DeepBullwhip

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

Maintained by the AI Verification & Validation (AI V&V) Lab at King Fahd University of Petroleum & Minerals (KFUPM).

Overview

DeepBullwhip provides a configurable simulation framework for studying the bullwhip effect in serial supply chains. It is designed for researchers and practitioners who need to:

Simulate multi-echelon supply chains under different demand patterns
Model arbitrary DAG supply chain topologies (serial, tree, convergent/divergent)
Compare ordering policies (Order-Up-To, custom policies) and cost structures
Quantify bullwhip amplification, fill rates, and total supply chain costs
Optimize inventory levels and policy parameters using mathematical programming
Generate publication-grade diagnostic visualizations (matplotlib + Graphviz)
Run Monte Carlo experiments to study forecast-accuracy vs. robustness tradeoffs
Integrate with the Python ecosystem: NetworkX, Graphviz, Pyomo

The package is extracted from a computational study on the accuracy–robustness tradeoff in ML-driven semiconductor supply chains (see simulation.ipynb).

Features

Component	Description
Demand generators	Pluggable via `DemandGenerator` ABC. Built-in: AR(1) semiconductor, Beer Game step, ARMA(p,q), Replay from data
Ordering policies	Pluggable via `OrderingPolicy` ABC. Built-in: OUT, Proportional OUT, Smoothing OUT, Constant Order
Cost functions	Pluggable via `CostFunction` ABC. Built-in: Newsvendor (h+b), Perishable (h+b+obsolescence)
Forecasters	Pluggable via `Forecaster` ABC. Built-in: Naive, Moving Average, Exponential Smoothing, DeepAR (GluonTS)
Benchmarking	`BenchmarkRunner` for standardized policy/forecaster comparison with LaTeX/CSV export
Datasets	Built-in datasets: Beer Game, WSTS semiconductor, synthetic AR(1)/ARMA, M5 Walmart
Registry	Decorator-based `@register` system for easy extensibility and model discovery
Supply chain	`SerialSupplyChain` supporting arbitrary K-echelon serial topologies via `EchelonConfig`
Network topologies	`SupplyChainGraph` + `NetworkSupplyChain` for arbitrary DAG supply chains (trees, convergent/divergent)
NetworkX integration	Bidirectional graph conversion, critical path analysis, centrality, topological ordering
Graphviz visualization	Publication-quality SVG/PDF network rendering with metrics overlay
Pyomo optimization	Inventory optimization, policy parameter tuning, network design (MIP)
Diagnostics	10 publication-grade plot functions + network diagram + geographic map visualization
Metrics	BWR, NSAmp, Fill Rate, Total Cost, Chen lower bound (standalone module + backward-compat diagnostics)
Vectorized engine	`VectorizedSupplyChain` — matrix-based `(N, K, T)` simulation for Monte Carlo batching. ~100x speedup over serial for N=1000 paths

Installation

# Install from PyPI
pip install deepbullwhip

# With all optional dependencies
pip install deepbullwhip[all]

For development:

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

Dependencies

Core: numpy, scipy, pandas, matplotlib
Dev: pytest, pytest-cov
Optional (Network): networkx (pip install deepbullwhip[network])
Optional (Viz): graphviz (pip install deepbullwhip[viz])
Optional (Optimize): pyomo (pip install deepbullwhip[optimize])
Optional (ML): scikit-learn, torch, gluonts (pip install deepbullwhip[ml])
Optional (Benchmark): kaggle, tabulate
All optional: pip install deepbullwhip[all]

Apple Silicon (MPS) note: GluonTS/PyTorch Lightning may fail on M1/M2/M3 Macs when the MPS backend is auto-selected. Set PYTORCH_ENABLE_MPS_FALLBACK=1 before running DeepAR training or benchmarks:
export PYTORCH_ENABLE_MPS_FALLBACK=1
python benchmarks/run_leaderboard.py
The CAIE experiment scripts set this automatically.

Quick Start

import numpy as np
from deepbullwhip import (
    SemiconductorDemandGenerator,
    SerialSupplyChain,
)

# 1. Generate demand (156 weeks, with shock at week 104)
gen = SemiconductorDemandGenerator()
demand = gen.generate(T=156, seed=42)

# 2. Simulate the default 4-echelon semiconductor supply chain
chain = SerialSupplyChain()
forecasts_mean = np.full_like(demand, demand.mean())
forecasts_std = np.full_like(demand, demand.std())
result = chain.simulate(demand, forecasts_mean, forecasts_std)

# 3. Inspect results
for k, er in enumerate(result.echelon_results):
    print(f"E{k+1}: {er.name:12s}  BW={er.bullwhip_ratio:.2f}  "
          f"FR={er.fill_rate:.0%}  Cost={er.total_cost:,.0f}")

Benchmarking (v0.2.0)

Compare ordering policies and forecasting methods in a single call:

from deepbullwhip.benchmark import BenchmarkRunner

runner = BenchmarkRunner(
    chain_config="semiconductor_4tier",  # or "beer_game", "consumer_2tier"
    demand="semiconductor_ar1",          # or "beer_game", "arma"
    T=156, N=100, seed=42,
)

# Compare policies
results = runner.run(
    policies=[
        "order_up_to",
        ("proportional_out", {"alpha": 0.3}),
        ("constant_order", {"order_quantity": 11.6}),
    ],
    forecasters=["naive", ("moving_average", {"window": 10})],
    metrics=["BWR", "FILL_RATE", "TC"],
)

# View results
print(results.pivot_table(index=["policy","echelon"], columns="metric", values="value"))

# Export
runner.export_csv(results, "benchmark_results.csv")
runner.export_latex(results, "benchmark_table.tex", caption="Policy Comparison")

Adding Custom Models

Extend the framework with the 3-step pattern:

from deepbullwhip.policy.base import OrderingPolicy
from deepbullwhip.registry import register

@register("policy", "my_policy")
class MyPolicy(OrderingPolicy):
    def __init__(self, lead_time: int, service_level: float = 0.95):
        self.lead_time = lead_time
    def compute_order(self, inventory_position, forecast_mean, forecast_std):
        return max(0.0, forecast_mean * 1.5 - inventory_position)

# Now use it in benchmarks:
results = runner.run(policies=["order_up_to", "my_policy"])

See Notebook 03: Custom Policies for a full walkthrough.

Real-World Dataset Benchmarks

Run benchmarks on well-known demand datasets out of the box:

from deepbullwhip.datasets.loader import load_dataset
from deepbullwhip.demand.replay import ReplayDemandGenerator

# Load M5 Walmart, Australian PBS, WSTS, or Beer Game
demand = load_dataset("m5", store="CA_1", dept="FOODS_1", freq="weekly")

runner = BenchmarkRunner(
    chain_config="consumer_2tier",
    demand=ReplayDemandGenerator(data=demand),
    T=200, N=10, seed=42,
)
results = runner.run(policies=["order_up_to", ("proportional_out", {"alpha": 0.3})])

Dataset	Source	Frequency	Periods
M5 Walmart	Kaggle M5 Competition	Weekly	277
Australian PBS	tidyverts/tsibbledata	Monthly	197
WSTS Semiconductor	Bundled sample	Monthly	60
Beer Game	Built-in	Weekly	52

Download scripts for each dataset are in data/raw/*/download.sh. See notebooks/08_benchmark_real_datasets.ipynb for a cross-dataset comparison.

Network Topologies (v0.3.0)

Model arbitrary DAG supply chains beyond serial chains:

from deepbullwhip import SupplyChainGraph, EdgeConfig, NetworkSupplyChain, EchelonConfig
import numpy as np

# Define a distribution tree: Factory -> Warehouse -> {Retail_A, Retail_B}
graph = SupplyChainGraph(
    nodes={
        "Factory": EchelonConfig("Factory", lead_time=4, holding_cost=0.10, backorder_cost=0.40),
        "Warehouse": EchelonConfig("Warehouse", lead_time=2, holding_cost=0.15, backorder_cost=0.50),
        "Retail_A": EchelonConfig("Retail_A", lead_time=1, holding_cost=0.20, backorder_cost=0.60),
        "Retail_B": EchelonConfig("Retail_B", lead_time=1, holding_cost=0.20, backorder_cost=0.60),
    },
    edges={
        ("Factory", "Warehouse"): EdgeConfig(lead_time=3),
        ("Warehouse", "Retail_A"): EdgeConfig(lead_time=1),
        ("Warehouse", "Retail_B"): EdgeConfig(lead_time=1),
    },
)

# Simulate
chain = NetworkSupplyChain(graph)
T = 52
result = chain.simulate(
    demand={"Retail_A": np.full(T, 5.0), "Retail_B": np.full(T, 3.0)},
    forecasts_mean={"Retail_A": np.full(T, 5.0), "Retail_B": np.full(T, 3.0)},
    forecasts_std={"Retail_A": np.full(T, 1.0), "Retail_B": np.full(T, 1.0)},
)

for name, er in result.node_results.items():
    print(f"{name:12s}  BW={er.bullwhip_ratio:.2f}  FR={er.fill_rate:.0%}")

NetworkX Integration

from deepbullwhip import to_networkx, from_networkx
from deepbullwhip.network import find_critical_path, echelon_centrality

# Convert to NetworkX for graph analysis
G = to_networkx(graph)
print("Critical path:", find_critical_path(G))
print("Centrality:", echelon_centrality(G))

# Build from NetworkX
import networkx as nx
G = nx.DiGraph()
G.add_node("Supplier", lead_time=4, holding_cost=0.1, backorder_cost=0.4)
G.add_node("Store", lead_time=1, holding_cost=0.2, backorder_cost=0.6)
G.add_edge("Supplier", "Store", lead_time=2)
chain = NetworkSupplyChain.from_networkx(G)

Graphviz Visualization

from deepbullwhip import render_network, save_figure

# Render network diagram (with optional simulation overlay)
source = render_network(graph, sim_result=result, engine="dot", title="Distribution Tree")
save_figure(source, "network.pdf")

Pyomo Optimization

from deepbullwhip.optimize import tune_service_levels, tune_smoothing_factors

# Find optimal service levels via simulation-optimization
scenarios = np.random.default_rng(42).normal(10, 2, (50, 52))
scenarios = np.maximum(scenarios, 0)

result = tune_service_levels(graph, scenarios, objective="total_cost")
print("Optimal service levels:", result.parameters)
print("Expected cost:", result.objective_value)

# Find optimal smoothing factors
result = tune_smoothing_factors(graph, scenarios)
print("Optimal alpha_s:", result.parameters)

Standardized Schema + Multi-Backend Rendering (v0.3.0)

Define supply chains in a standard JSON format and render identically across matplotlib, Graphviz, and TikZ:

JSON Schema

{
  "version": "1.0",
  "metadata": {"name": "Consumer 2-Tier", "tags": ["serial", "2-echelon"]},
  "nodes": [
    {"id": "Manufacturer", "config": {"lead_time": 4, "holding_cost": 0.10, "backorder_cost": 0.40},
     "layout": {"tier": 0, "role": "manufacturer"}},
    {"id": "Retailer", "config": {"lead_time": 1, "holding_cost": 0.20, "backorder_cost": 0.80},
     "layout": {"tier": 1, "role": "retailer"}}
  ],
  "edges": [{"source": "Manufacturer", "target": "Retailer", "config": {"lead_time": 3}}]
}

Multi-Backend Rendering

from deepbullwhip import render_graph, from_serial, to_json, save_json, load_json
from deepbullwhip.chain.config import beer_game_config

graph = from_serial(beer_game_config())

# Save to standard JSON
save_json(graph, "beer_game.json", metadata={"name": "Beer Game"})

# Render with matplotlib (default) — 4 built-in themes
fig = render_graph(graph, theme="kfupm")            # KFUPM green/gold (default)
fig = render_graph(graph, theme="ieee")              # IEEE grayscale, 3.5" width
fig = render_graph(graph, theme="presentation")      # Large fonts for slides
fig = render_graph(graph, theme="minimal")           # Clean black & white

# Render as TikZ for LaTeX papers
tex = render_graph(graph, backend="tikz", theme="ieee", title="Beer Game")
with open("beer_game.tex", "w") as f:
    f.write(tex)

# Render with Graphviz (requires pip install deepbullwhip[viz])
source = render_graph(graph, backend="graphviz", engine="dot")

# One-liner: load JSON and render
fig = render_from_json("beer_game.json", theme="kfupm")

Supply Chain Examples (Different Tier Counts)

2-Tier (Manufacturer → Retailer):

from deepbullwhip.chain.config import consumer_2tier_config
fig = render_graph(from_serial(consumer_2tier_config()), theme="minimal")

4-Tier Beer Game (Factory → Distributor → Wholesaler → Retailer):

from deepbullwhip.chain.config import beer_game_config
fig = render_graph(from_serial(beer_game_config()), theme="kfupm", title="MIT Beer Game")

Distribution Tree (Factory → Warehouse → {Store A, Store B}):

from deepbullwhip import SupplyChainGraph, EdgeConfig, EchelonConfig, render_graph

tree = SupplyChainGraph(
    nodes={
        "Factory": EchelonConfig("Factory", 4, 0.10, 0.40),
        "Warehouse": EchelonConfig("Warehouse", 2, 0.15, 0.50),
        "Store_A": EchelonConfig("Store_A", 1, 0.20, 0.60),
        "Store_B": EchelonConfig("Store_B", 1, 0.20, 0.60),
    },
    edges={
        ("Factory", "Warehouse"): EdgeConfig(lead_time=3),
        ("Warehouse", "Store_A"): EdgeConfig(lead_time=1),
        ("Warehouse", "Store_B"): EdgeConfig(lead_time=1),
    },
)
fig = render_graph(tree, theme="presentation", title="Distribution Network")
tex = render_graph(tree, backend="tikz", theme="ieee")  # For LaTeX papers

Benchmark Leaderboard

See the Benchmark Leaderboard on the docs site (source) for an interactive table: pick demand, policy, forecaster, and metric columns via checklists. Defaults match BenchmarkRunner metrics (BWR, CUM_BWR, FILL_RATE, TC).

Run it yourself (writes docs/leaderboard.html plus a short docs/LEADERBOARD.md):

python benchmarks/run_leaderboard.py
python benchmarks/run_leaderboard.py --help   # --demands, --policies, --forecasters, --compute-metrics, …

Contributing a New Component

deepbullwhip uses a registry architecture — adding a new forecaster, policy, or demand generator requires no changes to existing code.

Adding a Forecaster

1. Implement the Forecaster ABC

Create a file deepbullwhip/forecast/my_forecaster.py:

import numpy as np
from deepbullwhip.forecast.base import Forecaster
from deepbullwhip.registry import register

@register("forecaster", "my_forecaster")
class MyForecaster(Forecaster):
    """One-line description."""

    def __init__(self, my_param: float = 1.0):
        self.my_param = my_param

    def forecast(
        self, demand_history: np.ndarray, steps_ahead: int = 1
    ) -> tuple[float, float]:
        # Your logic here — must return (mean, std)
        mean = float(np.mean(demand_history[-10:]))
        std = float(np.std(demand_history[-10:]))
        return mean, std

Key points:

Must inherit from Forecaster
Must use @register("forecaster", "name") decorator
Must implement forecast(demand_history, steps_ahead) -> (mean, std)
Optionally override generate_forecasts(demand) for batch efficiency
If it has heavy dependencies (torch, gluonts, etc.), guard imports inside methods

2. Register in __init__.py

Add to deepbullwhip/forecast/__init__.py:

# For lightweight dependencies:
from deepbullwhip.forecast.my_forecaster import MyForecaster

# For heavy/optional dependencies:
try:
    from deepbullwhip.forecast.my_forecaster import MyForecaster
except ImportError:
    pass  # requires optional dependency X

3. Test locally

from deepbullwhip.benchmark import BenchmarkRunner

runner = BenchmarkRunner("semiconductor_4tier", "semiconductor_ar1", T=156, N=200, seed=42)
results = runner.run(
    policies=["order_up_to"],
    forecasters=["naive", "my_forecaster"],
    metrics=["BWR", "CUM_BWR", "FILL_RATE", "TC"],
)
print(results.pivot_table(
    index=["forecaster", "echelon"],
    columns="metric", values="value",
).to_string(float_format="%.1f"))

4. Run the official benchmark

python benchmarks/run_leaderboard.py

Include the output in your PR description.

Adding a Policy

Same pattern — implement OrderingPolicy ABC:

from deepbullwhip.policy.base import OrderingPolicy
from deepbullwhip.registry import register

@register("policy", "my_policy")
class MyPolicy(OrderingPolicy):
    def compute_order(self, inventory_position, forecast_mean, forecast_std) -> float:
        ...

Adding a Demand Generator

Same pattern — implement DemandGenerator ABC:

import numpy as np
from deepbullwhip.demand.base import DemandGenerator
from deepbullwhip.registry import register

@register("demand", "my_demand")
class MyDemandGenerator(DemandGenerator):
    def generate(self, T: int, seed: int | None = None) -> np.ndarray:
        ...

PR Checklist

Component file in the appropriate module (forecast/, policy/, demand/)
@register() decorator with unique name
Updated module __init__.py
Unit test in tests/
Benchmark output from python benchmarks/run_leaderboard.py pasted in PR description
Optional dependencies added to pyproject.toml extras (if any)
If your component requires heavy/optional dependencies (torch, gluonts, etc.), guard tests with pytest.mark.skipif so they skip gracefully when deps are missing

Default Supply Chain Configuration

Echelon	Role	Lead Time	h (holding)	b (backorder)
E1	Distributor / OEM	2 weeks	0.15	0.60
E2	Assembly & Test (OSAT)	4 weeks	0.12	0.50
E3	Foundry / Fab	12 weeks	0.08	0.40
E4	Wafer / Material Supplier	8 weeks	0.05	0.30

Vectorized Monte Carlo Simulation

For large-scale experiments, use the matrix-based engine that processes N demand paths simultaneously via NumPy broadcasting:

from deepbullwhip import SemiconductorDemandGenerator, VectorizedSupplyChain

gen = SemiconductorDemandGenerator()
demand = gen.generate_batch(T=156, n_paths=1000, seed=42)  # (1000, 156)

vchain = VectorizedSupplyChain()
fm = np.full_like(demand, demand.mean())
fs = np.full_like(demand, demand.std())
result = vchain.simulate(demand, fm, fs)

# Average metrics across all 1000 paths
print(result.mean_metrics())

# Extract a single path as standard SimulationResult
sr = result.to_simulation_result(path_index=0)

Benchmark (N=1000, T=156, K=4):

Engine	Time	Speedup
Serial (`SerialSupplyChain`)	3.9s	1x
Vectorized (`VectorizedSupplyChain`)	0.04s	~100x

The vectorized engine uses:

Pre-allocated (N, K, T) order/inventory/cost matrices
Circular buffer pipeline with O(1) indexing (vs O(L) list.pop)
Fully vectorized OUT policy and newsvendor cost across N paths and K echelons per time step
Batch demand generation via generate_batch() with (N, T) noise matrix

Customization

Custom echelon configuration

from deepbullwhip import EchelonConfig, SerialSupplyChain

configs = [
    EchelonConfig("Retailer", lead_time=1, holding_cost=0.20, backorder_cost=0.80),
    EchelonConfig("Manufacturer", lead_time=6, holding_cost=0.10, backorder_cost=0.40),
]
chain = SerialSupplyChain.from_config(configs)

Custom ordering policy

from deepbullwhip.policy.base import OrderingPolicy

class MyPolicy(OrderingPolicy):
    def compute_order(self, inventory_position, forecast_mean, forecast_std):
        # Your logic here
        return max(0.0, forecast_mean - inventory_position)

Custom cost function

from deepbullwhip.cost.base import CostFunction

class MyCost(CostFunction):
    def compute(self, inventory):
        # Your logic here
        return abs(inventory) * 0.1

Visualization

Diagnostic plots

All plot functions return matplotlib.figure.Figure objects and support width="single" (3.5") or width="double" (7.0") for journal formatting. Colors use the KFUPM AI V&V Lab palette.

from deepbullwhip.diagnostics.plots import (
    plot_demand_trajectory,
    plot_order_quantities,
    plot_inventory_levels,
    plot_inventory_position,
    plot_order_streams,
    plot_cost_timeseries,
    plot_cost_decomposition,
    plot_bullwhip_amplification,
    plot_summary_dashboard,
    plot_echelon_detail,
)

fig = plot_summary_dashboard(demand, result)
fig.savefig("dashboard.pdf", dpi=300)

Network and geographic visualization

from deepbullwhip.diagnostics.network import (
    kfupm_petrochemical_network,
    plot_network_diagram,
    plot_supply_chain_map,
)

network = kfupm_petrochemical_network()
fig = plot_network_diagram(network, sim_result=result)
fig = plot_supply_chain_map(network, sim_result=result)

Batch figure generation

python scripts/visualize.py --save --outdir figures --dpi 600

Project Structure

deepbullwhip/
├── __init__.py                 # Public API re-exports
├── _types.py                   # TimeSeries, EchelonResult, SimulationResult
├── registry.py                 # Decorator-based @register system
├── sensitivity.py              # Forecast sensitivity (lambda_f)
├── demand/
│   ├── base.py                 # DemandGenerator ABC
│   ├── semiconductor.py        # AR(1) + seasonal + shock
│   ├── beer_game.py            # Classic Beer Game step demand
│   ├── arma.py                 # General ARMA(p,q) process
│   └── replay.py              # Replay from historical data
├── policy/
│   ├── base.py                 # OrderingPolicy ABC
│   ├── order_up_to.py          # Order-Up-To (OUT) policy
│   ├── proportional_out.py     # Proportional OUT (POUT)
│   ├── constant_order.py       # Constant order (BWR=0)
│   └── smoothing_out.py        # Smoothing OUT
├── cost/
│   ├── base.py                 # CostFunction ABC
│   ├── newsvendor.py           # Newsvendor h/b cost
│   └── perishable.py           # Perishable (h+b+obsolescence)
├── forecast/
│   ├── base.py                 # Forecaster ABC
│   ├── naive.py                # Naive (sample mean/std)
│   ├── moving_average.py       # Rolling window MA
│   ├── exponential_smoothing.py # Single exponential smoothing
│   └── deepar.py               # DeepAR neural forecaster (GluonTS)
├── metrics/
│   ├── bullwhip.py             # BWR, CumulativeBWR
│   ├── inventory.py            # NSAmp, FillRate
│   ├── cost.py                 # TotalCost
│   └── bounds.py               # ChenLowerBound
├── benchmark/
│   ├── runner.py               # BenchmarkRunner
│   ├── configs.py              # Predefined chain configs
│   └── report.py               # LaTeX, Markdown, CSV export
├── datasets/
│   ├── beer_game.py            # Beer Game step demand
│   ├── synthetic.py            # AR(1), ARMA generators
│   ├── m5.py                   # M5 Walmart data loader
│   └── wsts.py                 # WSTS semiconductor data
├── chain/
│   ├── config.py               # EchelonConfig + defaults
│   ├── echelon.py              # SupplyChainEchelon
│   ├── serial.py               # SerialSupplyChain
│   ├── vectorized.py           # VectorizedSupplyChain (N,K,T) matrix engine
│   ├── graph.py                # SupplyChainGraph, EdgeConfig (v0.3.0)
│   └── network_sim.py          # NetworkSupplyChain (v0.3.0)
├── network/                    # NetworkX integration (v0.3.0)
│   ├── convert.py              # to_networkx, from_networkx
│   └── analysis.py             # critical path, centrality, etc.
├── optimize/                   # Pyomo optimization (v0.3.0)
│   ├── inventory.py            # Multi-echelon inventory optimization
│   ├── policy_tuning.py        # Service level / smoothing tuning
│   └── network_design.py       # Facility location MIP (experimental)
└── diagnostics/
    ├── metrics.py              # Bullwhip ratio, fill rate, etc.
    ├── plots.py                # 10 publication-grade plot functions
    ├── network.py              # Network diagram + geographic map
    └── graphviz_viz.py         # Graphviz rendering (v0.3.0)

├── schema/                     # JSON schema (v0.3.0)
│   ├── definition.py           # NodeLayoutHint, LayoutDefaults, NetworkMetadata
│   └── io.py                   # to_json, from_json, save/load
├── render/                     # Multi-backend renderer (v0.3.0)
│   ├── theme.py                # 4 built-in themes + registry
│   ├── layout.py               # Auto-layout from topology
│   ├── _matplotlib.py          # Matplotlib backend
│   ├── _graphviz.py            # Graphviz backend
│   ├── _tikz.py                # TikZ/LaTeX backend
│   └── api.py                  # Unified render_graph() entry point

benchmarks/
└── run_leaderboard.py              # Auto-generate docs/LEADERBOARD.md

scripts/
├── visualize.py                    # Batch diagnostic figure generation
├── generate_caie_figures.py        # CAIE 2026 paper figures (14 figures)
├── run_all_experiments.py           # CAIE 2026 paper numerical results
├── exp_chen_validation.py          # Chen et al. (2000) BWR formula validation
├── exp_corollary1.py               # Cumulative BWR concentration theorem
├── exp_cost_policy.py              # Cost asymmetry x policy interaction
└── exp_pareto.py                   # POUT alpha Pareto frontier

tests/                          # 385 unit tests, 98%+ coverage
notebooks/                      # All notebooks run on Google Colab
├── 01_supply_chain_cost.ipynb      # Costs, inventory, service level tradeoffs
├── 02_bullwhip_effect.ipynb        # Bullwhip amplification & Monte Carlo
├── 03_custom_policies.ipynb        # Custom policies, smoothing, @register
├── 04_network_viz_tutorial.ipynb   # DAG topologies, JSON schema, themes, NetworkX
├── 05_pyomo_optimization.ipynb     # Policy tuning, inventory opt, network design
├── 06_benchmark_policies.ipynb      # Systematic policy comparison
├── 07_benchmark_forecasters.ipynb   # Forecaster comparison
└── 08_benchmark_real_datasets.ipynb # M5, WSTS, Beer Game benchmarks

Testing

# Run all tests
python -m pytest tests/ -v

# With coverage
python -m pytest tests/ --cov=deepbullwhip --cov-report=term-missing

Current: 385 tests across all modules, 98%+ coverage.

Tutorials

All notebooks include Google Colab setup cells and run standalone.

Notebook	Topic
01 Supply Chain Cost	Newsvendor costs, holding vs backorder, service level tradeoffs
02 Bullwhip Effect	Bullwhip amplification, Monte Carlo validation, Chen lower bound
03 Custom Policies	Implementing & registering custom ordering policies
04 Network & Viz	DAG topologies, JSON schema, NetworkX integration, multi-backend rendering
05 Pyomo Optimization	Service level tuning, inventory optimization, network design

Citation

If you use DeepBullwhip in your research, please cite:

@software{deepbullwhip,
  title  = {DeepBullwhip: Multi-Tier Supply Chain Bullwhip Effect Simulator},
  author = {Arief, Mansur M.},
  url    = {https://github.com/ai-vnv/deepbullwhip},
  year   = {2025}
}

Documentation

Full API documentation is available at ai-vnv.github.io/deepbullwhip.

License

MIT License. See LICENSE for details.

Developed and maintained by the AI V&V Lab at KFUPM.

Name		Name	Last commit message	Last commit date
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.github		.github
benchmarks		benchmarks
data		data
deepbullwhip		deepbullwhip
docs		docs
notebooks		notebooks
scripts		scripts
tests		tests
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
README.md		README.md
mkdocs.yml		mkdocs.yml
pyproject.toml		pyproject.toml
vnv-badge.json		vnv-badge.json
vnv-report.json		vnv-report.json
vnvspec.yaml		vnvspec.yaml

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