Datasets:
license: cc-by-nc-4.0
pretty_name: PrimaForja Supply — One-Month Sample
language:
- en
tags:
- synthetic
- business
- supply-chain
- distribution
- finance
- inventory
- logistics
- sql
- tabular
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
size_categories:
- 10M<n<100M
extra_gated_heading: Request access to the PrimaForja Supply sample
extra_gated_prompt: >-
This sample is published by PrimaForja LLC under the Creative Commons
Attribution-NonCommercial 4.0 license (CC BY-NC 4.0). By requesting access you
agree that: (1) you will use the data for non-commercial purposes only —
commercial licenses for the full dataset are available at
https://www.primaforja.com; (2) you will credit "PrimaForja LLC" in any
publication, model, or derivative that uses it; (3) no trademark license is
granted — the PrimaForja name and marks may be used only for that required
attribution; and (4) the data is entirely synthetic and provided as-is,
without warranty of any kind. Access is granted automatically once you accept.
extra_gated_fields:
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Intended use:
type: select
options:
- BI / dashboard testing
- ML research or training
- Education
- Evaluating a commercial license
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configs:
- config_name: ap_ledger
data_files: ap_ledger.parquet
- config_name: ar_ledger
data_files: ar_ledger.parquet
- config_name: backorder_resolution_history
data_files: backorder_resolution_history.parquet
- config_name: backorders
data_files: backorders.parquet
- config_name: capex_schedule
data_files: capex_schedule.parquet
- config_name: carrier_performance_history
data_files: carrier_performance_history.parquet
- config_name: carriers
data_files: carriers.parquet
- config_name: corporate_overhead_monthly
data_files: corporate_overhead_monthly.parquet
- config_name: corporate_payroll
data_files: corporate_payroll.parquet
- config_name: customer_contacts
data_files: customer_contacts.parquet
- config_name: customer_item_preferences
data_files: customer_item_preferences.parquet
- config_name: customer_ltv_history
data_files: customer_ltv_history.parquet
- config_name: customers
data_files: customers.parquet
- config_name: dc_facility_costs
data_files: dc_facility_costs.parquet
- config_name: dc_item_slotting
data_files: dc_item_slotting.parquet
- config_name: dc_throughput_history
data_files: dc_throughput_history.parquet
- config_name: demand_signal_history
data_files: demand_signal_history.parquet
- config_name: distribution_centers
data_files: distribution_centers.parquet
- config_name: driver_costs
data_files: driver_costs.parquet
- config_name: equipment_maintenance_events
data_files: equipment_maintenance_events.parquet
- config_name: fleet_assets
data_files: fleet_assets.parquet
- config_name: fleet_maintenance_events
data_files: fleet_maintenance_events.parquet
- config_name: forecast_accuracy_history
data_files: forecast_accuracy_history.parquet
- config_name: fuel_costs
data_files: fuel_costs.parquet
- config_name: internal_consumption
data_files: internal_consumption.parquet
- config_name: inventory
data_files: inventory.parquet
- config_name: inventory_adjustments
data_files: inventory_adjustments.parquet
- config_name: inventory_monthly_snapshot
data_files: inventory_monthly_snapshot.parquet
- config_name: invoice_lines
data_files: invoice_lines.parquet
- config_name: invoices
data_files: invoices.parquet
- config_name: item_costs
data_files: item_costs.parquet
- config_name: item_forecast
data_files: item_forecast.parquet
- config_name: item_velocity_history
data_files: item_velocity_history.parquet
- config_name: items
data_files: items.parquet
- config_name: kpi_budget
data_files: kpi_budget.parquet
- config_name: kpi_projections
data_files: kpi_projections.parquet
- config_name: labor_costs
data_files: labor_costs.parquet
- config_name: manufacturers
data_files: manufacturers.parquet
- config_name: margin_waterfall
data_files: margin_waterfall.parquet
- config_name: marketing_campaigns
data_files: marketing_campaigns.parquet
- config_name: order_lines
data_files: order_lines.parquet
- config_name: orders
data_files: orders.parquet
- config_name: price_elasticity_history
data_files: price_elasticity_history.parquet
- config_name: price_history
data_files: price_history.parquet
- config_name: promo_orders
data_files: promo_orders.parquet
- config_name: promotional_lift_history
data_files: promotional_lift_history.parquet
- config_name: promotions
data_files: promotions.parquet
- config_name: purchase_orders
data_files: purchase_orders.parquet
- config_name: replenishment_signal_history
data_files: replenishment_signal_history.parquet
- config_name: returns
data_files: returns.parquet
- config_name: safety_incidents
data_files: safety_incidents.parquet
- config_name: sales_rep_compensation
data_files: sales_rep_compensation.parquet
- config_name: sales_rep_performance_history
data_files: sales_rep_performance_history.parquet
- config_name: sales_reps
data_files: sales_reps.parquet
- config_name: shipment_lines
data_files: shipment_lines.parquet
- config_name: shipments
data_files: shipments.parquet
- config_name: tracking_events
data_files: tracking_events.parquet
- config_name: vendor_item_details
data_files: vendor_item_details.parquet
- config_name: vendor_performance
data_files: vendor_performance.parquet
- config_name: vendor_scorecards
data_files: vendor_scorecards.parquet
- config_name: vendors
data_files: vendors.parquet
- config_name: workforce_headcount
data_files: workforce_headcount.parquet
PrimaForja Supply — One-Month Sample
A fully synthetic, fully reconciled month of a mid-market industrial distributor: ~40 million rows across 62 interrelated tables, generated at full production scale (27 distribution centers, ~6,800 customers, ~37,000 SKUs, 391 vendors, ~1.7 million orders).
What this is
A free sample of a production-scale synthetic dataset that models a US industrial/office-supply distributor doing roughly $12 million per day in revenue. Those numbers are the generator's default configuration, not a limit — the business scales up or down (rows, revenue, date range) with every table still agreeing with every other table. In its default shape the full product is two years of history (~800 million rows); this sample is one complete calendar month (June 2025), generated by the same pipeline at the same scale — every foreign key resolves and every financial number reconciles, at a size you can explore on a laptop.
Not one row of this is real data. No PII, no scrubbed production data, no licensing entanglements. It is generated by a deterministic, config-driven pipeline (3,300+ tunable parameters) in which the business logic interlocks: facility size drives headcount, headcount drives payroll, payroll rolls into EBITDA; discounts, promotions, freight, and cost flow into a pocket-margin waterfall that reconciles to the dollar.
The month has honest edges, the way a real month does: orders sit strictly inside June, but shipments and invoices for late-June orders spill into July, and June carries backorders and receivables opened earlier.
Using the sample
Each table is one Parquet file — use the dataset viewer above (pick a table from the config dropdown) or load any table directly:
import pandas as pd
orders = pd.read_parquet("hf://datasets/PrimaForja/supply-one-month-sample/orders.parquet")
DuckDB makes the joins effortless:
import duckdb
con = duckdb.connect()
df = con.sql("""
SELECT c.segment, COUNT(DISTINCT o.order_id) AS orders, SUM(ol.extended_price) AS revenue
FROM 'hf://datasets/PrimaForja/supply-one-month-sample/order_lines.parquet' ol
JOIN 'hf://datasets/PrimaForja/supply-one-month-sample/orders.parquet' o USING (order_id)
JOIN 'hf://datasets/PrimaForja/supply-one-month-sample/customers.parquet' c USING (customer_id)
GROUP BY 1 ORDER BY 3 DESC
""").df()
data_dictionary.md / data_dictionary.json document every table and
column; manifest.json records the generation run (seed 20250 —
deterministic: the same seed regenerates this sample byte-for-byte).
What's inside
Orders, order lines, shipments, shipment lines, tracking events, invoices, AR/AP ledgers, returns, backorders, purchase orders, inventory (positions, monthly snapshots, adjustments, slotting), item catalog with costs and price history, vendors with performance and scorecards, carriers, promotions and campaigns, labor/facility/fleet costs, a margin waterfall, and a C-level analytics layer (demand signals, forecast accuracy, customer LTV, price elasticity, DC throughput, rep performance).
Suggested ML tasks: demand forecasting, churn/LTV modeling, price elasticity estimation, backorder risk classification, carrier OTD prediction, anomaly detection on inventory adjustments.
The full product
The complete dataset — two years, ~800 million rows, plus a licensed library of 77 analytics views that power the live executive dashboards — is at https://www.primaforja.com, where you can explore the dashboards built on it and request demo access.
License
CC BY-NC 4.0 — © PrimaForja LLC. Free for research, learning, and evaluation with attribution; commercial use (including resale or bundling into paid products) requires a license — contact sales@primaforja.com.