From KPI Dashboards to Analytics-Enhanced Financial Performance Evaluation: A Firm-Level Analysis of Italian Machinery and Equipment Firms

The aim of this thesis is to explore and analyze the integration of advanced data analytics at the firm level and whether its use, in addition to traditional KPI-based analysis, improves the explanatory capacity of financial performance analysis. The empirical research is based on financial data from the Orbis database for large and very large firms in Italy operating in the manufacture of machinery and equipment sector (NACE Rev. 2 code 28) during 2019–2023. The thesis consists of three analytical stages. First, a KPI-based dashboard and descriptive statistics were used to assess firm performance. Second, regression-based driver analysis was conducted to examine financial factors associated with profitability. The results show that asset turnover, current ratio, revenue growth, and firm size are positively associated with net profit margin. Third, clustering-based segmentation identified two distinct firm profiles: a “stronger financial profile” and a “weaker financial profile.” Overall, the findings indicate that data analytics can enhance traditional KPI-based monitoring by providing greater explanatory and decision-support value.