Modern Research in Decision Making

Providing a combined model of data envelopment analysis and artificial neural network to ranking the efficiency of pharmaceutical companies

Document Type : Original Article

Authors

Mostafa Ebrahimpour Azbari 1
Aida Fallahpoor Mobaraki 2

1 Associate Professor, Department of Management, Faculty of Management and Economics, University of Guilan, Gilan, Iran

2 Ph.D. student of Production and Operations Management, Department of Management, Faculty of Management and Economics, University of Guilan, Gilan, Iran

Abstract
One of the models that is used to measure the efficiency and decision support is DEA, but considering all the merits, it also has its own limitations, which has led to the idea of combining it with artificial neural networks. ANN is increasingly used in model and data-based approaches to enrich analytical and predictive capabilities and thus improve decision-making. The present research presents a model for evaluating the efficiency of units by integrating neural networks. The studied sector is the active pharmaceutical industry in the Tehran stock market. To create the model, the efficiency of 4 DEA models on a variable scale, including input-oriented and output-oriented BCC model, SBM model and RAM model during the years 2018 to 2022 was calculated in GAMS. The efficiency values of these four models were ANN'S education vector. Also cost, income and profit data from 2018 to 2022 was entered into MATLAB as ANN'S input. To generalize education, the data of 2023 was used. The results showed that the trained efficiency boundary shows a more comprehensive and accurate approximation of efficiency for the ranking of pharmaceutical companies. The results of this research will help pharmaceutical companies in the fields of investment, resource allocation, predicting the results of policies and planning.

Keywords

Data Envelopment Analysis
Artificial Neural Network
Firm performance
efficiency
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