Machine Learning Integration in DEA Models: Current Developments and Future Challenges
Keywords:
Data Envelopment Analysis (DEA), Machine Learning (ML), Efficiency Analysis, Artificial Intelligence (AI), Decision Support Systems (DSS)Abstract
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.
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References
Alkhayrat, M., Aljnidi, M., & Aljoumaa, K. (2020). A comparative dimensionality reduction study in telecom customer segmentation using deep learning and PCA. Journal of Big Data, 7(1), 9.
Barr, R. S. (2004). DEA software tools and technology: A state-of-the-art survey. In Handbook on data envelopment analysis (pp. 539–566). Springer.
Brazier, J. (2008). PMC15 in or Out? Empirical Evidence on Income Losses in Health State Valuations and Implications for Economic Evaluations. Value in Health.
Bughin, J., Hazan, E., Sree Ramaswamy, P., DC, W., & Chu, M. (2017). Artificial intelligence the next digital frontier.
Charles, V., Gherman, T., & Zhu, J. (2021). Data envelopment analysis and big data: A systematic literature review with bibliometric analysis. Data-Enabled Analytics, 1–29.
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 785–794.
Cooper, W. W., Seiford, L. M., Tone, K., & Zhu, J. (2007). Some models and measures for evaluating performances with DEA: past accomplishments and future prospects. Journal of Productivity Analysis, 28(3), 151–163.
de Sousa, M. da C. S., Cribari-Neto, F., & Stosic, B. D. (2005). Explaining DEA technical efficiency scores in an outlier corrected environment: the case of public services in Brazilian municipalities. Brazilian Review of Econometrics, 25(2), 287–313.
Deng, L. (2014). A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transactions on Signal and Information Processing, 3, e2.
Emrouznejad, A., & Yang, G. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4–8.
Göckenjan, G., Nunner?Winkler, G., Iser, M., Wittwer, H., Schnädelbach, H., Reemtsma, J. P., Soeffner, H., Spittler, G., Bruchhausen, W., & Baranzke, H. (2009). Begriffe: Homo faber/Technik. Handbuch Anthropologie.
Gunasekaran, A., & Kobu, B. (2007). Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications. International Journal of Production Research, 45(12), 2819–2840.
Jacobides, M. G., Brusoni, S., & Candelon, F. (2021). The evolutionary dynamics of the artificial intelligence ecosystem. Strategy Science, 6(4), 412–435.
Jomthanachai, S., Wong, W.-P., & Lim, C.-P. (2021). An application of data envelopment analysis and machine learning approach to risk management. Ieee Access, 9, 85978–85994.
Kassambara, A. (2017). Practical guide to cluster analysis in R: Unsupervised machine learning (Vol. 1). Sthda.
Mosavi, A., Salimi, M., Faizollahzadeh Ardabili, S., Rabczuk, T., Shamshirband, S., & Varkonyi-Koczy, A. R. (2019). State of the art of machine learning models in energy systems, a systematic review. Energies, 12(7), 1301.
Motwani, M., Dey, D., Berman, D. S., Germano, G., Achenbach, S., Al-Mallah, M. H., Andreini, D., Budoff, M. J., Cademartiri, F., & Callister, T. Q. (2017). Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. European Heart Journal, 38(7), 500–507.
Nataraja, N. R., & Johnson, A. L. (2011). Guidelines for using variable selection techniques in data envelopment analysis. European Journal of Operational Research, 215(3), 662–669.
Nguyen, N. M. (2015). Restructuring an emerging market: The case of Vietnam. Golden Gate University.
O’Mara-Eves, A., Thomas, J., McNaught, J., Miwa, M., & Ananiadou, S. (2015). Using text mining for study identification in systematic reviews: a systematic review of current approaches. Systematic Reviews, 4(1), 5.
Onwuegbuzie, A. J., & Weinbaum, R. (2017). A framework for using qualitative comparative analysis for the review of the literature. The Qualitative Report, 22(2), 359–372.
Parmar, A., Katariya, R., & Patel, V. (2018). A review on random forest: An ensemble classifier. International Conference on Intelligent Data Communication Technologies and Internet of Things, 758–763.
Wiese, L., Pohlmeyer, A. E., & Hekkert, P. (2020). Design for sustained wellbeing through positive activities—a multi-stage framework. Multimodal Technologies and Interaction, 4(4), 71.
Yukhymenko, M. A., Brown, S. W., Lawless, K. A., Brodowinska, K., & Mullin, G. (2014). Thematic analysis of teacher instructional practices and student responses in middle school classrooms with problem-based learning environment. Global Education Review, 1(3), 93–110.
Zhu, N., Zhu, C., & Emrouznejad, A. (2021). A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies. Journal of Management Science and Engineering, 6(4), 435–448.
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Copyright (c) 2026 Hengki Tamando Sihotang, Fristi Riandari, Rasenda Rasenda, Wildan Alrasyid
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