Estimating and improving the efficiency of decision-making units with interval data

Subject Areas : International Journal of Data Envelopment Analysis

Farshad Hosseinzadehlotfi ¹ , Tofigh Allahviranloo ² , Bijan Rahmaniperchkolaei ³

1 -
2 -
3 - گروه مدیریت تهران مرکز

Received: 2024-12-13 Accepted : 2024-12-15 Published : 2024-10-01

Keywords: Data envelopment analysis, interval data, Estimate, Bank Branch.,

Abstract :

Performance measurement is always considered one of the most important tasks of managers. Hence, management knowledge is measurement knowledge and if we cannot measure something, we certainly cannot control it and consequently we cannot manage it. In this paper, we examine data envelopment analysis models for improving inefficient units. In this study, 20 bank branches in Tehran were selected and mathematical models were presented for estimating inputs with interval data.

 

The findings of this research highlight the importance of integrating advanced analytical tools like DEA into management practices. By quantifying inefficiencies and offering clear pathways for improvement, DEA empowers managers to make data-driven decisions that enhance overall performance. This approach is particularly valuable in competitive environments, such as the banking sector, where efficiency and service quality directly impact customer satisfaction and profitability.

 

References:

[1] Farrell, M. "The Measurement of productive Efficiency". Journal of Research Statistics society 20 253-290. (1957).
[2] Charnes, W. Cooper, W. Rhodes, E." Measuring the efficiency of decision-making units". European Journal of Operational Research 120.429-444. (1978).
[3] O'Neal. P, Ozcan. Y, Yangiang. M, "Benchmarking mechanical Ventilation Services in teaching hospitals". Journal of medical Systems 26 .227-240. (2002).
[4] Simar. L, Wilson. P, "Statical inference in non-parametric frontier models. The state of art Journal of productivity Analysis 13. 49-78. (2000).
[5] Cooper. W, Seiford. L, Tone. K, "Data Envelopment Analysis. A Comprehensive text with models. application. references and DEA-solver software". Journal of kluwer academic publishers.p.253. (1999).
[6] Kuosmanen. T, "Modeling blank data entries in Data Envelopment Analysis". Journal of Econometrics working paper Archive at wustl. (2002).
[7] Kao. C, Liu. S, " Data Envelopment Analysis with missing data; An application to university Libraries in Taiwan". Journal of the operational Research society 51.897-905.2000
[8] Smirlis, Y. G., Maragos, E. K., & Despotis, D. K. (2006). Data envelopment analysis with missing values: An interval DEA approach. Applied Mathematics and Computation, 177(1), 1-10.
[9] Soltanifar, M., Ghiyasi, M., Emrouznejad, A., & Sharafi, H. (2024). A novel model for merger analysis and target setting: A CSW-Inverse DEA approach. Expert Systems with Applications, 249, 123326.
[10] Razipour-GhalehJough, S., Lotfi, F. H., Rostamy-Malkhalifeh, M., & Sharafi, H. (2021). Benchmarking bank branches: A dynamic DEA approach. Journal of Information and Optimization Sciences, 42(6), 1203-1236.
[11] Soltanifar, M., Ghiyasi, M., & Sharafi, H. (2023). Inverse DEA-R models for merger analysis with negative data. IMA Journal of Management Mathematics, 34(3), 491-510.
[12] Ghiyasi, M., Soltanifar, M., & Sharafi, H. (2022). A novel inverse DEA-R model with application in hospital efficiency. Socio-economic planning sciences, 84, 101427.