Shipments with real lead times.

Generate suppliers, purchase orders, shipments, containers and warehouse deliveries with realistic routes, lead times, delays and on-time performance.

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The example

Inbound ocean freight to US distribution centres

What to look for in the screenshot above. These are the details that usually break in hand-made or random test data.

Each purchase order ships from a real-sounding supplier through a real port code (UN/LOCODE).
Lead times fit the lane: about 3 weeks from Rotterdam, 6–7 weeks from Asia, days by rail from Mexico.
on_time is true only when delivered_at is on or before the ETA, on every row.
Rail shipments from Mexico have no ocean container number, as in real data.

Built for Supply Chain & Logistics teams

Realistic lanes

Ports, carriers and lead times that fit each origin and destination.

Delays that matter

Late deliveries cluster on long lanes, so on-time KPIs look real.

Connected documents

Suppliers, POs, PO lines, shipments, containers and inventory all linked.

Valid-format identifiers

Container numbers, port codes and PO numbers in the right formats.

How it works

From idea to dataset in three steps

Describe or upload

Describe your data in plain English, or upload your SQL schema for PostgreSQL, MySQL / MariaDB, SQL Server and SQLite.

Review the plan

Check the tables, columns and rules the AI proposes, and adjust anything you like.

Generate and download

Choose how many rows you need and download a ready-to-run SQL script, CSV, Excel (one sheet per table) or JSON.

Questions

Can I model warehouses and inventory?

Yes. Add warehouse, bin and inventory tables that stock levels and shipments update.

Can I simulate disruptions?

Yes. Add a rule for a delayed lane or month and ETAs and delays follow it.

Can it generate GPS for trucks too?

Yes. See geospatial tracking: pings follow continuous paths per vehicle.