solsticestudioai/forge-industrial-pack

Forge Industrial Intelligence Pack (Sample)

A synthetic industrial real estate, logistics, and operations decision-telemetry dataset for anomaly detection, forecasting, decision-support, and policy-optimization research. Each row is a complete operational scenario lifecycle — from signal emergence through telemetry evolution, detection, forecast, impact, and recommended action — with 14 top-level context fields tying market, tenant, facility, portfolio, and decision-owner layers together.

Built by SolsticeAI as a free sample of a larger commercial pack. 100% synthetic. No real facility data, no real tenant names, no real portfolio telemetry — market names (Dallas-Fort Worth, LA, Chicago, etc.) refer to generic submarket archetypes, not specific properties or operators.

What is included

File	Rows	Format	Purpose
forge_industrial_sample.parquet	10,000	Parquet	Columnar, typed, best for analytics
forge_industrial_sample.jsonl	10,000	JSON Lines	Streaming / LLM training friendly

This sample: 10,000 operational scenario lifecycles.
Severity tiers: medium, high, critical (3,300 each)
Status: decision_pending, confirmed, triaged (3,300 each)
Scenario classes (7): 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
Markets (6): Dallas-Fort Worth, Los Angeles, Chicago, Atlanta, New Jersey, Phoenix
Tenant industries (5): ecommerce, 3PL, retail distribution, cold chain, industrial manufacturing
Action library: 14+ playbook actions across labor, power, capex, tenant, network, and pricing levers

Record structure

Each record is one operational scenario lifecycle with 14 top-level fields:

Field	Type	Contents
schema_version	string	Pack schema version (1.0.0-forge-industrial-sample)
event	struct	id, trace_id, timestamp, scenario, severity, status, confidence
organization	struct	sector, market, submarket, region, environment, port/airport proximity, grid headroom, vacancy, base rent, development cycle
identity_context	struct	principal_id, decision_lineage[] (role/action/authority), auth_method, stakeholder_latency_hours, meeting_load
vulnerability	struct	class, risk_taxonomy[], exposure, severity_model (base_score + vector)
tenant_context	struct	tenant_id, industry, truck_profile, growth rate, square footage, expansion openness, retention priority, churn risk
facility_context	struct	Building characteristics, capacity, energy profile
portfolio_context	struct	Network-level dynamics, capacity distribution
telemetry_stream	list	Ordered telemetry readings (dock utilization, queue length, power load, service commitment risk, etc.) with per-step event labels
detection	struct	analytic_family, primary_risk_class, rule_logic, baseline_deviation, anomaly_score, confidence_band, signal_conflicts[]
forecast	struct	Forward-looking predictions (demand shift, occupancy, pricing)
impact	struct	Economic consequences (revenue delta, NOI, capex, churn risk)
response	struct	recommended_actions[], primary_action, primary_action_score, primary_action_reason, alternative_actions[], decision_owner, execution_window_days, playbook_id, capex_gate_required, stakeholders[], expected_operational_outcome, recommended_tradeoff, execution_risk_band
simulation	struct	synthetic, engine, causal_coherence, friction_profile, ground_truth_label, intended_use[]

See SCHEMA.md for the full nested field breakdown.

Why this dataset is useful

Most public operations / logistics datasets are either flat sensor-stream snapshots or narrow single-scenario studies. Industrial real estate telemetry (dock utilization, grid headroom, tenant churn signals, decision lineage) is effectively never published. This pack is shaped around what operational-intelligence and decision-policy teams actually need:

• Full scenario lifecycles rather than isolated sensor frames
• Balanced severity and status distributions across decision-pending, triaged, and confirmed cases
• Coupled detection → forecast → impact → response layers per record (not just telemetry)
• Economic impact fields (revenue, NOI, capex, churn) for decision-aware modeling
• Decision-lineage metadata for policy-interpretability research
• Cross-scenario coverage (port, power, labor, cold chain, tenant, last-mile, site selection)
• Compact parquet (~3.8 MB) that fits inside CI and notebooks, with a fuller JSONL for LLM training

Typical use cases

• Operational-anomaly detection models
• Capacity-planning and forecasting pipelines
• Decision-support and recommendation engines
• RL / policy optimization for industrial operations
• Tenant-churn and retention modeling
• Site-selection and network-optimization research
• LLM fine-tuning on operational narratives and decision rationale
• Scenario-mining for logistics debugging
• Executive-dashboard and BI template development

Quick start

import pandas as pd
import pyarrow.parquet as pq

df = pq.read_table("forge_industrial_sample.parquet").to_pandas()

# Severity distribution
print(df["event"].apply(lambda e: e["severity"]).value_counts())

# Scenario × primary action cross-tab
df["scenario"] = df["event"].apply(lambda e: e["scenario"])
df["primary_action"] = df["response"].apply(lambda r: r["primary_action"])
print(pd.crosstab(df["scenario"], df["primary_action"]))

# Average impact.revenue_delta_usd by market
df["market"] = df["organization"].apply(lambda o: o["market"])
df["rev_delta"] = df["impact"].apply(lambda i: i.get("revenue_delta_usd"))
print(df.groupby("market")["rev_delta"].mean().round(0))

Streaming form:

import json

with open("forge_industrial_sample.jsonl") as f:
    for line in f:
        lifecycle = json.loads(line)
        # one operational scenario lifecycle per line

Responsible use

This dataset is intended for research, model development, and decision-policy simulation around industrial real estate, logistics, and operations. It contains synthesized telemetry, decision lineages, and economic-impact rollups — it does not contain real facility telemetry, real tenant identities, real portfolio NOI, or identifiable stakeholder data. Models trained on this data should be validated against real operational data under appropriate organizational review before being used for live recommendations or capex decisions.

License

Released under CC BY 4.0. Use freely for research, operations-tool prototyping, education, and commercial development with attribution.

Get the full pack

This Hugging Face repo is a 10K-lifecycle sample. The production pack scales to 5M+ lifecycles with expanded market and submarket coverage, richer per-step telemetry (ms-level sensor traces where applicable), additional scenario classes (ESG signals, geopolitical supply disruption, labor automation events, bonded warehouse flows), multi-year longitudinal traces per tenant, parquet + JSONL + BI-tool-aligned delivery, and buyer-specific variants.

Self-serve (Stripe checkout):

• Sample Scale tier — $5,000 — ~25K records, one subject, 72-hour delivery.

Full pack + enterprise scope:

• www.solsticestudio.ai/datasets — per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.

Procurement catalog:

• SolsticeAI Data Storefront — available via Datarade / Monda.

Citation

@dataset{solstice_forge_industrial_pack_2026,
  title        = {Forge Industrial Intelligence Pack (Sample)},
  author       = {SolsticeAI},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/solsticestudioai/forge-industrial-pack}
}