A quantitative analysis: Assessment of data-driven decisions in CNC machining

A rapidly evolving field of manufacturing is real and data-driven decision-making has become a pivotal element in optimizing Computer Numerical Control (CNC) machining processes. This paper evaluates the effectiveness of employing a Likert scale to assess the impact of data-driven decisions in CNC machining environments. The key performance indicators (KPIs) such as precision, efficiency, user satisfaction and cost-effectiveness were focal points. The research aims to evaluate the impact of data analysis on decision-making processes in CNC machining, exploring factors such as accuracy, efficiency, and productivity. The results indicate a significant positive correlation between data-driven decisions and improved CNC machining outcomes. This paper investigated how subjective assessments from industry professionals can provide actionable insights. This study combines quantitative data with qualitative feedback to offer a comprehensive view of the effectiveness of data-driven strategies in CNC machining. However, the results suggested that data-driven decisions in CNC machining are perceived positively by industry professionals, with significant improvements in key performance areas.