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# Supply Chain Simulation - Watch Industry

A comprehensive supply chain simulation for the watch industry, implementing demand forecasting, supply planning, assembly, distribution, and customer behavior modeling.

## Installation

1. Clone or download the project files

2. Install in development mode:

```bash

pip install -e .

```

Or install dependencies directly:

```bash

pip install -r requirements.txt

```

## Project Structure

```

supply_chain_sim/

├── __init__.py              # Package initialization

├── entities.py              # Data models (Brand, Supplier, Customer, etc.)

├── synthetic_setup.py       # Generate synthetic entities

├── forecasting.py           # Demand forecasting with ML

├── supply_order.py          # Supply order logic and planning

├── event_injection.py       # Internal/external event management

├── assembly.py              # Component flow and watch assembly

├── distribution.py          # Retail distribution logic

├── customer_behavior.py     # Customer purchase simulation

├── model_update.py          # AI model updates and tracking

├── research_logging.py      # KPI tracking and reporting

└── simulation.py            # Main simulation orchestrator

```

## Usage

### Basic Run

```bash

python run_simulation.py

```

### With Custom Parameters

```bash

python run_simulation.py --days 60 --customers 1000 --retailers 10 --seed 123

```

### Command Line Options

- `--days`: Number of simulation days (default: 30)

- `--customers`: Number of customers (default: 500)

- `--retailers`: Number of retailers (default: 6)

- `--seed`: Random seed for reproducibility (default: 42)

- `--debug`: Enable debug logging

### Programmatic Usage

```python

from supply_chain_sim import run_oneshot_simulation

# Custom configuration

config = {

    'seed': 42,

    'n_brands': 2,

    'n_suppliers': 5,

    'n_components': 15,

    'n_watches': 8,

    'n_warehouses': 2,

    'n_retailers': 6,

    'n_customers': 500,

    'simulation_days': 30,

    'timestep_days': 1

}

# Run simulation

simulation = run_oneshot_simulation(config)

```

## Key Features

1. **Synthetic Entity Generation**: Creates brands, suppliers, customers, watches with realistic distributions

2. **Demand Forecasting**: ML-based demand prediction with uncertainty intervals

3. **Supply Planning**: Intelligent component ordering based on forecasts and inventory

4. **Event System**: Internal campaigns and external market events

5. **Assembly Simulation**: Component delivery delays and watch assembly with capacity constraints

6. **Distribution Logic**: Regional demand-based distribution to retailers

7. **Customer Behavior**: Purchase probability based on preferences, budget, and brand awareness

8. **Performance Tracking**: GMROI, fill rate, forecast accuracy, and other KPIs

9. **Research Logging**: Comprehensive reporting and baseline comparisons

## Simulation Flow

The simulation follows a linear flow as designed:

1. **Initialization**: Generate all entities

2. **Forecasting**: Predict demand using ML models

3. **Supply Planning**: Generate component orders

4. **Event Management**: Apply campaigns and market events

5. **Assembly**: Process deliveries and assemble watches

6. **Distribution**: Allocate watches to retailers

7. **Customer Simulation**: Process purchases and returns

8. **Model Updates**: Train AI models with new data

9. **KPI Tracking**: Log performance metrics

## Output

The simulation provides:

- Real-time progress updates for each timestep

- KPI tracking (GMROI, fill rate, conversion rate, forecast accuracy)

- Final comprehensive report with:

  - Model performance metrics

  - Average and final KPIs

  - Revenue and cost analysis

  - Comparison against baselines (if set)

## Notes

- Pygame rendering is prepared for future implementation

- Logging can be controlled via the --debug flag

- All random processes use seeds for reproducibility

- The simulation is designed to be modular and extensible

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numpy>=1.21.0

pandas>=1.3.0

pygame>=2.0.0

scikit-learn>=1.0.0

xgboost>=1.5.0

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from setuptools import setup, find_packages

setup(

    name="supply-chain-sim",

    version="0.1.0",

    packages=find_packages(),

    install_requires=[

        "numpy>=1.21.0",

        "pandas>=1.3.0",

        "pygame>=2.0.0",

        "scikit-learn>=1.0.0",

        "xgboost>=1.5.0",

    ],

    python_requires=">=3.8",

)

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"""Supply Chain Simulation Package for Watch Industry."""

from .simulation import SupplyChainSimulation, run_oneshot_simulation

from .synthetic_setup import SyntheticEntitySetup

from .forecasting import Forecasting

from .supply_order import SupplyOrderLogic

from .event_injection import EventInjection

from .assembly import ComponentFlowAssembly

from .distribution import RetailDistribution

from .customer_behavior import CustomerBehavior

from .model_update import ModelUpdate

from .research_logging import ResearchLogging

__version__ = "0.1.0"

__all__ = [

    "SupplyChainSimulation",

    "run_oneshot_simulation",

    "SyntheticEntitySetup",

    "Forecasting",

    "SupplyOrderLogic",

    "EventInjection",

    "ComponentFlowAssembly",

    "RetailDistribution",

    "CustomerBehavior",

    "ModelUpdate",

    "ResearchLogging"

]

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