SCM-SQL — a supply-chain natural-language-to-SQL evaluation set

500 (question, gold SQL) pairs authored against the live Odoo 17 supply-chain schema, spanning 6 explicit complexity levels including multi-turn dialogues.

Built for the dissertation Domain-Aware Multi-Agent Natural-Language-to-SQL for Enterprise Supply Chain Intelligence by Aniruddha Prakash Kawarase (BITS Pilani WILP, 2026). Released as a public evaluation benchmark so other researchers can compare domain-aware text-to-SQL systems on realistic enterprise-ERP queries.

Why this dataset exists

Public text-to-SQL benchmarks like BIRD and Spider are open-domain: they test whether a model generalises across many small schemas (typically 3-6 tables per database). Enterprise supply-chain deployments look different: one deep schema, many tables, dialect quirks, and analysts who iterate on their questions over multiple turns. SCM-SQL is designed to stress-test that enterprise setting.

What's in the box

• 500 (question, gold SQL) pairs — data/pilot_500.yaml
• 6 explicit complexity levels — L1 (single-table filter) → L6 (multi-turn refinement)
• 4 supply-chain sub-domain tags — demand, finance, inventory, logistics
• Every gold SQL is execute-verified against a stock Odoo 17 demo database
• Multi-turn dialogues — 50 L6 pairs consist of 2-3 turns each where turn N refines the SQL of turn N-1

Distribution

Level	Style	n	Multi-turn?
L1	Single-table filter or aggregate	100	No
L2	Two-table JOIN + GROUP BY	100	No
L3	Nested subquery / semi-join / 3-table join	130	No
L4	Window function	60	No
L5	CTE + rollup / GROUPING SETS	60	No
L6	Multi-turn conversational refinement	50	Yes (2-3 turns each)
Total		500	556 turn-level trials

Per-domain (a pair can carry multiple domain tags):

Domain	Approx. count
demand	~ 145
finance	~ 140
inventory	~ 130
logistics	~ 130

Target database

Every gold SQL is executable on stock Odoo 17 (image odoo:17.0 from Docker Hub, unmodified). The Odoo demo dataset ships with approximately 42 000 rows across 498 tables and is fetched by docker compose up on the reference implementation.

No modification is made to the Odoo demo data itself. SCM-SQL is a new artefact authored on top of the Odoo schema — it does not fork, edit, or redistribute the underlying Odoo data.

Loading

With datasets:

from datasets import load_dataset
ds = load_dataset("AniruddhaAI/scm-sql", split="test")
print(ds[0])

Directly from YAML:

import yaml
with open("data/pilot_500.yaml") as f:
    pairs = yaml.safe_load(f)["pairs"]
print(len(pairs), "pairs")
print(pairs[0])

See examples/ for full loader and evaluation-harness snippets.

Schema

Every pair is a YAML document with the following fields:

Field	Type	Description
id	string	Unique identifier, e.g. L1-001, L6-023
level	int (1-6)	Complexity tier
domains	list[string]	One or more of demand, finance, inventory, logistics
nl	string	(L1-L5 only) The natural-language question
gold_sql	string	(L1-L5 only) The verified gold PostgreSQL statement
turns	list	(L6 only) Sequence of {nl, gold_sql} turns; turn N refines turn N-1
tags	list[string]	Intent tags (aggregate, filter, join, window, cte, multi_turn, ...)

Full schema documentation with examples: docs/SCHEMA.md.

Evaluation protocol

SCM-SQL follows the Execution Accuracy (EX) protocol standard in the text-to-SQL literature: a predicted SQL is counted correct if and only if executing it against the target Odoo database produces a row-equivalent result set to the gold SQL (order-agnostic, column-name-agnostic row multiset comparison).

An additional metric, Soft-EX, absorbs benign alias renames: a prediction returning revenue where the gold returns total_revenue still counts as correct as long as the row-values match.

See examples/evaluate_predictions.py for a reference implementation.

Modifications to source datasets

None. SCM-SQL is a new artefact, not a fork of any existing dataset. The Odoo 17 demo database itself is unchanged — no INSERT, UPDATE, or DELETE statement is ever issued against it. The reference implementation runs against the stock odoo:17.0 Docker image, verifiable by pulling with digest pinning.

Companion project

The reference multi-agent NL-to-SQL implementation that this dataset was built to evaluate lives at:

https://github.com/AniruddhaPKawarase/scm-nl2sql

That repository contains a LangGraph orchestrator (Router · Specialist · Composer · Compliance · Executor), a Next.js UI with live evaluation-metric chips, and the full evaluation harness (scripts/run_evaluation.py) that computes EX / Soft-EX / VES on this dataset.

License

CC BY-SA 4.0 — attribution + share-alike. This matches the licences under which BIRD and Spider are released, so mixed benchmarking is licence-consistent.

Citation

If you use SCM-SQL in your research, please cite:

@misc{kawarase2026scmsql,
  title  = {SCM-SQL: A Supply-Chain Natural-Language-to-SQL Evaluation Set},
  author = {Kawarase, Aniruddha Prakash},
  year   = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/AniruddhaAI/scm-sql}},
  note   = {Companion repository: https://github.com/AniruddhaPKawarase/scm-nl2sql}
}

See CITATION.cff for the machine-readable citation file.

Contact

Aniruddha Prakash Kawarase · BITS Pilani WILP · aniruddhakawarase@gmail.com