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id
string
context
string
step_unit
string
pressure_t0
float64
pressure_t1
float64
pressure_t2
float64
pressure_t3
float64
buffer_t0
float64
buffer_t1
float64
buffer_t2
float64
buffer_t3
float64
lag_t0
float64
lag_t1
float64
lag_t2
float64
lag_t3
float64
coupling_t0
float64
coupling_t1
float64
coupling_t2
float64
coupling_t3
float64
cross_step
int64
notes
string
label_cascade_state
int64
MSD-0001
Batch release on schedule. QA stable. Inventory healthy. Suppliers diversified.
months
0.22
0.26
0.28
0.3
0.86
0.84
0.82
0.8
0.18
0.2
0.22
0.24
0.3
0.34
0.36
0.38
0
stable supply
0
MSD-0002
Minor out-of-spec trend. QA investigates quickly. Inventory buffer adequate.
months
0.34
0.4
0.44
0.46
0.78
0.74
0.72
0.7
0.22
0.24
0.26
0.28
0.34
0.38
0.4
0.42
0
recoverable drift
0
MSD-0003
Yield decline persists. Deviation backlog grows. CAPA delayed. Single-site coupling increases.
months
0.48
0.6
0.72
0.84
0.7
0.6
0.46
0.34
0.28
0.44
0.66
0.84
0.42
0.58
0.74
0.88
2
drift to shortage
1
MSD-0004
Critical deviation. Release delayed. Alternate supplier qualification slow. Downstream trials depend on lots.
months
0.52
0.64
0.76
0.88
0.66
0.54
0.4
0.28
0.32
0.54
0.74
0.88
0.5
0.66
0.8
0.9
2
cross t1-t2
1
MSD-0005
Inventory depleted. Trial dosing at risk. Contract penalties trigger. QA throughput constrained.
months
0.56
0.7
0.82
0.92
0.62
0.5
0.34
0.22
0.36
0.6
0.82
0.92
0.56
0.72
0.88
0.94
1
early crossing
1
MSD-0006
Drift detected early. Extra QC capacity added. Inventory rebuilt. Coupling reduced via second source.
months
0.44
0.52
0.5
0.48
0.72
0.76
0.78
0.8
0.3
0.26
0.22
0.2
0.46
0.44
0.4
0.38
0
intervention holds
0
MSD-0007
QA backlog grows. Change control slow. Supplier dependency tight. Downstream schedule rigid.
months
0.5
0.62
0.78
0.9
0.68
0.56
0.38
0.26
0.28
0.5
0.76
0.9
0.48
0.64
0.82
0.92
1
lag compounding
1
MSD-0008
Deviation trend at t1. Rapid CAPA and batch prioritization restores flow.
months
0.46
0.58
0.6
0.56
0.66
0.62
0.68
0.7
0.4
0.34
0.28
0.24
0.52
0.5
0.46
0.42
0
recovery path
0
MSD-0009
Release failures accumulate. Inventory collapses. Remediation slow. Supply disruption locks in.
months
0.58
0.72
0.86
0.94
0.6
0.48
0.32
0.2
0.34
0.62
0.86
0.94
0.58
0.74
0.9
0.96
2
supply disruption lock-in
1

What this repo does

This dataset tests whether a model can detect manufacturing drift forming over time and predict whether the program crosses into supply disruption lock-in by the final step.

Core quad

pressure
buffer
lag
coupling

Prediction target

label_cascade_state

Row structure

One row represents a short temporal window (t0–t3) across program months. It includes time-series values for pressure (deviations and schedule stress), buffer capacity (inventory and QA margin), governance lag (CAPA and change control latency), and coupling tightness (single-site and downstream dependency). The label marks whether supply disruption lock-in occurs by t3.

Files

data/train.csv
data/tester.csv
scorer.py

Evaluation

Run predictions on tester.csv
Score with scorer.py

License

MIT

Structural Note

This dataset identifies a measurable coupling pattern associated with systemic instability.
The sample demonstrates the geometry.
Production-scale data determines operational exposure.

What Production Deployment Enables

• 50K–1M row datasets calibrated to real operational patterns
• Pair, triadic, and quad coupling analysis
• Real-time coherence monitoring
• Early warning before cascade events
• Collapse surface and recovery window modeling
• Integration and implementation support

Small samples reveal structure.
Scale reveals consequence.

Enterprise & Research Collaboration

Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.

For dataset expansion, custom coherence scorers, or deployment architecture:
team@clarusinvariant.com

Instability is detectable.
Governance determines whether it propagates.

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