Machine Learning for KPI Development in Public Administration

Efficient and effective service delivery to citizens in Public Administrations (PA) requires the use of key performance indicators (KPIs) for performance evaluation and measurement. This paper proposes an innovative framework for constructing KPIs in performance evaluation systems using Random Forest and variable importance analysis. Our approach aims to identify the variables that have a strong impact on the performance of PAs. This identification enables a deeper understanding of the factors that are critical for organizational performance. By analyzing the importance of variables and consulting domain experts, relevant KPIs can be developed. This ensures improvement strategies focus on critical aspects linked to performance. The framework provides a continuous monitoring flow for KPIs and a set of phases for adapting KPIs in response to changing administrative dynamics. The objective of this study is to enhance the performance of PAs by applying machine learning techniques to achieve a more agile and results-oriented PAs.