erp-datagen

Generate realistic synthetic procurement data for SAP ECC, Microsoft D365, and JDE — for testing, AI training, and demos.

The problem

Developers and data engineers building on ERP systems constantly need realistic test data. Real production data can't be shared. Existing tools don't understand procurement domain structure — duplicate vendor names, multi-currency POs, three-way match scenarios, and the messy edge cases that actually matter.

This tool does.

Supported ERP systems

| ERP | Entities supported | |------------|-------------------------------------------------------------------------------------------| | SAP ECC | LFA1, EKKO, EKPO, MKPF, MSEG, RBKP, RSEG | | D365 F&O | VendTable, PurchTable, PurchLine, VendPackingSlipJour, VendInvoiceJour, VendInvoiceTrans | | JDE E1 | F0101, F4301, F4311, F43121, F0411 |

All three ERPs support a full-p2p scenario that generates all tables linked by real keys in one command.

Requirements

• Node.js >= 18

Install

npx erp-datagen --help
# or install globally
npm install -g erp-datagen

Or clone and run locally:

git clone https://github.com/kundanshar-cell/erp-datagen.git
cd erp-datagen
npm install

Usage

Generate a single entity

npx erp-datagen generate --erp=<erp> --entity=<entity> [options]

| Option | Description | Default | |---|---|---| | --erp | ERP system: sap-ecc, jde, d365 | required | | --entity | Entity name (see table below) | required | | --rows | Number of rows to generate | 100 | | --output | Output format: csv, json | csv | | --file | Write to file instead of stdout | stdout | | --missing-rate | Rate of missing optional fields (0–1) | 0 |

Examples:

# SAP ECC — 500 vendors as CSV
npx erp-datagen generate --erp=sap-ecc --entity=vendors --rows=500 --output=csv

# SAP ECC — 200 PO headers as JSON, written to file
npx erp-datagen generate --erp=sap-ecc --entity=po-headers --rows=200 --output=json --file=./ekko.json

# JDE — 100 PO lines with 20% missing fields (simulates messy data)
npx erp-datagen generate --erp=jde --entity=po-lines --rows=100 --missing-rate=0.2

# D365 — 300 invoice headers as CSV
npx erp-datagen generate --erp=d365 --entity=invoice-headers --rows=300 --output=csv

Supported entities per ERP

| ERP | --entity value | Table | |-----|-----------------|-------| | sap-ecc | vendors | LFA1 | | sap-ecc | po-headers | EKKO | | sap-ecc | po-lines | EKPO | | sap-ecc | gr-headers | MKPF | | sap-ecc | gr-lines | MSEG | | sap-ecc | invoice-headers | RBKP | | sap-ecc | invoice-lines | RSEG | | jde | vendors | F0101 | | jde | po-headers | F4301 | | jde | po-lines | F4311 | | jde | gr-lines | F43121 | | jde | invoices | F0411 | | d365 | vendors | VendTable | | d365 | po-headers | PurchTable | | d365 | po-lines | PurchLine | | d365 | gr-headers | VendPackingSlipJour | | d365 | invoice-headers | VendInvoiceJour | | d365 | invoice-lines | VendInvoiceTrans |

Generate a full linked dataset (full-p2p scenario)

Generates all tables for an ERP linked by real document keys — vendors → PO headers → PO lines → goods receipt → invoices. One file per table, written to an output directory.

npx erp-datagen scenario --erp=<erp> --name=full-p2p [options]

| Option | Description | Default | |---|---|---| | --erp | ERP system: sap-ecc, jde, d365 | required | | --name | Scenario name: full-p2p | required | | --rows | Approximate number of PO lines | 100 | | --output | Output format: csv, json | csv | | --output-dir | Directory to write files | ./output | | --missing-rate | Rate of missing optional fields (0–1) | 0 |

Examples:

# SAP ECC — full P2P dataset, 1000 PO lines, CSV
npx erp-datagen scenario --erp=sap-ecc --name=full-p2p --rows=1000 --output-dir=./output

# JDE — full P2P as JSON
npx erp-datagen scenario --erp=jde --name=full-p2p --rows=500 --output=json --output-dir=./output/jde

# D365 — messy data with 30% missing fields
npx erp-datagen scenario --erp=d365 --name=full-p2p --rows=1000 --missing-rate=0.3 --output-dir=./output/d365

Sample output (SAP ECC, 1000 rows):

LFA1_vendors.csv        (100 rows)
EKKO_po_headers.csv     (200 rows)
EKPO_po_lines.csv       (622 rows)
MKPF_gr_headers.csv     (207 rows)
MSEG_gr_lines.csv       (522 rows)
RBKP_invoice_headers.csv (155 rows)
RSEG_invoice_lines.csv  (567 rows)

Edge cases included

• Duplicate vendor names with different formats (ACME Ltd, Acme Limited, ACME LIMITED)
• Configurable missing fields (--missing-rate)
• Multi-currency (GBP, USD, EUR, INR, SGD, JPY)
• Multi-language vendor names
• Realistic document numbering per ERP convention
• Three-way match scenarios (PO → GR → Invoice) with referential integrity
• Blocked/deleted vendors, cancelled PO lines
• Partial deliveries (GR quantity < PO quantity)
• Invoice price variance vs PO price
• Reversed GR documents and credit memos
• Dispute flags on invoices

Output formats

--output=csv --output=json

sql and parquet formats are on the roadmap.

Roadmap

• [x] Publish to npm
• [ ] SQL and Parquet output formats
• [ ] Oracle Fusion Procurement
• [ ] Coupa supplier/PO entities
• [ ] Web UI for no-code data generation
• [ ] Configurable data quality profiles (clean / messy / broken)

Who is this for

• Developers building ERP integrations who need test data
• Data engineers building procurement analytics pipelines
• AI/ML teams training models on procurement data
• Consultants demoing ERP tools without production data

Contributing

PRs welcome — especially for Oracle Fusion, Coupa, and Ariba schemas. See CONTRIBUTING.md for guidelines.

Author

Built by Kundan Sharma — IT & Digital Solution Architect specialising in procurement data transformation and agentic AI in enterprise supply chains.

15+ years designing and delivering digital transformation programmes across enterprise. GitHub

If this saved you time, leave a star.

License

MIT — see LICENSE for details.