AI-Powered Demand Forecasting & Inventory Optimization for Retailers
Retailers face a persistent challenge: balancing inventory.
- Overstocking ties up capital and increases storage costs.
- Stockouts lead to lost sales, frustrated customers, and damaged reputation.
- Manual forecasting (Excel sheets, gut-feel decisions) is error-prone and reactive.
Smart Inventory AI Pro solves this problem with a modular, explainable AI system that delivers:
- 📈 Accurate demand forecasts
- 📦 Optimized reorder points and safety stock
- 🔍 Anomaly detection for unusual sales patterns
- 💡 Business-friendly insights that drive smarter decisions
The impact: higher profitability, reduced waste, and improved customer satisfaction — all in a lightweight, plug-and-play solution.
Retail shop owners often struggle with:
- Overstocking → Capital locked in unsold goods
- Stockouts → Lost revenue and customer churn
- Manual Forecasting → Time-consuming, error-prone spreadsheets
- Complex ERP Systems → Expensive, hard to implement, overkill for small shops
- Basic Dashboards → Show past sales but lack predictive intelligence
| Existing Solutions | Limitations | Smart Inventory AI Pro Advantage |
|---|---|---|
| Excel sheets | Manual, error-prone | Automated AI forecasting |
| ERP systems | Costly, complex | Lightweight, modular, easy to deploy |
| Sales dashboards | Historical only | Predictive + prescriptive insights |
Key Differentiators:
- 🧠 AI-Powered Forecasting (ensemble methods for robust accuracy)
- 💡 Explainable Insights (business-friendly recommendations)
- ⚡ Lightweight Deployment (Streamlit-based, runs locally or in cloud)
- 📂 Plug-and-Play CSV Upload (no complex integrations required)
- 🧩 Modular Architecture (easy to extend, customize, and scale)
- Profitability Boost: Reduce capital lock-in and avoid costly stockouts.
- Operational Efficiency: Automate forecasting and inventory planning.
- Accessibility: Designed for small and medium retailers, not just enterprises.
- Scalability: Works for single shops or multi-store chains.
Smart Inventory AI Pro follows a modular, layered design:
- Frontend (Streamlit) → Interactive dashboards and visualizations
- Models Layer → Forecasting, anomaly detection, inventory optimization
- Explainability Layer → Business insights and recommendations
- Utilities Layer → Data loaders, helpers, validation
- Config Layer → Themes, styling, customization
This separation of concerns ensures maintainability, extensibility, and clean interfaces.
- Ensemble Forecasting: EWMA, Linear Regression, Seasonal Decomposition
- Anomaly Detection: Statistical + IQR-based methods with severity scoring
- Safety Stock Optimization: Dynamic calculations based on demand volatility
- Reorder Point Logic: Lead time + demand variability for smarter replenishment
- No more guessing stock levels.
- No more losing sales because of empty shelves.
- No more wasting money on excess inventory.
👉 Just upload your sales CSV, and let Smart Inventory AI Pro tell you what to order, when, and how much.
To make testing and evaluation easier, a ready-to-use kaggle dataset is included in the dataset/ folder.
This dataset allows you to:
- Instantly explore forecasting features
- Test anomaly detection
- Validate inventory optimization logic
- Understand dashboard visualizations
You can either:
- Use the built-in AI-generated sample data inside the app
- Upload the provided dataset from the
dataset/folder - Upload your own retail sales CSV file
date,product_id,units_sold
2024-01-01,PROD001,45
2024-01-02,PROD002,52
2024-01-03,PROD003,48smart_inventory_ai/
│
├── app.py # Main application entry point
├── requirements.txt # Python dependencies
├── README.md # This file
│
├── config/
│ └── theme.py # CSS styling and theme configuration
│
├── data/
│ └── sample_generator.py # Sample data generation
│
├── models/
│ ├── forecasting.py # Forecasting engine with ensemble methods
│ ├── anomaly.py # Anomaly detection algorithms
│ ├── inventory.py # Inventory optimization logic
│ └── explainability.py # Business insights generation
│
├── dashboard/
│ ├── charts.py # Plotly chart generation
│ ├── metrics.py # Metric card components
│ └── layout.py # UI layout and components
│
├── utils/
│ ├── data_loader.py # Data loading and validation
│ └── helpers.py # Helper utility functions
│
└── assets/
├── sia_logo.jpeg # SIA Logo
└── styles.css # Additional CSS styles
🔗 https://smart-inventory-ai.streamlit.app/
Watch the complete walkthrough of SIA – Smart Inventory AI in action:
🔗 Click here to watch the demo video
The demo covers:
- Problem statement
- System architecture flow
- Live web application walkthrough
- Forecasting & inventory optimization
- Explainable AI insights
- Future vision
- 📡 API Integration with POS systems
- 📱 Mobile App for shop owners
- 🧾 Multi-Product Forecasting with portfolio optimization
- 🌍 Cloud Deployment for scalability
- 🛒 Retail-Specific Modules (perishables, seasonal goods, promotions)
# Install dependencies
pip install -r requirements.txt
# Run the application
streamlit run app.pyUpload your CSV (date, product_id, units_sold) and start optimizing inventory instantly.
Validated with walk-forward testing:
- MAE → Average prediction error in units
- MAPE → Percentage-based accuracy measure
Typical accuracy:
-
<10% → Excellent
-
10–20% → Good
-
20% → Fair
We welcome contributions, feature requests, and collaborations.
Open an issue or submit a pull request to join the project.
For questions or support, please open an issue in this repository.
Built with ❤️ by Team Eureka Fourge
Team Information
- Himanshu Jadhav (Team Leader)
- Yash Bhongale
- Ritesh Gaike
- Onkar Kharat