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http://www.production.periodikos.com.br/article/doi/10.1590/0103-6513.20250022
Production
Research Article

Internal benchmarking efficiency assessment in a steel company using the DEA network

Josiane Vogt; Gabriel Motta Luft; Mariana Almeida; Daniel Pacheco Lacerda; Fabio Antonio Piran

http://dx.doi.org/10.1590/0103-6513.20250022 Production, vol.35, e20250022, 2025
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Abstract

Paper aims: This study aims to assess the network efficiency of the bar and profile rolling process in a steel manufacturing company using Network DEA (NDEA).

Originality: This research presents the first application of NDEA in combination with internal benchmarking, illustrating its feasibility in supporting significant performance improvements.

Research method: A case study was conducted in a steel plant to analyze the overall network efficiency.

Main findings: The average efficiency was 41.99%, with minimum and maximum values of 15.12% and 99.23%, respectively. Internal benchmarking revealed that the third stage of the rolling process negatively affected overall efficiency. Additionally, critical incidents influencing performance were identified, with 66.67% occurring in the fourth quarter each year.

Implications for theory and practice: Combining NDEA with internal benchmarking enables a continuous improvement framework, allowing the company to monitor and adjust operations for enhanced efficiency.

Keywords

Efficiency, Steel industry, Network data envelopment analysis (NDEA), Internal benchmarking

References

Amir, H. Y., & Reza, K. M. (2022). Centralized resource allocation in two-stage production systems: a network DEA approach. Economic Computation and Economic Cybernetics Studies and Research, 56(3/2022), 279-296. http://doi.org/10.24818/18423264/56.3.22.18.

Barat, M., Tohidi, G., & Sanei, M. (2019). DEA for nonhomogeneous mixed networks. Asia Pacific Management Review, 24(2), 161-166. http://doi.org/10.1016/j.apmrv.2018.02.003.

Camanho, A. S., Silva, M. C., Piran, F. S., & Lacerda, D. P. (2024). A literature review of economic efficiency assessments using Data Envelopment Analysis. European Journal of Operational Research, 315(1), 1-18. http://doi.org/10.1016/j.ejor.2023.07.027.

Camargo, L. F. R., Rodrigues, L. H., Lacerda, D. P., & Piran, F. S. (2018). A method for integrated process simulation in the mining industry. European Journal of Operational Research, 264(3), 1116-1129. http://doi.org/10.1016/j.ejor.2017.07.013.

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429-444. http://doi.org/10.1016/0377-2217(78)90138-8.

Chen, H., Lin, H., & Zou, W. (2020). Research on the Regional Differences and Influencing Factors of the Innovation Efficiency of China’s High-Tech Industries: based on a Shared Inputs Two-Stage Network DEA. Sustainability, 12(8), 3284. http://doi.org/10.3390/su12083284.

Chen, Y., Cook, W. D., Kao, C., & Zhu, J. (2013). Network DEA pitfalls: divisional efficiency and frontier projection under general network structures. European Journal of Operational Research, 226(3), 507-515. http://doi.org/10.1016/j.ejor.2012.11.021.

Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: opportunities and challenges. Academy of Management Journal, 50(1), 25-32. http://doi.org/10.5465/amj.2007.24160888.

Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35-49. http://doi.org/10.1016/S0038-0121(99)00012-9.

Jung, S., Shin, J., & Kim, C. (2025). A study on the operational and competitive efficiency of National Oil Companies using two-stage network DEA model. Operations Management Research: Advancing Practice Through Research, 18(1), 269-283. http://doi.org/10.1007/s12063-024-00518-9.

Kao, C. (2009). Efficiency measurement for parallel production systems. European Journal of Operational Research, 196(3), 1107-1112. http://doi.org/10.1016/j.ejor.2008.04.020.

Kao, C. (2014). Network data envelopment analysis: a review. European Journal of Operational Research, 239(1), 1-16. http://doi.org/10.1016/j.ejor.2014.02.039.

Kao, C., & Hwang, S.-N. (2008). Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418-429. http://doi.org/10.1016/j.ejor.2006.11.041.

Khoveyni, M., & Eslami, R. (2022). Two-stage network DEA with shared resources: illustrating the drawbacks and measuring the overall efficiency. Knowledge-Based Systems, 250, 108725. http://doi.org/10.1016/j.knosys.2022.108725.

Lu, C.-C., Dan, W., Chen, X., Tseng, C.-K., & Chou, K.-W. (2021). Evaluation of the operating performance of Taiwanese machine tool industry with the dynamic network DEA model. Enterprise Information Systems, 15(1), 87-104. http://doi.org/10.1080/17517575.2019.1709662.

Miguel, P. A. C. (2007). Estudo de caso na engenharia de produção: estruturação e recomendações para sua condução. Production, 17(1), 216-229. http://doi.org/10.1590/S0103-65132007000100015.

Nasim, R., Seyyed Esmaeil, N., Zohreh, M., & Farzad Movahedi, S. (2022). A new modeling approach for undesirable factors in efficiency evaluation of cement industry with four stages structure based on piecewise linear NDEA Model. Economic Computation and Economic Cybernetics Studies and Research, 56(1/2022), 57-74. http://doi.org/10.24818/18423264/56.1.22.04.

Omid, A., & Zegordi, S. H. (2015). Integrated AHP and network DEA for assessing the efficiency of Iranian handmade carpet industry. Decision Science Letters, 4(4), 477-486. http://doi.org/10.5267/j.dsl.2015.6.002.

Piran, F. A. S., De Paris, A., Lacerda, D. P., Camargo, L. F. R., Serrano, R., & Cassel, R. A. (2020). Overall equipment effectiveness: required but not enough—An analysis integrating overall equipment effect and data envelopment analysis. Global Journal of Flexible Systems Managment, 21(2), 191-206. http://doi.org/10.1007/s40171-020-00238-6.

Piran, F. S., Camanho, A. S., Silva, M. C., & Lacerda, D. P. (2023). Internal benchmarking for efficiency evaluations using data envelopment analysis: a review of applications and directions for future research. In P. Macedo, V. Moutinho & M. Madaleno (Eds.), Advanced mathematical methods for economic efficiency analysis (Lecture Notes in Economics and Mathematical Systems, pp. 143-162). Cham: Springer. http://doi.org/10.1007/978-3-031-29583-6_9.

Piran, F. S., Lacerda, D. P., Camanho, A. S., & Silva, M. C. A. (2021). Internal benchmarking to assess the cost efficiency of a broiler production system combining data envelopment analysis and throughput accounting. International Journal of Production Economics, 238, 108173. http://doi.org/10.1016/j.ijpe.2021.108173.

Ratner, S. V., Shaposhnikov, A. M., & Lychev, A. V. (2023). Network DEA and Its Applications (2017-2022): a Systematic Literature Review. Mathematics, 11(9), 2141. http://doi.org/10.3390/math11092141.

Sampaio Brasil, J. E., Piran, F. A. S., Lacerda, D. P., Morandi, M. I. W., Oliveira da Silva, D., & Sellitto, M. A. (2024). Enhancing the efficiency of a gas-fueled reheating furnace of the steelmaking industry: assessment and improvement. Management of Environmental Quality, 35(6), 1254-1273. http://doi.org/10.1108/MEQ-08-2023-0266.

Seth, H., Tripathi, D. K., Chadha, S., & Tripathi, A. (2024). Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing. Journal of Modelling in Management. http://doi.org/10.1108/JM2-07-2024-0210.

Silva, J. (2025). With Trump’s tariffs looming - will countries scramble to cut deals? London: BBC News.

Song, K. H., Choi, S., & Han, I. H. (2020). Competitiveness evaluation methodology for aviation industry sustainability using network DEA. Sustainability, 12(24), 10323. http://doi.org/10.3390/su122410323.

Wu, H., Lv, K., Liang, L., & Hu, H. (2017). Measuring performance of sustainable manufacturing with recyclable wastes: a case from China’s iron and steel industry. Omega, 66, 38-47. http://doi.org/10.1016/j.omega.2016.01.009.

Yang, W., Shao, Y., Qiao, H., & Wang, S. (2014). An empirical analysis on regional technical efficiency of chinese steel sector based on network DEA method. Procedia Computer Science, 31, 615-624. http://doi.org/10.1016/j.procs.2014.05.308.

Yang, W., Shi, J., Qiao, H., Shao, Y., & Wang, S. (2017). Regional technical efficiency of Chinese Iron and steel industry based on bootstrap network data envelopment analysis. Socio-Economic Planning Sciences, 57, 14-24. http://doi.org/10.1016/j.seps.2016.07.003.

Yin, R. K. (2005). Estudo de caso: planejamento e métodos (3. ed). Porto Alegre: Bookman.

Zhang, Q., & Zhang, R. (2025). An extended mixed-network DEA method to analyze the power supply system with shared resources. Energy, 324, 136110. http://doi.org/10.1016/j.energy.2025.136110.

Zibaei Vishghaei, Y., Kordrostami, S., Amirteimoori, A., & Shokri, S. (2024). Inverse two-stage data envelopment analysis with interval measures for resource planning. Journal of Modelling in Management, 19(4), 1057-1078. http://doi.org/10.1108/JM2-02-2023-0044.
 

Submitted date:
03/05/2025

Accepted date:
07/14/2025

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