Sai Soorya Sulur Venkateswaran

Motivated and detail-oriented Industrial Engineer with a strong foundation in process improvement, continuous improvement, and data analytics. Skilled in applying engineering principles to optimize efficiency and streamline operations. Eager to leverage academic knowledge and problem-solving skills to contribute to dynamic teams and drive operational excellence. Seeking an opportunity to kickstart a career in a forward-thinking organization where I can apply my passion for data-driven decision-making and continuous improvement methodologies to enhance productivity and performance.

• Certified in SAP – S/4 HANA – Sourcing and Procurement
• Lean Six Sigma Green Belt

Cycle Time Reduction in Production Process at a Foundry for Fully-Finished Castings and Forgings                                            Feb 2023 - May 2023                                                                                                                                  
Assisted a project focused on reducing cycle time in a foundry that supplies fully-finished castings and forgings. Conducted time-motion studies and identified bottlenecks. Implemented Lean techniques, including SMED, 5S, and process optimization to improve mold setup, casting/forging, and finishing operations.

Results:

• Achieved 18% cycle time reduction.
• Increased throughput by 15%.
• Reduced non-value-adding activities by 22%.
• Reduced mold setup times by 45%.

Design and Development of Drowsiness detection alarm,                                                                                                                   Nov 2022 - Dec 2022

To design and develop a low cost and efficient drowsiness detection alarm which detects eye closure and alerts the driver through buzzer., Performed Failure Mode Effect Analysis and Quality Function Deployment., 

• Achieved 95% accuracy in detecting drowsiness during controlled tests.
• Reduced false positives by 30% through algorithm calibration.
• Delivered a cost-efficient solution with a total project cost of under $85.
• Conducted successful field trials, demonstrating system reliability in real-world driving conditions.

Tools Utilized: Python, buzzer, web cam, battery, Raspberry Pi. 

Power systems fault prediction using Machine Learning,                                                                                                                     Oct 2022 - Dec 2022 

To develop a machine learning models to predict the faults from the given dataset of power system., Used various Machine Learning models which enhances the quality and safety of the electrical power system.

• Achieved 94% accuracy in fault prediction.
• Reduced false positives by 20%, improving prediction reliability.
• Created real-time, actionable insights through Tableau dashboards for faster decision-making.

Tools Utilized: Python, Tableau. 

Micro-grid design with Photovoltaics and Vehicle to grid connectivity                                                                                             Nov 2020 - May 2021 

Designed and simulated a connected microgrid containing photovoltaic system, battery storage and Vehicle to Grid connectivity with a simple peak shaving algorithm., Implemented the system to work as a backup power source during power outages in the grid.

• Reduced grid dependency by 20-30% through efficient energy management.
• Ensured continuous power during outages with 100% uptime for critical loads.
• Achieved efficient energy utilization with a cost-effective design.

Tools Utilized: MATLAB.