Machine Learning Integration in DEA Models: Current Developments and Future Challenges - Hengki Tamando Sihotang; Fristi Riandari; Rasenda Rasenda; Wildan Alrasyid (Institute of Computer Science (IOCS), 2026)
The increasing availability of large and complex datasets has created new opportunities for enhancing Data Envelopment Analysis (DEA) through the integration of Machine Learning (ML) techniques. This study reviews current developments in the integration of ML and DEA models and identifies key challenges, trends, and future research opportunities. A systematic literature review was conducted by examining recent studies that combine DEA with various machine learning algorithms across multiple application domains, including healthcare, banking and finance, manufacturing, supply chain management, energy, agriculture, and higher education. The findings indicate that Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests, Gradient Boosting methods, and Deep Learning models are among the most frequently employed techniques in DEA-ML frameworks. Despite these advantages, several challenges remain, including data quality issues, model interpretability, computational complexity, limited generalizability, and the lack of standardized integration frameworks. The review concludes that the integration of ML and DEA offers substantial potential for advancing efficiency analysis and organizational performance evaluation. Future research should focus on developing explainable artificial intelligence (XAI) solutions, real-time efficiency analytics, federated learning approaches, and standardized hybrid DEA-ML frameworks to improve transparency, scalability, and practical applicability across diverse operational environments.