THE ROLE OF EFQM AND BUSINESS MODELS IN ENHANCING QUALITY PERFORMANCE: A MACHINE LEARNING APPROACH

Abstract

The EFQM model plays a key role in monitoring the performance of small and medium-sized enterprises (SMEs), providing a comprehensive framework for assessing organizational excellence. Equally important are business models, which shape the strategic direction and operational efficiency of these enterprises. This study examines the impact of aligning the EFQM model with business models on quality performance in small and medium-sized enterprises (SMEs) in Serbia. A case study was conducted using real data from 20 enterprises that applied for the Quality Oscar, and artificial neural networks (ANNs) were used to analyze the complex interdependencies between business excellence, business model innovation, and quality performance. The results show that a 5% increase in the integration of the EFQM model with the business model significantly improves key quality performance indicators. Considering the business uncertainty and limited resources of SMEs, improvements plateau after a 20% increase in alignment, suggesting an optimal threshold for strategic implementation. These findings highlight the value of structured frameworks for business excellence in improving operational performance, while also demonstrating the effectiveness of machine learning in optimizing decision-making processes. The study provides valuable insights for SMEs aiming to improve quality performance through targeted alignment of the EFQM model and their own business model.