Integrating Analytics for Machine Losses in Stator & Rotor Production at ABC Corporation

Abstract

This study integrates advanced analytics to minimize machine losses in stator and rotor production at xyz. Leveraging IoT sensors and predictive machine learning models, the project identifies root causes of inefficiencies (e.g., unplanned downtime, stator overheating, and rotor imbalance) and implements data-driven strategies. Key results include a 20–30% reduction in unplanned downtime, ₹4.2 lakh/month cost savings, and improved OEE metrics. The framework demonstrates scalability for automotive manufacturing, emphasizing predictive maintenance, cross-departmental collaboration, and employee training. Challenges such as data silos, cyber security risks, and cultural resistance are addressed through structured solutions.