GHOST

Zero-shot supply chain disruption forecasting — no labeled data required.

GHOST (Graph-based Hierarchical On-the-fly Self-correcting Threat detector) predicts supply chain disruptions using only standard operational metrics — no historical disruption labels, no pretraining, no external dataset. It runs in ~15 minutes on a T4 GPU across 180k+ orders.

The system is a closed-loop self-distillation framework: it synthesizes its own disruption scenarios, trains its own risk models, and corrects its own drift — entirely from operational data.

How it works

Operational data (orders, lead times, costs)
    │
    ▼
Zero-shot risk inference
  Statistical anomaly detection per node per timestep
  Sigmoid-normalized risk scores — no labels needed
    │
    ▼
Bootstrap synthetic scenario generation
  8 disruption types: port strikes, natural disasters,
  cyber attacks, demand surges, geopolitical conflicts...
  Diversity-constrained candidate selection
    │
    ▼
Bidirectional LSTM with attention
  128 hidden units per direction
  Weighted MSE loss (4x penalty on high-risk misses)
  Bootstrap self-distillation with stability preservation
    │
    ▼
Dual-level GNN risk propagation
  Global: system-wide risk injection to all nodes
  Local: edge-conditioned multi-head attention (8 heads)
  Captures cascading failure patterns
    │
    ▼
Probe-based drift correction
  Embedding centroid monitoring
  Adaptive fine-tuning when drift > 0.015
  No manual retraining or new labels needed
    │
    ▼
Risk predictions + mitigation report
  Per-node risk scores, critical node identification,
  actionable mitigation strategies

Results

Overall performance — DataCo Smart Supply Chain (180,519 orders)

Metric	Value
Prediction MSE	0.008067
High-risk samples identified	30,943 (17.2%)
Bootstrap stability	3/3 iterations preserved
Drift correction	0.000014 → 0.000
Zero-shot scenario mapping	5/5 (100%)
Runtime (T4 GPU)	15.3 minutes
Peak memory	12.4 GB

Ablation

Configuration	MSE	High-Risk ID	Runtime
LSTM only	0.0089	28,456	8.2 min
+ Multi-anchor ensemble	0.0084	29,234	9.1 min
+ Bootstrap (no stability)	0.0112	31,567	12.8 min
+ Stability mechanism	0.0081	30,943	13.2 min
+ Drift correction	0.0081	30,943	14.1 min
Full GHOST system	0.0081	30,943	15.3 min

Comparison with baselines

Method	MSE	High-Risk ID	Runtime
Statistical Threshold	0.0156	18,052	0.8 min
Isolation Forest	0.0134	22,341	3.2 min
Random Forest	0.0098	27,123	5.7 min
LSTM Only	0.0089	28,456	8.2 min
Graph Attention Network	0.0091	29,012	11.4 min
GHOST (Full)	0.0081	30,943	15.3 min

Quick start

git clone https://github.com/rxbinsingh/GHOST
cd GHOST
pip install -r requirements.txt

from src.ghost_complete import GHOST

ghost = GHOST()
data  = ghost.load_data('path/to/supply_chain_data.csv')
results = ghost.run_pipeline(data)

risk_scores      = results['risk_predictions']
high_risk_nodes  = results['high_risk_nodes']

Repository structure

Path	Description
`src/ghost_complete.py`	Full GHOST pipeline — risk inference, LSTM, GNN, drift correction
`src/core/`	Core algorithms — bootstrap, drift detection, stability
`src/models/`	LSTM and GNN model definitions
`src/data/`	Data loading and preprocessing utilities
`src/decision/`	Decision support and mitigation report generation
`notebooks/GHOST_Demo.py`	End-to-end demo
`docs/`	API reference, installation guide, mathematical formulations
`requirements.txt`	Python dependencies

Dataset

Evaluated on the DataCo Smart Supply Chain dataset — 180,519 real supply chain orders, 53 operational features, no ground-truth disruption labels. Ideal for zero-shot evaluation.

Requirements

Python 3.8+
PyTorch 1.12+
NetworkX 2.8+
NVIDIA GPU recommended (T4 or better); CPU supported but slow

Paper

GHOST: Self-Bootstrapping Supply Chain Disruption Forecasting via Multi-Scale Risk Propagation and Adaptive Drift Correction Robin Singh, 2025 https://doi.org/10.13140/RG.2.2.27961.94567

@article{singh2025ghost,
  title   = {GHOST: Self-Bootstrapping Supply Chain Disruption Forecasting
             via Multi-Scale Risk Propagation and Adaptive Drift Correction},
  author  = {Singh, Robin},
  year    = {2025},
  doi     = {10.13140/RG.2.2.27961.94567},
  url     = {https://doi.org/10.13140/RG.2.2.27961.94567}
}

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