ERP Inventory Movement and State Invariant Validation Dataset
- Records
- 4250
- Size
- 0.3GB
- Format
- JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-04
This dataset uses structured JSON to describe inventory receipts, issues, transfers, batch or location changes, and related inventory snapshots, covering common ERP inventory movement scenarios. Each example can be used to check constraints such as quantity conservation, source and destination location consistency, batch traceability, and stock availability, with machine-readable violations as output. It provides explicit validation signals for verifiable-reward reinforcement learning and supports inventory constraint validation, model training, and evaluation.
Overview
Dataset Insights
Preview
Sample Examples
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| violations | array | Machine-readable violations found when validating the movement and its resulting inventory state. |
| movement_input | object | The original movement record and related inventory snapshots provided for validation. |
| validation_status | string | The overall result derived from the inventory invariant checks. |
| availability_check | object | A computed check that the movement does not consume more available stock than is held at the source location. |
| batch_traceability_valid | boolean | Indicates whether batch identifiers are tracked consistently between the movement and its inventory snapshots. |
| location_transition_valid | boolean | Indicates whether the recorded source and destination locations are consistent with the movement and inventory snapshots. |
| quantity_conservation_check | object | A computed comparison of the expected net quantity change with the actual change shown in the inventory snapshots. |
Trust
Compliance Statement
Authorization Type
Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use
Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization
No PII, no real company names, simulated scenarios follow industry standards
Compliance System
Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs
FAQ
Frequently Asked Questions
- What inventory scenarios does the dataset cover?
- The JSON examples describe inventory receipts, issues, transfers, batch or location changes, and related inventory snapshots for validating common ERP inventory movement constraints.
- Which rules can the data validate?
- It supports checks for quantity conservation, source and destination location consistency, continuous batch identification, and whether a movement exceeds available stock at the source location. Results include an overall status and machine-readable violations.
- Which training and evaluation tasks is it suitable for?
- It is suitable for inventory constraint validation, structured business reasoning, and violation detection. It can also provide checkable outcome signals for verifiable-reward reinforcement learning.
- How can I obtain the data, and what are the licensing terms?
- Visit the dataset product page for access options, file specifications, and licensing information. Permitted use depends on the published license; confirm the applicable terms before commercial deployment or redistribution.
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Cite this Work
@dataset{Mobiusi2026,
title={ERP Inventory Movement and State Invariant Validation Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/010a5416212c840d5ed0a2ce7c5dd142?dataset_scene_cate_type=5},
urldate={2026-10-22},
keywords={ERP inventory dataset,inventory movement validation,inventory constraint checking,quantity conservation validation,batch traceability data,location transition validation,RLVR training data,inventory state validation},
version={1.0}
}Using this in research? Please cite us.