Forecast technical asset obsolescence
Forecast product retirement, technical obsolescence, security/compliance retirement and vendor/platform end as competing technical-asset risks using complete right-censored lifecycle episodes, age-specific empirical-Bayes Dirichlet hazards, coherent common scenarios and current-age simulation of stranded carrying value plus foregone contribution.
What it's for
Gives leadership an age-conditioned probability and financial exposure for why technical assets may become obsolete—rather than a generic code-age or technical-debt score.
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 |
|---|---|---|---|
| current_assets | array of objects (7 fields) ≥ 1 item | Evidence | Yes |
| historical_asset_episodes | array of objects (5 fields) ≥ 5 items | Evidence | Yes |
| horizon_periods | integer ≥ 1, ≤ 120 | Your calibration | Optional |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| minimum_exits_per_class | integer ≥ 0 | Your calibration | Optional |
| prior_strength | number > 0 | Your calibration | Optional |
| scenarios | array of objects (6 fields) ≥ 2 items | Evidence | Yes |
| seed | integer ≥ 0, ≤ 2147483647 | Numerical control | Optional |
| simulations | integer ≥ 100, ≤ 100000 | Numerical control | Optional |
| tail_probability | number > 0, ≤ 0.5 | Your calibration | Optional |
Each current_assets
record
| Field | Type | Required |
|---|---|---|
| age_periods | integer (≥ 0, ≤ 600) | Yes |
| asset_class | string (non-empty) | Yes |
| carrying_value | number (≥ 0) | Yes |
| evidence_verified | boolean | Yes |
| id | string (non-empty) | Yes |
| period_contribution_at_risk | number (≥ 0) | Yes |
| remaining_amortization_periods | integer (≥ 0, ≤ 600) | Yes |
{
"current_assets": [
{
"age_periods": 4,
"asset_class": "service",
"carrying_value": 100,
"evidence_verified": true,
"id": "current-service",
"period_contribution_at_risk": 20,
"remaining_amortization_periods": 10
}
],
"historical_asset_episodes": [
{
"asset_class": "service",
"evidence_verified": true,
"id": "asset-episode-service-0",
"observed_age_periods": 3,
"outcome": "product_retirement"
},
{
"asset_class": "service",
"evidence_verified": true,
"id": "asset-episode-service-1",
"observed_age_periods": 4,
"outcome": "technical_obsolescence"
},
{
"asset_class": "service",
"evidence_verified": true,
"id": "asset-episode-service-2",
"observed_age_periods": 5,
"outcome": "security_or_compliance"
},
{
"asset_class": "service",
"evidence_verified": true,
"id": "asset-episode-service-3",
"observed_age_periods": 6,
"outcome": "vendor_or_platform_end"
},
{
"asset_class": "service",
"evidence_verified": true,Truncated for display — the full payload is 257 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.
{
"asset_diagnostics": [
{
"age_extrapolation_used": true,
"asset_class": "service",
"asset_id": "current-service",
"class_support_sufficient": true,
"evidence_verified": true,
"exit_cause_probabilities": {
"product_retirement": 0.3,
"security_or_compliance": 0.16,
"technical_obsolescence": 0.21,
"vendor_or_platform_end": 0.33
},
"exit_period_if_within_horizon": {
"p10": 1,
"p50": 3,
"p90": 4
},
"expected_lost_contribution": 70.48,
"expected_management_value_exposure": 145.85,
"expected_stranded_carrying_value": 75.37,
"historical_class_exit_count": 12,
"probability_exit_by_horizon": 1,
"tail_conditional_management_value_exposure": 216.1
}
],
"assumptions": [
"Episodes begin at a stable placed-in-service origin, retain every governed exit cause and honest right-censoring, and share a comparable cadence and process epoch. Censoring is noninformative conditional on modeled class or separately stressed.",
"Cause-specific discrete hazards are partially pooled toward the tenant-wide age risk set with a Dirichlet empirical-Bayes prior. Scenario multipliers preserve a coherent common future; age beyond support reuses the last supported hazard and is visibly flagged.",
"Stranded carrying value plus foregone contribution is a management exposure metric, not an accounting impairment amount, fair value, tax result or recoverable-amount test. Finance/controller owners must interpret book-value consequences separately.",
"The forecast concerns aggregate technical assets and value streams, never employee, vendor or customer quality. It does not authorize retirement, migration, write-down, procurement or staffing action."
],
"configuration": {
"age_beyond_support_policy": "reuse_last_supported_age_hazard_and_flag",
"exit_causes": [
"product_retirement",
"technical_obsolescence",
"security_or_compliance",
"vendor_or_platform_end"
],
"horizon_periods": 6,
"minimum_exits_per_class": 2,
"prior_strength": 10,Truncated for display — the full payload is 82 lines.
How it works
Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.
- 1 Reconstruct age risk sets from every exited or honestly censored tenant asset episode and retain four mutually exclusive governed retirement causes.
- 2 Partially pool class-age survive/cause probabilities toward the tenant-wide risk set with a Dirichlet empirical-Bayes prior, retaining class support and age extrapolation flags.
- 3 Simulate current assets conditionally from their observed age under shared scenario hazard and value multipliers, then report cause, timing, stranded carrying-value, contribution-loss and portfolio-tail distributions.
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
- Training examples precede their outcomes, censoring and unresolved work are represented, and deployment populations remain comparable to validation cohorts.
- Placed-in-service origin, cadence, class and cause definitions are stable; censoring is honest; scenario multipliers preserve common futures; management exposure is not confused with accounting impairment.
- A predictive interval or risk estimate is not a deadline promise, causal explanation, or individual-performance judgment.
- The forecast is not an impairment amount, fair value, accounting opinion or retirement authorization, and age beyond observed support is explicitly flagged rather than silently extrapolated.
Minimum evidence
- historical_asset_episodes: at least 5 rows/items
- current_assets: at least 1 rows/items
- scenarios: at least 2 rows/items
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
- extraction-complete tenant lifecycle cohorts and current as-of asset snapshot joined to finance/product exposure, preserving silent survivors as censored and cause definitions across process epochs
- asset origin/exit/censoring/cause/class definitions, cadence and epoch, evidence completeness, empirical-Bayes prior/support, age extrapolation, scenario dependence and multipliers, horizon/tail level, carrying/contribution perimeter, pseudonymization and accountable owners
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": "forecast product retirement technical obsolescence securitycompliance" }
→ finds "forecast_technical_asset_obsolescence"
gitrevio_capability_describe
{ "capability_id": "forecast_technical_asset_obsolescence" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "forecast_technical_asset_obsolescence", "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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