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Generate Logistics and Supply Chain Synthetic Data in Python

Logistics data connects physical assets — vehicles, drivers, and routes — with time-sensitive operations like shipments and deliveries. Building a fleet management system, training a route optimization model, or developing a supply chain analytics dashboard requires data where delivered_at is always after shipped_at, driver ratings are realistic, and route distances follow the right distribution. Misata generates all of it in one call.

The logistics domain generates four interconnected tables: drivers assigned to vehicles, shipments assigned to routes and drivers, all with FK integrity and realistic temporal constraints. On-time delivery rate is ~88% by default and configurable from your story.

import misata

tables = misata.generate("A logistics company with 200 drivers and 50k shipments", rows=1000, seed=42)
print(list(tables.keys()))   # ['drivers', 'vehicles', 'routes', 'shipments']
print(tables["shipments"][["status", "weight_kg"]].describe())

What Misata generates

Four tables: drivers → vehicles, routes, shipments (which reference both routes and drivers). Every shipment has a valid driver and route reference; temporal columns are always coherent.

Tables and columns

Table Key columns
drivers driver_id, name, license_type, rating, hire_date, vehicle_id
vehicles vehicle_id, type, plate, capacity_kg, year, status
routes route_id, origin, destination, distance_km, duration_hours, cost
shipments shipment_id, route_id, driver_id, weight_kg, status, shipped_at, delivered_at

Realistic distributions

  • On-time rate is ~88% — configurable via story ("95% on-time rate", "frequent delays")
  • delivered_at is always after shipped_at — enforced, not probabilistic
  • Route distances lognormal — right mix of short urban hauls and long-distance routes
  • Driver ratings beta-distributed in the 4.0–5.0 range — reflecting how platform ratings cluster high
  • Vehicle capacity matches vehicle type — light vans have lower capacity than heavy trucks

Quick start

import misata
import pandas as pd

tables = misata.generate(
    "A logistics company with 300 drivers, mixed fleet, 10k shipments",
    rows=1000,
    seed=42,
)

# Verify temporal coherence
shipments = tables["shipments"].copy()
shipped = pd.to_datetime(shipments["shipped_at"])
delivered = pd.to_datetime(shipments["delivered_at"])
completed = shipments["status"] == "delivered"
assert (delivered[completed] >= shipped[completed]).all()

# On-time performance
on_time = shipments[shipments["status"] == "delivered"].shape[0]
total = shipments.shape[0]
print(f"On-time rate: {on_time/total:.1%}")

Common use cases

  • Route optimization model training — generate training data with distance, duration, cost, and driver ratings for last-mile delivery optimization
  • Fleet management dashboard development — build vehicle utilization, driver performance, and route efficiency dashboards before your telematics data is connected
  • Supply chain simulation — test warehouse management logic against thousands of inbound and outbound shipments with realistic weight and timing distributions
  • Delivery SLA monitoring — validate your alerting and escalation logic against shipments in all status states (in transit, delayed, delivered, failed)
  • Driver scoring algorithm testing — generate driver histories with varied ratings and delivery completion rates
  • Logistics ERP integration testing — seed test databases with full driver-vehicle-route-shipment hierarchies for API validation

Advanced: delay scenario modeling

tables = misata.generate(
    "Logistics company with 500 drivers — delivery delays in December due to holiday volume surge, "
    "on-time rate drops to 70% in December, back to 90% in January",
    rows=2000,
    seed=42,
)

Advanced: locale-aware generation

# German logistics — German city names, EU routes, EUR costs
tables = misata.generate("German parcel delivery company with 200 drivers, EU routes", rows=1000)

# Indian logistics — Indian cities, INR pricing, mixed vehicle types
tables = misata.generate("Indian last-mile delivery company with 300 drivers", rows=1500)

Advanced: quality-guaranteed generation

tables = misata.generate(
    "Logistics company with 500 drivers",
    min_quality_score=85,
    smart_correlations=True,  # auto-correlates distance↔duration, weight↔cost
    rows=2000,
    seed=42,
)