
During December 2024, Rajesh developed end-to-end 3D container loading and cargo visualization features for the Sreedhar1729/D repository, focusing on logistics planning and operational efficiency. He implemented dynamic truck tiling, stacking, and collision logic using JavaScript and Three.js, enhancing the data model to support accurate material calculations. Rajesh also built a responsive UI with charts for cargo volume, weight, and capacity, leveraging SAPUI5 for data visualization. In the SAPviswam/rf_app repository, he integrated Twilio-based SMS notifications for supervisor password approvals. His work demonstrated depth in 3D rendering, data modeling, and front-end development, addressing complex visualization and notification requirements.

December 2024 delivered end-to-end visualization and notification capabilities to support logistics planning and operational efficiency. Key work spanned two repositories, delivering (1) a unified 3D container loading and visualization feature with truck-type tiling, stacking/collision logic, 3D rendering, and enhanced data models for material data and calculations; (2) cargo visualization UI with charts showing volume, weight, and remaining capacity, including pie charts and responsive sizing; and (3) SMS-based supervisor notifications for password update approvals via Twilio. The month also included data-model cleanup to remove gaps and improve calculation accuracy, and charting refinements to support faster, data-driven decisions.
December 2024 delivered end-to-end visualization and notification capabilities to support logistics planning and operational efficiency. Key work spanned two repositories, delivering (1) a unified 3D container loading and visualization feature with truck-type tiling, stacking/collision logic, 3D rendering, and enhanced data models for material data and calculations; (2) cargo visualization UI with charts showing volume, weight, and remaining capacity, including pie charts and responsive sizing; and (3) SMS-based supervisor notifications for password update approvals via Twilio. The month also included data-model cleanup to remove gaps and improve calculation accuracy, and charting refinements to support faster, data-driven decisions.
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