Inspiration
Scheduling employee shifts is often chaotic — managers struggle to balance workloads, employee preferences, and last-minute changes. We wanted to build something that uses AI intelligence to remove manual pain, improve fairness, and optimize productivity.
What it does
ShiftSmart automatically creates optimal shift schedules using AI. It analyzes data like workload, employee skills, attendance, and preferences to assign shifts fairly and efficiently. It also reschedules automatically when someone calls in sick, ensuring smooth operations.
How we built it
We used Spring Boot for the backend API, React.js for the frontend, and PostgreSQL as the database. AI scheduling logic was implemented in Python using scikit-learn for prediction models and pandas for data processing. We containerized the app with Docker and deployed it on AWS EC2 for scalability.
Challenges we ran into
Integrating AI models with real-time scheduling data
Ensuring fairness and avoiding employee bias in algorithm design
Handling large datasets efficiently under time constraints
Accomplishments that we're proud of
What we learned
We learned how to combine AI, backend logic, and UI/UX seamlessly to create a real-world productivity solution. We also learned the value of collaboration, version control, and quick iteration under a hackathon timeline.
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