ATLAS is a Next.js-based intelligent inventory management system designed to help teams track items, manage stock, analyze historical changes, and run advanced simulations to optimize inventory strategies.
The entire project is built with a scalable architecture, optimized for SEO, and uses AVIF images for maximum performance.
- Smart inventory intake, updates, and consumption
- Simulation engine to test stock strategies
- Full item history timeline
- Inventory diff engine for displaying changes
- User settings & preferences
- Responsive, fast, and SEO-ready
- Built on Next.js App Router + Server Actions
| Algorithm | Purpose | Description |
|---|---|---|
| Stock Level Tracking | Real-time stock state | Tracks additions/removals using incremental counters. |
| Demand Forecasting (EMA) | Predict future consumption | Uses Exponential Moving Average to estimate future depletion rates. |
| Restock Suggestion Logic | Automated advice | Suggests reorder quantities by combining safety stock + predicted demand. |
| Simulation Engine | Strategy testing | Runs multiple simulated cycles with various parameters to compare outcomes. |
| History Diff Engine | Change tracking | Generates field-level diffs between old and new item states. |
| Low-Stock Alerts | Warning system | Triggers notifications when thresholds are crossed. |
/app
/home
/inventory
add/
manage/
take/
/simulation
/history
/settings
/components
/lib
/public/screenshots
| Home | History |
|---|---|
![]() |
![]() |
| Add Inventory | Manage Inventory |
|---|---|
![]() |
![]() |
| Take Inventory | Settings |
|---|---|
![]() |
![]() |
| 3D Simulation |
|---|
![]() |
git clone https://github.com/mohaneddz/Smart-Warehouse-Management-System
cd ATLAS
pnpm install
pnpm run dev
The simulation module allows users to test restock strategies by generating multiple virtual cycles.
Each cycle evaluates:
- Starting stock
- Daily usage pattern
- Forecasted usage (EMA)
- Safety stock
- Restock quantity
- Total duration until stockout
The system compares scenarios and highlights the most optimal strategy.
This project is released under the MIT License.






