Aditya Shah

Aditya Manojkumar Shah is a meticulous and astute manufacturing engineer with a proven track record of optimizing processes, analyzing data and driving operational efficiency. He is proficient in MySQL, Python, Tableau, Power BI, Minitab and R-Programming. Shah also possesses adeptness in lean methodologies, statistical process control and root cause analysis. Demonstrating a demonstrated prowess in identifying areas for improvement, he excels in developing data-driven solutions and achieving substantial abatements in setbacks.

Shah’s skill set extends across manufacturing engineering, process improvement, quality control, project management, production planning, and inventory management. Committed to delivering analytical insights and enhancing productivity, he is dedicated to contributing to organizational success.

Motivated by a desire to continuously expand his knowledge and skills, Shah pursued an IMSE degree to gain deeper insights from diverse courses and to benefit from the flexibility it offers in his professional endeavors. Having completed his Co-Ops at Tesla, he has honed his practical experience in applying his expertise to real-world challenges within the manufacturing industry.

Program

On-campus

Internship Details

During his internships at Tesla, Shah gained valuable experience in process engineering, contributing significantly to optimizing manufacturing operations and achieving substantial cost savings. In his role, he was involved in various projects aimed at enhancing efficiency, reducing cycle times, and improving overall equipment effectiveness (OEE).

At Tesla’s California facility, Aditya focused on implementing Statistical Process Control (SPC) and Out-of-Control (OOC) alerts, which led to early detection of supplier fastener quality issues. Additionally, he identified process bottlenecks, conducted tool utilization analysis, and redesigned process flows to minimize task dependency, ultimately enhancing OEE metrics.

Utilizing the DMAIC methodology, Shah conducted comprehensive analyses incorporating techniques such as Ishikawa diagrams, three-legged 5-why analysis, Measurement System Analysis (MSA), and Process Capability Analysis (Cp, CPK). These efforts resulted in a reduction in equipment false pass rates and cost savings. He also conducted Value Stream Map analysis, identifying and eliminating non-value-adding time, leading to a reduction in Non-Value-Adding Time through station relocation and process flow optimization.

During his internship at Tesla’s Nevada facility, Shah utilized MySQL for data analysis and Minitab for process capability calibration, achieving a reduction in false failures, an increase in equipment yield, and cost savings. Collaborating with the quality team, he conducted fault injection and equipment evaluations, resulting in improvement in reliability and fault detection. By employing Root Cause 5-Why analysis and comparative data analysis, he achieved a notable reduction in non-conformance instances. Furthermore, Shah optimized testing equipment limits and parameters, conducted sensitivity analyses, and achieved significant cycle time reductions, resulting in substantial cost savings. Leveraging dynamic models, he enhanced testing accuracy, reduced cycle times, and substantial cost savings.

Overall, Shah’s internships at Tesla equipped him with hands-on experience in process engineering, data analysis, and problem-solving, enabling him to drive operational improvements and cost savings through innovative solutions and methodical approaches.

Education