Introduction A couple of weeks ago, I built a full-stack mobile and AI-powered supply chain optimization app designed to simulate the real world. The goal of the project was to build an intelligent system that combines: The platform built with Why I Built This Project One of the most interesting parts of this project has been bridging mobile engineering with AI engineering. Instead of building isolated ML notebooks, I wanted to build a system where AI was integrated directly into a real application. A system where users can interact with predictions and analytics, get real live operational insights all within the mobile app. The backend follows a modular structure:app/ app/ ├── models/ ├── api/ ├── schemas/ ├── services/ ├── analytics/ ├── core/ ├── db/ ├── utils/ └── scripts/ Database Design The system currently contains three major entities: Shipments class ShipmentCreate(BaseModel): product_name: str origin: str destination: str status: str distance_km: float expected_delivery_days: int actual_delivery_days: int
warehouse_id: Optional[int] = None
driver_id: Optional[int] = None
Warehouses class WarehouseCreate(BaseModel): name: str city: str Drivers class DriverCreate(BaseModel): name: str truck_number: str These relationships allowed me to model a simplified logistics network. Building the Analytics Layer This was where the project started transitioning from a CRUD application into an AI-powered system. I used Pandas to convert logistics data into analytics-ready DataFrames. Example: shipments_df = pd.read_sql( "SELECT * FROM shipments", engine ) Shipment Analytics Some of the analytics currently implemented include: Shipment Status Analysis shipments_df["status"].value_counts() Delivery Performance shipments_df[ "expected_delivery_days" ].mean() Route Analysis shipments_df.groupby(
["origin", "destination"]
).size().sort_values(ascending=False).head()
result = [{
"hospital": hospital,
"state": state,
"count": count
}for (hospital, state), count in delayed_routes.items()
]
shipments_df.groupby(
["destination"]
).size().sort_values(ascending=False).head()
Product Analysis
result = (
df["product_name"]
.value_counts(normalize=True)
.mul(100)
.round(2)
).head()
These analysis were exposed through FastAPI APIs and consumed directly by the React Native app. Seeding Large Logistics Datasets To simulate real logistics operations, I generated synthetic shipment data using Faker and Mockaroo. This allowed me to create: This was important because AI models require larger datasets for meaningful analysis. Transitioning Into AI The most exciting part of the project was the machine learning roadmap. Route Optimization Identify more efficient logistics routes. Warehouse Efficiency Scoring Analyze warehouse performance and congestion. Demand Forecasting Predict future shipment volumes. Anomaly Detection Detect unusual shipment behaviors. Final Thoughts This project taught me how backend systems and AI systems interact. What started as a logistics CRUD system evolved into something much bigger. I've been able to build a project that reflects both software engineering and AI engineering workflows. Links to the project can be found here : mobile, backend.