DeepBullwhip

Multi-tier supply chain bullwhip effect simulator with modular demand models, ordering policies, and cost functions.

Maintained by the AI V&V Lab at KFUPM.

Features

• Modular architecture — swap demand generators, ordering policies, and cost functions via ABCs
• Configurable multi-echelon chains — define any serial supply chain topology
• Vectorized Monte Carlo engine — ~100x speedup for batch simulations
• Publication-grade diagnostics — 10 plot functions + network/map visualizations
• 99% test coverage — comprehensive unit tests across all modules

Installation

pip install deepbullwhip

Or install from source:

git clone https://github.com/ai-vnv/deepbullwhip.git
cd deepbullwhip
pip install -e ".[dev]"

Quick Example

import numpy as np
from deepbullwhip import SemiconductorDemandGenerator, SerialSupplyChain

gen = SemiconductorDemandGenerator()
demand = gen.generate(T=156, seed=966)

chain = SerialSupplyChain()
fm = np.full_like(demand, demand.mean())
fs = np.full_like(demand, demand.std())
result = chain.simulate(demand, fm, fs)

for er in result.echelon_results:
    print(f"{er.name}: BW={er.bullwhip_ratio:.2f}, FR={er.fill_rate:.0%}")