A Machine Learning-Driven Approach to Performance Management Systems

Abstract: Performance Management Systems (PMS) are vital for organizations as they help in the systematic collection of pre-processing, processing, and analyzing data regarding performance to evaluate and improve it based on specific objectives. This paper analyzes PMS frameworks, such as the Integrated Technological Approach, the Balanced Scorecard (BSC) and Organizational Information Processing Theory (OIPT), and look into the involvement of data and famous machine learning algorithms, like logistic regression, and decision trees, in performing effective measurements and management of performance. The study shows how organizations can use predictive insights from internal and external performance metrics by applying machine learning models to optimize organizational decision-making. In healthcare applications, data-driven approaches in the EFQM and SERVQUAL are thus applied in which integrating internal process data with external service quality metrics drives operational excellence. The study concludes in support of integrating machine learning and data analytics into PMS frameworks to ensure strategic alignment and improve decision-making across diverse applications. Keywords: Performance Management System, Data Analysis, Machine Learning, Data Analytics, Decision-Making Systems, Management Data