Datasets:

scm-resilience-data
/

logistics-disruption-archive

supplier_diversity float64 0.01 0.98	delivery_consistency float64 0 0.96	inventory_buffer float64 0.09 0.98	chain_performance float64 0 0.6	sector stringclasses 5 values	disruption_type stringclasses 5 values
0.166164	0.189137	0.630975	0.014419	pharma	cyber
0.109329	0.302354	0.657761	0.085153	automotive	none
0.564806	0.286623	0.752381	0.164517	food	natural
0.502764	0.256733	0.287824	0.021578	textiles	natural
0.440378	0.624657	0.510958	0.162053	food	natural
0.215283	0.473153	0.827343	0.085374	textiles	none
0.554831	0.117366	0.714206	0.034423	pharma	pandemic
0.945397	0.337446	0.85727	0.25098	pharma	natural
0.64563	0.416492	0.535358	0.129501	textiles	geopolitical
0.595597	0.222586	0.358725	0.054598	food	none
0.095709	0.270158	0.429971	0.060632	food	cyber
0.717863	0.74215	0.744211	0.431845	textiles	geopolitical
0.657804	0.704694	0.770111	0.379686	automotive	geopolitical
0.483342	0.582564	0.524811	0.150316	food	pandemic
0.242309	0.533558	0.824465	0.103173	automotive	geopolitical
0.348523	0.45698	0.485921	0.036164	automotive	natural
0.034568	0.449054	0.280892	0	automotive	pandemic
0.3935	0.807821	0.898092	0.269066	pharma	none
0.326663	0.325266	0.384402	0.049771	food	cyber
0.020742	0.529866	0.618254	0	textiles	geopolitical
0.731381	0.615859	0.616027	0.262424	automotive	natural
0.465457	0.495083	0.31475	0.080561	pharma	geopolitical
0.444956	0.147419	0.847684	0.056724	textiles	cyber
0.507525	0.306056	0.766943	0.161532	food	natural
0.535598	0.55995	0.923995	0.275242	electronics	natural
0.043486	0.506584	0.556561	0.027053	electronics	none
0.539179	0.474566	0.827362	0.207273	textiles	geopolitical
0.619093	0.190159	0.904364	0.043899	textiles	cyber
0.378757	0.25287	0.32597	0.064167	pharma	geopolitical
0.196078	0.525134	0.768728	0.094984	food	geopolitical
0.66703	0.551828	0.827902	0.304994	electronics	none
0.266257	0.260525	0.425771	0.085799	automotive	pandemic
0.233838	0.591372	0.64699	0.071339	textiles	geopolitical
0.918528	0.331908	0.520395	0.101841	pharma	pandemic
0.713341	0.789828	0.741843	0.390483	pharma	pandemic
0.839496	0.711858	0.70054	0.406019	textiles	none
0.207291	0.180488	0.773361	0.047122	pharma	none
0.440031	0.427445	0.922514	0.154686	textiles	cyber
0.518251	0.553256	0.449863	0.098461	textiles	none
0.429947	0.287021	0.39774	0.042314	electronics	none
0.491556	0.664209	0.842841	0.352322	electronics	cyber
0.59485	0.533567	0.876915	0.343658	automotive	natural
0.423229	0.153119	0.587206	0.02947	electronics	cyber
0.37757	0.491208	0.562345	0.092174	pharma	cyber
0.595048	0.578751	0.857387	0.210736	automotive	none
0.689686	0.345961	0.708765	0.146294	pharma	natural
0.86319	0.384645	0.61505	0.168604	textiles	none
0.953689	0.393464	0.468687	0.155571	food	none
0.475936	0.472617	0.848276	0.19673	textiles	cyber
0.125513	0.516563	0.524514	0.002754	textiles	cyber
0.21311	0.512077	0.852586	0.107938	pharma	none
0.630387	0.436286	0.500079	0.11509	automotive	natural
0.705887	0.273875	0.565109	0.134813	electronics	geopolitical
0.821402	0.810768	0.331889	0.256169	pharma	cyber
0.668866	0.328766	0.401721	0.052921	electronics	geopolitical
0.488991	0.2307	0.191034	0.018462	food	geopolitical
0.702873	0.295548	0.754646	0.165503	automotive	pandemic
0.838961	0.62751	0.8273	0.44416	food	cyber
0.288492	0.425198	0.742297	0.07511	textiles	cyber
0.331152	0.414705	0.431322	0.111655	pharma	natural
0.078289	0.593403	0.69785	0.063542	pharma	pandemic
0.725771	0.132186	0.914705	0.055138	electronics	cyber
0.691079	0.435488	0.657186	0.175773	textiles	natural
0.81668	0.427614	0.523793	0.225074	pharma	natural
0.924484	0.236369	0.776428	0.185908	pharma	pandemic
0.485585	0.500599	0.923867	0.24135	textiles	geopolitical
0.772763	0.712789	0.454785	0.238752	textiles	none
0.528353	0.369363	0.55837	0.115208	automotive	pandemic
0.106853	0.529949	0.460901	0.032494	food	geopolitical
0.529701	0.657418	0.779537	0.303951	pharma	cyber
0.944193	0.206361	0.398522	0.074614	textiles	none
0.170581	0.725577	0.688647	0.081008	food	geopolitical
0.494529	0.392711	0.648246	0.136442	pharma	natural
0.641867	0.158299	0.355621	0.038296	textiles	geopolitical
0.634339	0.339727	0.687689	0.154864	electronics	pandemic
0.925769	0.102328	0.505213	0.028188	textiles	pandemic
0.811774	0.64045	0.954182	0.471782	automotive	natural
0.836222	0.51655	0.851706	0.411781	electronics	natural
0.559397	0.58635	0.836401	0.260595	food	none
0.160672	0.574599	0.456168	0.160011	food	geopolitical
0.211889	0.520159	0.71856	0.052532	pharma	geopolitical
0.389733	0.153278	0.241195	0.053915	textiles	pandemic
0.27462	0.481922	0.792649	0.11307	textiles	cyber
0.391458	0.184931	0.71365	0.110165	electronics	geopolitical
0.351336	0.428663	0.692765	0.116782	food	geopolitical
0.414128	0.132524	0.514929	0	automotive	none
0.625956	0.53157	0.69001	0.187884	electronics	geopolitical
0.500363	0.051486	0.317132	0.003482	textiles	natural
0.458705	0.511869	0.718134	0.183303	automotive	pandemic
0.866151	0.410029	0.456631	0.150138	automotive	geopolitical
0.602641	0.355413	0.585926	0.135131	pharma	cyber
0.348113	0.265724	0.524239	0.033923	textiles	natural
0.783887	0.371077	0.414523	0.128963	textiles	pandemic
0.572733	0.166961	0.716827	0.02678	textiles	none
0.18224	0.787389	0.739053	0.133611	food	geopolitical
0.616201	0.086668	0.701493	0.032139	food	natural
0.628672	0.129842	0.717156	0.067181	pharma	cyber
0.654531	0.342903	0.767939	0.135705	automotive	geopolitical
0.429673	0.498851	0.606398	0.172972	textiles	none
0.808198	0.412982	0.487221	0.102281	pharma	natural

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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.

Downloads last month: -

Size of downloaded dataset files:

83.7 kB

Size of the auto-converted Parquet files:

83.7 kB

Number of rows:

2,000