_cpi_spi_mean": [
0.096,
0.1075
],
"posterior_predictive_log_correlation": -0.0108,
"prior_cpi_mean": 1,
"prior_effective_periods": 4,
"prior_log_standard_deviation": 0.08,
"prior_spi_mean": 1,
"seed": 85,
"simulation_draws": 5000
},
"decision": "earned_value_forecast_clears_budget_and_deadline_gates",
"method": "bayesian_correlated_cpi_spi_earned_value_forecast_v1",
"period_diagnostics": [
{
"cumulative_cpi": 1.1923,
"cumulative_spi": 1.1923,
"incremental_cpi": 1.1923,
"incremental_spi": 1.1923,
"period": 1,
"period_id": "delivery-period-1"
},
{Truncated for display — the full payload is 110 lines.
How it works
Sequential Bayesian & bandits — Learn while deciding — update beliefs as evidence arrives and choose where the next unit of effort is worth spending.
- 1 Reconcile at least four immutable period increments to one scope baseline, currency, earned-value acceptance rule, budget and planned duration, then compute incremental and cumulative CPI and SPI.
- 2 Combine the tenant-governed bivariate log-performance prior with current period log CPI/SPI variation, retain cost-schedule correlation, simulate posterior predictive performance, and propagate it through remaining budgeted work and duration.
- 3 Return p10/p50/p90 final cost and completion period, budget/deadline probabilities, classical formula cross-checks and an explicit abstention until enough accepted scope has actually been earned.
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
- The likelihood or reward model, prior support, action logging, delayed outcomes, and any stationarity assumptions match the deployment process.
- The scope baseline and earned-value rule are stable; current period performance plus the local prior transport to remaining work; quality, dependencies, milestones, capacity, financing and structural breaks are represented or separately stress-tested.
- Posterior probability and adaptive selection are model-conditional; they are not substitutes for randomized propensities or guaranteed safety.
- Earned value means accepted scope—not commits, story points created, hours or subjective percent complete—and the predictive distribution is neither a delivery guarantee, accounting estimate, causal diagnosis nor employee score.
Minimum evidence
- periods: at least 4 rows/items
- budget_at_completion: required and organization-defined
- planned_duration_periods: required and organization-defined
- elapsed_periods: required and organization-defined
How to validate it
Use chronological train/calibration/test windows, compare proper scores and decision value with a simple baseline, and recalibrate only from outcomes resolved after prediction time.
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
- tenant-calibrated bivariate log CPI/SPI prior from comparable point-in-time project snapshots with final cost and completion outcomes, retaining cost-schedule dependence and formal rebaseline lineage
- scope baseline and version, acceptance rule, value/cost unit and accounting perimeter, cadence, comparable-project cohort, prior mean/dispersion/strength, progress sufficiency, budget/deadline risk limits, simulation precision, and forecast review owner
Calibration workflow
- 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
- 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 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
- 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 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
- 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": "turn periodlevel planned value accepted earned" }
→ finds "calculate_earned_value_forecast"
gitrevio_capability_describe
{ "capability_id": "calculate_earned_value_forecast" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "calculate_earned_value_forecast", "arguments": { ... } }
→ returns the result shown aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.
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