The Smart Inventory AI System is an AI-powered decision-support platform designed to optimize inventory management using multi-agent intelligence and human-in-the-loop collaboration.
It analyzes demand patterns, stock levels, and risk factors to generate intelligent recommendations such as restocking, discounting, or maintaining optimal inventory levels.
Traditional inventory systems are:
- ❌ Static and reactive
- ❌ Prone to overstock and stockouts
- ❌ Inefficient in decision-making
This results in:
- 📉 Financial losses
- 📦 Inventory wastage
- ⏳ Delayed business decisions
This project introduces an AI-driven system that:
- 📊 Predicts demand trends
- 📦 Monitors inventory levels
⚠️ Calculates risk scores- 🤖 Generates intelligent recommendations
- 👤 Enables human validation
The system simulates a multi-agent AI framework:
- 🟢 Demand Agent → Predicts future demand
- 🔵 Inventory Agent → Tracks stock levels
- 🟣 Decision Agent → Recommends actions
➡️ All agents collaborate to deliver data-driven decisions.
Core functionalities include:
- 📊 Demand vs Stock analysis
- 📉 Gap calculation
⚠️ Risk scoring
- 🔄 Restock when demand is high
- 💸 Apply Discount for overstock
- ✅ No Action when balanced
A key feature of the system:
- 🤖 AI suggests decisions
- 👤 Humans approve or reject
✔ Ensures trust
✔ Improves reliability
✔ Enables real-world usability
Built using Streamlit, the dashboard includes:
- 📦 KPI metrics (Demand, Stock, Gap, Decision)
- 📈 Real-time analytics
- 📊 Interactive charts (Plotly)
- ⚡ Risk score & system health
- 🐍 Python
- 📊 Streamlit
- 🧮 Pandas & NumPy
- 📈 Plotly
- 💰 Reduced inventory costs
- 📊 Improved demand forecasting
- ♻️ Minimized wastage
- ⚡ Faster decision-making
- 📦 Optimized supply chain
- 🔗 Real-time data integration
- 🤖 Advanced machine learning models
- ☁️ Cloud deployment
- 🏭 Multi-warehouse optimization
git clone https://github.com/Mousoom07/Smart-Inventory-AI-System.git
cd Smart-Inventory-AI-System
pip install -r requirements.txt
python streamlit run dashboard/app.py