metadata
license: cc0-1.0
task_categories:
- tabular-classification
- tabular-regression
tags:
- supply-chain
- resilience
- logistics
- disruption
- risk-management
size_categories:
- 1K<n<10K
Logistics Disruption Archive
Supply chain resilience metrics across 1,000 simulated logistics scenarios, covering five industry sectors under various disruption conditions.
Useful for studying how supplier diversity, delivery reliability, and inventory buffers interact to determine overall chain performance under stress.
Usage
from datasets import load_dataset
dataset = load_dataset("scm-resilience-data/logistics-disruption-archive")
df = dataset["train"].to_pandas()
Or use the provided loader:
from loader import load_data
df = load_data()
Schema
Metrics
| Column | Type | Range | Description |
|---|---|---|---|
| supplier_diversity | float | 0–1 | Degree of supplier diversification (higher = more diverse) |
| delivery_consistency | float | 0–1 | Reliability of on-time delivery (higher = more consistent) |
| inventory_buffer | float | 0–1 | Safety stock adequacy relative to demand variance |
| chain_performance | float | 0–1 | Composite supply chain performance score |
Categorical Variables
| Column | Type | Values | Description |
|---|---|---|---|
| sector | string | pharma, automotive, food, textiles, electronics | Industry sector |
| disruption_type | string | cyber, natural, pandemic, geopolitical, none | Type of disruption event |
Statistics
- Rows: 1,000
- Columns: 6
- Sectors: 5
- Disruption types: 5
Potential Use Cases
- Classification: predicting disruption type from metric profiles
- Regression: estimating chain_performance from input metrics
- Clustering: identifying resilience patterns across sectors
- Threshold analysis: determining critical metric levels for chain failure
License
CC0 1.0 Universal (Public Domain)
Note: One thread breaks, the whole tapestry unravels.