Integrating Relative Efficiency Models with Machine Learning Algorithms for Performance Prediction

• Published in: SAGE open 2024-06, Vol.14 (2)
• Main Authors: Perroni, Marcos Gonçalves, Veiga, Claudimar Pereira da, Forteski, Elaine, Marconatto, Diego Antonio Bittencourt, da Silva, Wesley Vieira, Senff, Carlos Otávio, Su, Zhaohui
• Format: Article
• Language: English
• Subjects: Algorithms; Data envelopment analysis; Efficiency; Machine learning; Support vector machines
• ISSN: 2158-2440; 2158-2440
• DOI: 10.1177/21582440241257800

Predicting operational performance enables organizations to develop operational effectiveness goals considering different combinations of resources. Measuring performance is consolidated with advances in relative efficiency analysis techniques, including data envelopment analysis (DEA) and stochastic frontier analysis (SFA), albeit these methods lack predictive capability. This paper proposes an approach for performance prediction by integrating relative efficiency measurement models with machine learning algorithms. Data analyses were conducted using data provided by the energy assessment project offered to small and medium-sized manufacturing companies in the United States (n 7,548) using sales as the output, with the inputs being the number of employees, hours of operation, electricity, natural gas, cost of electricity, and cost of natural gas. Performance was estimated differently, employing parametric (SFA) and non-parametric (DEA) methods. The prediction benchmarking process occurred by adopting machine learning algorithms: regression (LM), support vector machine (SVM), K-nearest neighbor (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT). The findings showed that it is possible to identify the best prediction algorithm associated with a performance model. However, the performance prediction may differ if different strategies for measuring performance or machine learning model configurations are used. In addition, SFA-LOG and SVM had the best performance for regression, and DEA-VRS/IRS excelled with random forest; the RF algorithm was the best fit across all performance approaches. The error rate depends on the algorithm and the performance model, and the number of classes must be reduced to obtain a higher success rate. Plain language summary Predicting operational performance enables organizations to develop operational effectiveness goals by considering different combinations of resources. Companies that incorporate more accurate forecast models into their strategies can have a competitive advantage in the market, mainly by delivering the product to the right place, in the exact quantity, and at the right price. Given this context, there is a need to test new models and their combinations to achieve better results. This paper proposes an approach for performance prediction by integrating relative efficiency measurement models with machine learning algorithms. To test the prediction models, data analyses were cond