• Optimized SAP MM processes by conducting a thorough analysis of P2P and S2P processes, resulting in a 20% increase in efficiency.• Implemented new inventory management strategies for elevator assembly parts, leading to a 25% reduction in holding funds and improved cash flow.• Spearheaded RFQ process improvements by implementing supplier competitiveness metrics, resulting in cost savings of $50,000 on RFQ items and increasing supplier response rate by 40%.• Exposure to SAP full life cycle implementation, including requirement collection and gap analysis.

• Developed and implemented a Convolutional Neural Network (CNN) model to predict fluid flow velocity field with an accuracy of 94%, surpassing the previous state-of-the-art model by 11%.• Analyzed and processed a dataset of 10,000 fluid flow samples of pressure coefficients and velocity field which was generated by the spatial modes of a fluid flow compiled by the Ifort Compiler.

• Collaborated with a team of researchers to work on the "Deep Learning Binary-Classification Model for Casting Products Inspection" project, which published in the prestigious IEEE Xplore (doi: 10.1109/PARC52418.2022.9726590).• Implemented a cutting-edge deep learning model for inspecting casting products, resulting in a 95% accuracy rate, surpassing the industry standard by 13%.• Assisted in scaling up the deep learning model to be deployed across multiple production lines, leading to a 20% increase in efficiency and cost savings.