Atlas
This project was initiated to solve specific technical and domain challenges. Analysing and optimising logistics data to predict optimal stocking strategies and designing interactive 3D simulations.
About This Project
Analysing and optimising logistics data to predict optimal stocking strategies and designing interactive 3D simulations.
Processed raw warehouse logs using Pandas, deployed predictive clustering models to group inventory logically, built a diff engine and simulation engine using Next.js and Three.js, and provided a responsive dashboard to manage stock.
Provided actionable analytics that reduced retrieval time in simulated warehouse conditions.
Data Engineer
2025
Public
University
Technology Stack
Project Story
Analysing and optimising logistics data to predict optimal stocking strategies and designing interactive 3D simulations.
Processed raw warehouse logs using Pandas, deployed predictive clustering models to group inventory logically, built a diff engine and simulation engine using Next.js and Three.js, and provided a responsive dashboard to manage stock.
Provided actionable analytics that reduced retrieval time in simulated warehouse conditions.
Insights & Takeaways
Highlights
- Case study content natively baked into the project dataset.
- Clear storytelling built around the specific problems faced and the technologies used.
Challenges
- Strict focus on performance and maintainability.
- Selecting standard tools to ensure scalability: Next.js, React, TypeScript, Tailwind CSS, Tauri, Python, Flask, Three.js, PostgreSQL
- Processed raw warehouse logs using Pandas, deployed predictive clustering models to group inventory logically, built a diff engine and simulation engine using Next.js and Three.js, and provided a responsive dashboard to manage stock.
Lessons Learned
- Provided actionable analytics that reduced retrieval time in simulated warehouse conditions.
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