SupplyAI

Inspiration:

Global supply chains are constantly affected by weather, transportation delays, rising costs, and unexpected disruptions. We were inspired to build SupplyAI because companies often have lots of shipment data but still need a fast way to understand what is at risk and what action to take next. We wanted to create a control tower that turns complex logistics information into clear decisions.

What it does:

SupplyAI is an intelligent supply-chain risk and decision platform. A user enters a shipment’s origin, destination, cargo, and transportation method. SupplyAI then analyzes the shipment and displays its estimated route, ETA, cost, and overall risk. It also incorporates live weather data across the shipment corridor, breaks down major risk factors, and compares alternative transportation options. Users can simulate a disruption and see how the shipment’s risk, ETA, and cost change. SupplyAI then re-ranks alternative options to recommend a better response. How we built it We built SupplyAI using Next.js, React, TypeScript, Tailwind CSS, and JSON. We integrated external APIs for geocoding, routing, mapping, and live weather data. Our system combines real-world environmental data with our own logistics models to estimate distance, transportation time, cost, and risk. We also created an interactive map that changes based on the selected transportation option and a dedicated Risk Intelligence view for understanding why a shipment may be vulnerable.

Challenges we ran into:

One of our biggest challenges was combining multiple types of information into one reliable system. Routing, weather, transportation modes, and risk calculations all return different types of data. We also had to solve technical problems involving map rendering, API responses, multimodal routes, weather checkpoints, and keeping the dashboard synchronized when users selected alternative routes or simulated disruptions. Accomplishments that we're proud of We're proud that SupplyAI became more than a static dashboard. Users can actually interact with a shipment, switch between Route and Risk views, compare alternatives, and simulate what happens when conditions change. We're especially proud of the disruption simulation and recommendation system, because it demonstrates the main idea behind SupplyAI: not simply identifying a problem, but helping the user decide what to do next.

What we learned:

We learned how important it is to connect software engineering with a real-world problem. We gained experience working with APIs, React state, TypeScript, interactive maps, live data, risk modeling, and deployment. We also learned that good decision-support software needs to explain its recommendations. A risk score is much more useful when users can understand what caused the risk and what alternatives they have. What's next for SupplyAI Our next step would be connecting SupplyAI to real enterprise shipment and carrier data so it could monitor active shipments continuously. We would also like to incorporate live traffic, port congestion, geopolitical events, severe-weather forecasts, inventory impact, and historical disruption data. Long term, SupplyAI could become a predictive supply-chain control tower that detects emerging disruptions early and recommends actions before they significantly affect a business.

Built With

• chatgpt
• css
• github
• html
• json
• netlify
• react
• typescript
• visual-studio