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9a961f7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | # Forge Industrial Intelligence Pack — Schema
One row = one complete operational scenario lifecycle. All records share the same fourteen top-level fields.
Schema version: `1.0.0-forge-industrial-sample`
## Top-level fields
### `schema_version` — string
Schema identifier. Constant within a sample release.
### `event` — struct
Identifier fields and the overall status/severity for the lifecycle.
| Field | Type | Notes |
|---|---|---|
| `id` | string | Stable event identifier, e.g., `FORGE-100000`. |
| `trace_id` | string (UUID) | Cross-links telemetry within the lifecycle. |
| `timestamp` | string (ISO-8601) | Scenario anchor time. |
| `scenario` | string | One of: `port_spillover_surge`, `power_constrained_ev_yard`, `labor_gap_service_degradation`, `cold_chain_excursion_risk`, `tenant_hypergrowth_overrun`, `last_mile_cutoff_compression`, `site_selection_power_arbitrage`. |
| `severity` | string | `medium`, `high`, `critical`. |
| `status` | string | `decision_pending`, `triaged`, `confirmed`. |
| `confidence` | double | 0–1 confidence of the event label. |
### `organization` — struct
Market-level context for the facility/portfolio.
| Field | Type | Notes |
|---|---|---|
| `sector` | string | Always `industrial_real_estate`. |
| `market` | string | Generic submarket archetype (e.g., `Dallas-Fort Worth`, `Los Angeles`, `Chicago`, `Atlanta`, `New Jersey`, `Phoenix`). Used as a type-label, not a specific-property reference. |
| `submarket` | string | Submarket handle (e.g., `Inland Port`, `Inland Empire`, `Joliet`, `West Valley`). |
| `region` | string | Regional bucket (`south_central_us`, `west_coast_us`, etc.). |
| `environment` | string | Deployment bucket (e.g., `production_portfolio`). |
| `port_proximity_km` | int | Proximity to major port, km. |
| `airport_proximity_km` | int | Proximity to major airport, km. |
| `highway_access_score` | double | 0–1. |
| `labor_tightness` | double | 0–1. |
| `grid_headroom_mw` | double | Available grid capacity (MW). |
| `industrial_vacancy_pct` | double | Submarket vacancy rate (percent). |
| `base_rent_psf_yr` | double | Submarket base rent ($/sqft/yr). |
| `population_density` | int | People per sq km. |
| `development_cycle_days` | int | Typical time-to-delivery for spec construction. |
### `identity_context` — struct
Decision owner and authorization lineage.
| Field | Type | Notes |
|---|---|---|
| `principal_id` | string | Synthetic identity handle (e.g., `ops://tnt-XXX/node/fac-YYY`). |
| `decision_lineage` | list<struct> | Ordered `role` / `action` / `authority_level` entries showing the approval chain. |
| `auth_method` | string | How the decision was authenticated (e.g., `tower_dashboard`). |
| `is_automated_recommendation` | bool | Whether the decision started as an automated rec. |
| `stakeholder_latency_hours` | int | Hours of stakeholder lag. |
| `meeting_load` | string | `low` / `moderate` / `high`. |
### `vulnerability` — struct
Risk taxonomy and severity vector.
| Field | Type | Notes |
|---|---|---|
| `class` | string | Scenario-class label (same as `simulation.ground_truth_label`). |
| `risk_taxonomy` | list<string> | Ordered taxonomy codes (e.g., `LOG-LOC-001`, `LOG-DEM-004`). |
| `exposure` | string | Primary exposure dimension (e.g., `demand_pressure`, `service_risk`). |
| `severity_model.base_score` | double | 0–10 severity base. |
| `severity_model.vector` | string | Vector string encoding severity dimensions (e.g., `LOG:3.1/EX:O/FR:C/...`). |
### `tenant_context` — struct
Synthetic tenant profile tied to this scenario.
| Field | Type | Notes |
|---|---|---|
| `tenant_id` | string | Synthetic tenant identifier (e.g., `TNT-189FCA4461`). |
| `industry` | string | `ecommerce`, `3pl`, `retail_distribution`, `cold_chain`, `industrial_manufacturing`. |
| `truck_profile` | string | `parcel_heavy`, `mixed_freight`, `palletized`, `reefer`, `inbound_component`. |
| `order_growth_rate` | double | Growth rate delta. |
| `sqft_footprint` | int | Facility sqft. |
| `expansion_open` | bool | Whether the tenant is open to expanding. |
| `retention_priority` | string | `standard`, `high`, `strategic`. |
| `risk_of_churn` | double | 0–1 churn risk. |
### `facility_context` — struct
Building-level attributes: capacity, energy, clear heights, dock counts, refrigeration, automation level.
### `portfolio_context` — struct
Network-level dynamics: submarket share, adjacent-node capacity, cross-site elasticity.
### `telemetry_stream` — list<struct>
Variable-length ordered telemetry readings representing the evolving system state across the scenario. Each element includes an event-name label matching one item in the scenario's event sequence, plus relevant signal readings (dock utilization, queue length, power load, service commitment risk, truck turn time, temperature delta, etc.).
### `detection` — struct
Anomaly-signature metadata.
| Field | Type | Notes |
|---|---|---|
| `analytic_family` | string | Analytic family label (e.g., `industrial_real_estate_behavioral_intelligence`). |
| `primary_risk_class` | string | Primary risk dimension. |
| `rule_logic` | string | SQL-like rule expression for the detector. |
| `baseline_deviation` | string | Short English description of the deviation pattern. |
| `anomaly_score` | double | 0–1. |
| `confidence_band` | string | `low` / `medium` / `high` / `very_high`. |
| `forecast_pressure_delta` | double | Delta vs baseline forecast. |
| `signal_conflicts` | list<string> | Conflicting signal labels, if any. |
### `forecast` — struct
Forward-looking predictions tied to this scenario: demand shift, occupancy trajectory, pricing outlook.
### `impact` — struct
Economic consequences: revenue delta, NOI impact, capex required, churn risk delta.
### `response` — struct
Recommended actions and execution context.
| Field | Type | Notes |
|---|---|---|
| `recommended_actions` | list<string> | Top actions ranked by score. |
| `primary_action` | string | Selected action (e.g., `shift_overflow_to_satellite`). |
| `primary_action_score` | double | Scoring metric for the primary action. |
| `primary_action_reason` | string | Short reason code. |
| `alternative_actions` | list<struct> | `action` / `score` / `reason` triples. |
| `decision_owner` | string | Role who owns the decision (e.g., `operations_lead`, `market_officer`, `development_committee`). |
| `execution_window_days` | int | Execution SLA. |
| `playbook_id` | string | Synthetic playbook identifier, prefixed `FORGE-PB-`. |
| `capex_gate_required` | bool | Whether capex approval is required. |
| `stakeholders` | list<string> | Additional stakeholder roles. |
| `expected_operational_outcome` | string | `partial_success`, `success`, `mitigation`, etc. |
| `recommended_tradeoff` | string | Short tradeoff description (e.g., `preserve_capital_with_higher_latency_risk`). |
| `execution_risk_band` | string | `low`, `moderate`, `elevated`, `high`. |
### `simulation` — struct
Simulation engine provenance and ground-truth labels.
| Field | Type | Notes |
|---|---|---|
| `synthetic` | bool | Always `true`. |
| `engine` | string | Simulation engine label (`forge_industrial_sim_v1`). |
| `causal_coherence` | string | Coherence mode descriptor (e.g., `rich_schema_with_balanced_policy`). |
| `friction_profile` | struct | Numeric friction dimensions: `budget_pressure`, `labor_shortage`, `power_stress`, `demand_pressure`, `service_risk`, `portfolio_tightness`, `capital_availability`, `entitlement_friction`, `weather_disruption`, `upside_mode` (bool). |
| `ground_truth_label` | string | Scenario class (e.g., `Network_Optimization`, `Labor_Constraint`, `Temperature_Control_Risk`). |
| `intended_use` | list<string> | ML use-case tags (e.g., `forecasting`, `site_selection`, `tenant_expansion_decisions`). |
## Distribution of this sample
- 10,000 lifecycles total.
- Severity: balanced across `medium` / `high` / `critical` (~3,300 each).
- Status: balanced across `decision_pending` / `triaged` / `confirmed` (~3,300 each).
- Scenario: balanced across 7 scenario classes (~1,400 each).
- Market: balanced across 6 submarket archetypes (~1,600 each).
- Industry: balanced across 5 tenant archetypes (~2,000 each).
## Sanitization notes
- Internal identifier prefix (`PLG-REAL-*`) has been normalized to `FORGE-*`.
- Internal playbook prefix (`PLG-PB-*`) has been normalized to `FORGE-PB-*`.
- Internal engine label (`SIMA-inspired industrial reality generator`) has been normalized to `forge_industrial_sim_v1`.
- Market names (Dallas-Fort Worth, LA, Chicago, Atlanta, NJ, Phoenix) are used as submarket archetypes for industrial-real-estate type labels, not specific property or operator references.
- No real facility telemetry, real tenant identities, real portfolio NOI, or identifiable stakeholder data are present.
## Relationship to the full pack
The production pack scales to 5M+ lifecycles with expanded market and submarket coverage, richer per-step telemetry, additional scenario classes (ESG signals, geopolitical supply disruption, labor automation events, bonded warehouse flows), and multi-year longitudinal traces per tenant. See the pack card for commercial access.
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