Supply Chain Synthetic Dataset for Simulation and Modeling

Synthetic data generated to model supply chain operations without using real proprietary information. The dataset is hosted on Kaggle, a popular platform for data science competitions and projects. Its specific origin, size, and creation method are not detailed in the available metadata.

Use Cases

• Train a model for demand forecasting using synthetic historical data (inferred from domain, verify after download)
• Simulate supply chain disruptions and test resilience strategies (inferred from domain, verify after download)
• Benchmark optimization algorithms for inventory or routing problems (inferred from domain, verify after download)

Strengths

• Published on Kaggle, a major platform for data sharing and community validation.
• Synthetic nature avoids privacy and confidentiality issues associated with real operational data.

Limitations

• Metadata is minimal; actual content requires verification after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and license are unknown, which may limit suitability assessment.

Provenance

Source

Kaggle