Calculate opportunity cost of WIP

What it's for

Translates excess parallel work into the value lost while higher-value outcomes wait, and shows the assumptions behind a focus-first counterfactual.

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.

{
  "material_opportunity_cost": 100,
  "scenario_probabilities": [
    0.2,
    0.5,
    0.3
  ],
  "total_capacity_per_period": 1,
  "work_items": [
    {
      "current_expected_completion_period": 10,
      "delay_value_per_period_scenarios": [
        20,
        15,
        10
      ],
      "id": "large-platform",
      "remaining_effort": 10
    },
    {
      "current_expected_completion_period": 12,
      "delay_value_per_period_scenarios": [
        80,
        60,
        40
      ],
      "id": "small-revenue",
      "remaining_effort": 2
    }
  ]
}

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": [
    "Items are independently executable on one fungible constrained capacity pool; remaining effort and per-period delay value are comparable and incremental.",
    "The Smith-rule focus sequence minimizes expected weighted completion cost only under the declared deterministic-effort, no-release-date, no-preemption, single-pool abstraction.",
    "This is an economic counterfactual for aggregate WIP policy, not a directive to interrupt unsafe work or a performance judgment about people carrying concurrent obligations."
  ],
  "configuration": {
    "material_opportunity_cost": 100,
    "scenario_count": 3,
    "tail_probability": 0.1,
    "total_capacity_per_period": 1
  },
  "decision": "wip_opportunity_cost_material",
  "method": "wip_smith_rule_value_delay_counterfactual_v1",
  "summary": {
    "cvar_focus_sequence_disadvantage": -380,
    "expected_current_delay_cost": 841,
    "expected_focused_delay_cost": 290,
    "expected_wip_opportunity_cost": 551,
    "focus_sequence_ids": [
      "small-revenue",
      "large-platform"
    ],
    "probability_focus_sequence_reduces_delay_cost": 1,
    "work_item_count": 2
  },
  "truncated_work_item_count": 0,
  "work_item_diagnostics": [
    {
      "current_expected_completion_period": 12,
      "expected_delay_value_per_period": 58,
      "expected_item_opportunity_cost": 580,
      "focus_sequence_rank": 1,
      "focused_completion_period": 2,
      "remaining_effort": 2,
      "work_item_id": "small-revenue"
    },
    {
      "current_expected_completion_period": 10,
      "expected_delay_value_per_period": 14.5,
      "expected_item_opportunity_cost": -29,
      "focus_sequence_rank": 2,
      "focused_completion_period": 12,
      "remaining_effort": 10,

How it works

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

1. 1 Freeze the active aggregate work boundary, comparable remaining effort, one fungible constrained capacity pool, current completion forecasts, and finance-approved incremental delay value paths.
2. 2 Order work by remaining effort divided by expected delay value, calculate cumulative focus completion times, and compare scenario-wise weighted completion cost with the current plan.
3. 3 Report expected opportunity cost, probability the focus policy helps, tail disadvantage, and abstain from operational prescription when release dates, safety, dependencies, preemption, or specialist pools violate the scheduling abstraction.

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": "quantify the expected valuedelay cost of" }
  → finds "calculate_opportunity_cost_of_wip"

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

gitrevio_capability_run
  { "capability_id": "calculate_opportunity_cost_of_wip", "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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