"ENHANCING QUALITY CIRCLE EFFECTIVENESS: LEVERAGING AI-DRIVEN EMPLOYEE PERFORMANCE ANALYTICS"

Abstract

Increasing the efficiency of Quality Circles (QCs) in businesses is the focus of this study, which investigates the possibility of using AI-driven employee performance metrics. Manual performance evaluations and subjective feedback have always been the foundation of Quality Circles, which are collaborative groups with the goal of achieving continuous improvement. The purpose of this research is to examine how advanced analytics, which are driven by artificial intelligence, may give more accurate and actionable insights regarding employee performance, which in turn can lead to improved quality control results. Data were gathered from a manufacturing business of a medium size over the course of six months using a mixed-methods approach. Artificial intelligence capabilities such as machine learning algorithms and natural language processing were brought into play during this time. According to the data, there was a twenty percent rise in the efficiency of quality control, which was shown by the increased rate of proposals being implemented and the enhanced level of employee satisfaction. Analytics powered by artificial intelligence were able to identify performance patterns and the underlying causes of problems that had been neglected in the past. This made it possible for quality control personnel to solve problems in a more focused and efficient manner. It is recommended that businesses who want to attain better performance and continuous improvement use AI tools since this study illustrates the potential of AI technologies to revolutionize conventional quality control techniques.