Electric vehicle (EV) manufacturing requires exceptional precision, quality, and constant process improvement, and the automotive industry necessitates it. The exploitation of Poka Yoke (mistake proofing) in rotor production by Tesla Inc., a global leader in EVs, is an illustration. This paper uses Poka Yoke techniques to reduce human and mechanical errors in the Tesla rotor manufacturing process in high-precision environments. Strategically integrating Poka-Yoke principles across Tesla’s rotor production stages such as material handling, machining, balancing, and inspection, we introduce two sensor-based inspecting solutions of sensor-based inspections and active dynamic gauging, two error-proofing solutions of error-proofing fixtures and color-coded use tools, one solution of automated guided vehicles (AGV) and lastly case study of how made the parts to fit process was transformed into an error proofing process. Proactively finding deviations, suggesting and guiding operator actions, and preventing assembly errors increase overall quality and safety and improve the operational efficiency of these systems. The study also brings forward the integration of IoT and data analytics in real-time monitoring, predictive maintenance, automated decision-making, and further strengthening error prevention mechanisms. Despite high initial investment, system complexity, and training, there are benefits of reduced defects, improved resource utilization, and higher workplace safety that exceed the cost. Poka Yoke also supports Tesla’s Lighthouse Goals of minimizing waste and optimizing product lifecycle performance. This study conclusion with best practices and suggestions for using technology-driven, scalable Poka-Yoke systems in high-precision manufacturing. Tesla’s case shows how mistake-proofing has become a friendly means to drive rigor in production, innovation, operational excellence, and sustainable manufacturing in the dynamic automotive industry.

Poka-Yoke, Tesla, Rotor Production, Lean Manufacturing, Error Prevention

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