Integrated Business Planning (IBP) System

A modular Integrated Business Planning software system that helps align demand, inventory, supply, and financial planning processes.

Features

• IBP for Demand: Short/mid-term demand forecasting using statistical models and ML algorithms
• IBP for Inventory: Stock level optimization using EOQ models and service-level metrics
• IBP for Supply: Production and supply planning based on capacity constraints
• IBP for S&OP: Strategic alignment of supply/demand planning with financial goals
• IBP for Response: Real-time re-planning for supply chain disruptions
• IBP for Control Tower: End-to-end visibility of supply chain performance metrics

Technical Stack

• Backend: Python with FastAPI
• Frontend: Streamlit for interactive dashboards
• Data Processing: Pandas, NumPy, scikit-learn
• Forecasting: ARIMA, XGBoost, Prophet
• Visualization: Plotly, Matplotlib, Seaborn
• ML Monitoring: MLflow, Evidently AI, Deepchecks

Getting Started

1. Clone this repository
2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the Streamlit application:

   cd app
   streamlit run main.py
   

Module Overview

Demand Planning

• Advanced forecasting algorithms including ARIMA, Exponential Smoothing, and Prophet
• Automated model selection based on data characteristics
• Support for multiple time series frequencies (daily, weekly, monthly)
• Interactive visualization of forecast results
• Model performance metrics and validation

Inventory Optimization

• Dynamic safety stock calculations
• EOQ (Economic Order Quantity) optimization
• Service level-based inventory planning
• ABC/XYZ analysis
• Inventory cost optimization

Supply Planning

Plan production and procurement based on demand forecasts and capacity constraints.

S&OP Alignment

Align operational planning with financial objectives and KPIs.

Response Planning

Simulate and respond to supply chain disruptions with dynamic replanning.

Control Tower

Monitor end-to-end supply chain performance with real-time dashboards and alerts.

Data Sources

The system supports various data import methods:

• CSV files with custom formatting
• Excel files (.xlsx, .xls)
• SQL databases
• External APIs (economic indicators, market trends, etc.)

Sample Data

The app/data directory contains sample datasets for testing:

• Sales history
• Inventory levels
• Supply chain data
• KPI metrics
• Alerts and notifications

Recent Updates

• Improved forecasting accuracy with automated frequency detection
• Enhanced UI with better error handling and user feedback
• Streamlined data import process
• Better exception handling and error messages
• Cleaned up codebase and removed redundant files
• Updated dependency management

Scenario Planning

Users can create and simulate various "what-if" scenarios:

• Demand spikes or drops
• Price changes and promotions
• Supply chain disruptions
• Production capacity changes
• Market trend impacts

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for any bugs or feature requests.

Deployment

Local Development

1. Clone the repository:

   git clone https://github.com/bahaeddinmselmi/forcastapp.git
   cd forcastapp

2. Install dependencies:

   pip install -r requirements.txt

3. Run the app:

   streamlit run app/main.py

Streamlit Cloud Deployment

1. Fork this repository to your GitHub account
2. Log in to Streamlit Cloud
3. Create a new app and select your forked repository
4. Set the following:
   • Main file path: app/main.py
   • Python version: 3.13
5. Click "Deploy!"

Configuration

The app can be configured through:

• .streamlit/config.toml for Streamlit settings
• app/config.py for application settings