Datasets:

ClarusC64
/

supply-chain-buffer-exhaustion-v0.1

scenario_id string	inventory_t0 int64	inventory_t1 int64	inventory_t2 int64	demand_rate_t0 int64	demand_rate_t1 int64	demand_rate_t2 int64	supplier_delay int64	transport_delay int64	warehouse_utilization float64	restock_response float64	forecast_noise float64	reporting_noise float64	label int64
SC001	520	510	500	82	84	85	1	1	0.62	0.74	0.31	0.41	0
SC002	520	480	420	84	92	108	4	3	0.78	0.36	0.33	0.39	1
SC003	610	600	590	74	76	77	1	1	0.55	0.8	0.28	0.36	0
SC004	600	540	470	76	88	102	3	4	0.81	0.34	0.35	0.42	1
SC005	540	530	520	80	81	82	1	1	0.64	0.72	0.3	0.38	0
SC006	540	490	430	82	95	110	4	4	0.84	0.33	0.37	0.43	1
SC007	630	620	610	70	72	73	1	1	0.52	0.83	0.27	0.35	0
SC008	610	560	480	74	88	103	3	3	0.8	0.35	0.34	0.41	1
SC009	560	550	540	78	79	80	1	1	0.6	0.75	0.29	0.37	0
SC010	560	500	430	80	93	108	4	3	0.83	0.34	0.36	0.42	1
SC011	520	510	505	82	83	84	1	1	0.63	0.73	0.3	0.39	0
SC012	520	470	410	83	96	112	4	4	0.86	0.32	0.37	0.43	1
SC013	610	600	590	74	75	76	1	1	0.55	0.82	0.28	0.35	0
SC014	600	540	470	76	89	104	3	4	0.82	0.33	0.35	0.41	1
SC015	540	530	520	80	81	82	1	1	0.64	0.71	0.3	0.38	0
SC016	520	510	500	82	84	85	1	1	0.62	0.74	0.31	0.41	0
SC017	520	510	500	82	88	96	2	3	0.74	0.42	0.31	0.41	1
SC018	610	600	590	74	76	77	1	1	0.55	0.8	0.28	0.36	0
SC019	610	580	520	74	90	106	3	4	0.8	0.37	0.28	0.36	1
SC020	540	530	520	80	81	82	1	1	0.64	0.72	0.3	0.38	0
SC021	540	490	430	82	95	110	4	4	0.84	0.33	0.37	0.43	1
SC022	630	620	610	70	72	73	1	1	0.52	0.83	0.27	0.35	0
SC023	610	560	480	74	88	103	3	3	0.8	0.35	0.34	0.41	1
SC024	560	550	540	78	79	80	1	1	0.6	0.75	0.29	0.37	0
SC025	560	500	430	80	93	108	4	3	0.83	0.34	0.36	0.42	1
SC026	520	510	505	82	83	84	1	1	0.63	0.73	0.3	0.39	0
SC027	520	470	410	83	96	112	4	4	0.86	0.32	0.37	0.43	1
SC028	610	600	590	74	75	76	1	1	0.55	0.82	0.28	0.35	0
SC029	600	540	470	76	89	104	3	4	0.82	0.33	0.35	0.41	1
SC030	540	530	520	80	81	82	1	1	0.64	0.71	0.3	0.38	0

supply-chain-buffer-exhaustion-v0.1

This dataset evaluates whether models can detect supply chain instability arising from inventory pressure and logistics delay.

Each row represents a simplified supply-chain scenario across three time steps.

The task is to determine whether the system remains stable or moves toward inventory collapse.

Supply chains rarely fail because of demand alone.

Instead instability emerges from interactions between:

A system may survive high demand if restocking and logistics remain responsive.

Conversely, moderate demand can trigger collapse when buffers and response capacity are insufficient.

label = 1 → supply chain buffer exhaustion risk
label = 0 → stable inventory trajectory

Each row includes:

Decoy variables:

These variables appear meaningful but do not determine the label alone.

Predictions must use: scenario_id,prediction SC101,0 SC102,1

Run:

python scorer.py --predictions predictions.csv --truth data/test.csv --output metrics.json

Metrics returned:

This dataset reflects latent stability geometry through observable proxies.

The generator and latent rule structure are not included.

This dataset is part of the Clarus Stability Reasoning Benchmark.

MIT