| cff-version: 1.2.0 | |
| message: "If you use SCM-SQL in your research, please cite it as below." | |
| title: "SCM-SQL: A Supply-Chain Natural-Language-to-SQL Evaluation Set" | |
| authors: | |
| - family-names: Kawarase | |
| given-names: Aniruddha Prakash | |
| orcid: null | |
| version: "1.0.0" | |
| date-released: "2026-07-30" | |
| url: "https://huggingface.co/datasets/AniruddhaAI/scm-sql" | |
| repository-code: "https://github.com/AniruddhaPKawarase/scm-sql-dataset" | |
| license: CC-BY-SA-4.0 | |
| type: dataset | |
| keywords: | |
| - text-to-sql | |
| - nl-to-sql | |
| - supply-chain | |
| - domain-specific-benchmark | |
| - multi-turn-dialogue | |
| - erp | |
| - odoo | |
| abstract: > | |
| SCM-SQL is a 500-pair evaluation set for natural-language-to-SQL systems, | |
| authored against the live Odoo 17 supply-chain schema. It spans six | |
| explicit complexity levels including 50 multi-turn refinement | |
| dialogues, four supply-chain sub-domain tags (demand, finance, | |
| inventory, logistics), and 556 turn-level trials in total. Every gold | |
| SQL is execute-verified against a stock Odoo 17 demo database. Built | |
| for the dissertation "Domain-Aware Multi-Agent NL-to-SQL for | |
| Enterprise Supply Chain Intelligence" (BITS Pilani WILP, 2026) and | |
| released so other researchers can benchmark domain-aware text-to-SQL | |
| systems on realistic enterprise-ERP queries. | |