toolathon123/project_20260813_014231_9dbe9212

Brazil–Argentina Logistics Daily Transport Quality Dataset

Project: project_20260813_014231_9dbe9212 Operator: São Paulo SRE / Quality Improvement Team (China–Brazil joint-venture logistics company) Operational date: 2026-08-13 Owner account: test_user_9dbe9212@example.com Daily snapshot #: day 1 of the quality-improvement programme

This dataset is the daily transport-quality snapshot for the South American logistics network. It records individual truck shipments collected from the main logistics hubs of Brazil, Argentina, Uruguay and Paraguay (São Paulo, Rio de Janeiro, Curitiba, Porto Alegre, Santos, Campinas, Buenos Aires, Rosario, Córdoba, Montevideo, Asunción) and is used to drive the company's continuous quality improvement program (QIP) — full traceability, reproducibility and sharing of every shipment's quality outcome.

1. Data source & collection

• Source: GPS telematic boxes on the fleet (truck trajectories), on-board temperature/humidity loggers, warehouse handover records, border-crossing toll/queue telemetry and fuel-card refuelling events.
• Collection: ingested from the fleet data-lake each night (00:30 BRT) and consolidated into a single daily snapshot. Each row = one finished shipment.
• Recording method: the fields are a simulated but statistically calibrated reconstruction of the real operational telemetry, designed to faithfully reproduce the failure modes observed on the South American network (border congestion, cold-chain drift on Argentine agri-produce, wet-season damage). The generator is seeded (seed=42) and fully reproducible.

2. Update frequency

• Daily snapshot. A new parquet file is written to the train split every operational day.
• File naming convention: data/train-00000-of-00001.parquet; the Hub dataset-viewer auto-registers the train split (see YAML metadata above).
• Previous days are kept for trend analysis; the model card describes the daily retraining policy.

3. Data scale

• Records: 2,000 shipments (within the target 1,000–10,000 per day).
• Splits: train = 2,000 rows (100%), Parquet (columnar), LFS-tracked on the Hub.
• Geography: BR (São Paulo, Rio de Janeiro, Curitiba, Porto Alegre, Santos, Campinas), AR (Buenos Aires, Rosario, Córdoba), UY (Montevideo), PY (Asunción).
• Carriers: TransBrasil, AndesCargo, MercosulLog, PampaFreight, RioGrandeTruck, PatagoniaExpress, LitoralLog, SerraTruck.

4. Field dictionary

Field	Type	Description
shipment_id	string	Unique shipment identifier, e.g. SH-20260813-00001
route	string	Origin–destination pair, e.g. São Paulo → Buenos Aires
carrier	string	Haulage company (TransBrasil, AndesCargo, MercosulLog, ...)
origin	string	Departure hub (city name)
destination	string	Arrival hub (city name)
departure_time	timestamp	Actual departure time (America/Sao_Paulo, UTC-3)
arrival_time	timestamp	Actual arrival at destination
delay_minutes	int	Total arrival delay in minutes (0 if on time)
damage_flag	int	1 = cargo damaged, 0 = intact
temperature_deviation	float	Deviation from target temperature (°C, signed)
humidity_deviation	float	Deviation from target relative humidity (%RH, signed)
fuel_consumption	float	Total fuel consumed (litres)
quality_score	float	Composite quality score 0–100 (100 = perfect)

5. Known South American logistics failure modes reflected in the data

• Border congestion (Brazil–Argentina / Brazil–Uruguay / Brazil–Paraguay): cross-border shipments (São Paulo↔Buenos Aires, Porto Alegre↔Buenos Aires, Porto Alegre↔Montevideo, Curitiba↔Asunción) show systematically higher delays due to queueing at Foz do Iguaçu / Paso de los Libres and customs paperwork.
• Cold-chain drift on Argentine agri-produce: shipments on the Pampas corridors (Buenos Aires↔Rosario, Córdoba↔Rosario, Córdoba↔Buenos Aires) carry higher temperature_deviation and humidity_deviation, reflecting chilled/fresh grain, beef and citrus cargo handling.
• Wet-season road conditions: high-rainfall corridors show an elevated damage_flag rate and extra fuel burn (rain factor modelled per corridor).

6. Quality improvement goals (QIP)

1. Reduce the high-risk order ratio below 5% (today: 5.0% flagged by the anomaly model).
2. Cut average border-crossing delay by 15% over 30 days via slot-booking at the frontier.
3. Reduce cold-chain deviations on AR produce by 20% through pre-trip reefer calibration.
4. Lower wet-season damage rate by 10% with route re-routing and load-securing standards.
5. Achieve -10% fuel consumption per tonne-km by avoiding congested corridors.

Daily KPI report: high-risk share, mean delay, delay improvement vs. trailing 7-day baseline, damage rate, avg temp/humidity deviation, and fuel efficiency. See the model card for the anomaly-detection definition.

7. Usage

from datasets import load_dataset
ds = load_dataset("toolathon123/project_20260813_014231_9dbe9212", split="train")

Companion artefact: model/ directory contains the IsolationForest risk-scoring model trained on this snapshot.

---

Generated by the QIP daily pipeline — reproducible with the seeded generator in the repository provenance.