Determining the effectual factors in enhancing efficiency in non-homogenous Decision-Making Units By utilizing the NDEA Approach (Case study: Cement Production Process)

Subject Areas : International Journal of Mathematical Modelling & Computations

Homa Abedidehkordi ¹ , Ghasem Tohidi ^{2 *} , Shabnam Razavian ³ , Mohammad Ali Keramati ⁴

1 - a Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Mathematics, South Tehran Branch, Islamic Azad University, Tehran, Iran
4 - Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Received: 2025-03-14 Accepted : 2025-05-18 Published : 2025-05-20

Keywords: Efficiency, Data envelopment analysis Network, Non-homogenous decision-making units, Cement production process, Hierarchical analysis process. ,

Abstract :

 Computing efficiency, without being attentive to its enhancement, is not a very desirable aspect and usually organizations, on the basis of targeting improvement assess efficiency. In the research presently conducted, on the fundaments of the efficiency gaged, in the numerous decision-making units (DMUs) of cement producers, the effectual factors implicated towards the enhancement of efficiency, have been brought into consideration. The efficiency of the DMUs have been calculated by taking advantage of the NDEA technique and the efficiency status of each one of the cement factories and its processes have been determined and compared to that, of the other factories. By utilizing this information, the root causes are primarily specified on the basis of the experiences of the experts, well versed in this subject-matter and consequently, by employing the Hierarchical Analysis Approach and the Wasserman’s Communication Technique, the factors influencing the efficiency of the cement production process, are focused on or prioritized. This method, from the viewpoint of a data collection method is descriptive, as well as, is accounted for being a Delphi survey procedure The results indicate that, four factories have an unwarranted consumption of alkaline materials whereas, eight factories are inclined towards calcareous materials. The treatment of chlorine, material-mixing time, the crushing of material, hardness and uneven texture, including, the sodium and potassium feeding rates are respectively, amidst the most crucial aspects, entangled, in an unsuitable consumption of alkaline and calcareous materials, on the grounds of which, solutions based on providing enhancement can be determined

References:

[1] H. Abedi Dehkordi, GH. Tohidi, SH. Razavyan, and m. Keramati, Efficiency evaluation of cement production companies using nonhomogeneous network DEA, Journal of Industrial management study, 21(69) (2023) 199-233, https://doi.org/10.22054/jims.2023.67648.2788.

[2] M. Akbari , J. Shahamat and S. Davari, Investigating effective factors on the efficiency and organizational performance of government department employees, the 8th International Conference on New Research Achievements in Jurisprudence, Law and Sciences (2021).

[3] H. Amirteimoori, A. Amirteimoori and M. karbasian, Performance analysis and ranking of provincial gas companies in the presence of undesirable products, Journal of Operational Research and Its Applications, 17(2)(2020) 1-8, doi:20.1001.1.22517286.2020.17.2.6.4.

[4] M. J. Asgharpour, Multi- criteria decisions, Tehran: university book publishing, (1999).

[5] M. Barat, G. Tohidi, and M. Sanei, DEA for nonhomogeneous mixed networks, Asia Pacific Management Review, 24(2) (2018) 161–166, https://doi.org/10.1016/j.apmrv.2018.02.003.

[6] T.C. Bond, The role of performance measurement in continuous improvement, International Journal of Operations and Production Management, 12(19) (1999) 1318-133, doi:10.1108/01443579910294291

[7] M. Beygi , Basics of planning in bussness. Publication baztab, (2012).

[8] A.Charns, W.W. Cooper, and E. Rhodes, Measuring the efficiency of decision making units, European Journal of Operational Research, 2 (1998), 429–444, https://doi.org/10.1016/0377-2217(78)90138-8.

[9] W. D. Cook, R. Imanirad, J. Harrison, P. Rouse and J. Zhu , Data envelopment analysis with nonhomogeneous DMUs, Operations Research, 61(3) (2013) 666-676, https://doi.org/10.1287/opre.2013.1173.

[10] D. Campisi, P. Mancuso, S. Luigi Mastrodonato and D. Morea, Efficiency assessment of knowledge intensive business services industry in Italy: data envelopment analysis (DEA) and financial ratio analysis, Measuring Business Excellence, (2019) 23: 484-495, doi:10.1108/MBE-09-2019-0095.

[11] J. Du, Y. Chen ,and J. Huo, DEA for non-homogenous parallel networks, Omega, 56(1) (2015) 122– 132.

[12] M.H. Darvish motavali, F. Hoseynzadeh lotfi, N. Shoja and A. Gholamabri, , calculating the efficiency of sustainable supply chain in the cement industry (application of network data envelopment analysis model), Economic Modeling Quarterly, journal of Economic Modeling, 13(2) (2019).

[13] M.J. Farrell, The measurement of productive efficiency, Journal of the Royal Statistical Society: Series A (General), 120(3) (1957) 253 – 290.

[14] A. Foriqi, Identifying factors affecting productivity in organizational management, The 7th National Conference on New Technologies in Civil Engineering, Architecture and Urban Planning, Tehran (2020).

[15] V. Haji Hasani, Investigating factors affecting the performance of the cement industry based on the range method, The first internal conference of management and accounting, Natanz (2013).

[16] J. Jablonsky, Data Envelopment Analysis Models in Non-Homogeneous Environment, Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 67(6) (2019) 1535–1540, http://dx.doi.org/10.11118/actaun201967061535.

[17] C. Kao, Network Data Envelopment Analysis Foundations and Extensions, International Series in Operations Research & Management Science, springer, (2017).

[18] F. Kaydipoor, SH, Salmani, Y. Sadeghi and Z. Sadeghi, Measuring the efficiency and productivity of cement industry companies of Tehran Stock Exchange by data coverage analysis approach and Malmquist productivity index in gray environment, journal of Innovation management and operational strategies, 1(4)(2020)382-363, https://doi.org/10.22105/imos.2021.276467.1038.

[19] N.Kiani and R.Radfar, Identifying and ranking factors affecting organizational productivity using the Dematel model, journal of productivity management, 9(35)(2015)111-130, doi: 20.1001.1.27169979.1394.9.4.5.4.

[20] W. Li, L. Liang, W. D. Cook and J. Zhu, DEA models for non-homogeneous DMUs with different input configurations. European Journal of Operational Research, 254(3) (2016) 946- 956,
doi:10.1016/j.ejor.2016.04.063.

[21] M. Mehregan and Z. Moradi, Using the Multi-Stage of Integrating Approaches Data Envelopment Analysis (DEA) and Balanced Scorecard (BSC) for Enhanced Performance Assessment, Journal of Industrial Management Perspective, 10(37) (2020) 143-165, https://dx.doi.org/10.52547/JIMP.10.1.143.

[22] M. Mirzaei, M. A. Afshar kazemi and A. Toloui Ashlaghi, Designing a new hybrid model based on data envelopment analysis artificial neural network, genetic algorithm and particle mass optimization for efficiency evaluation and modeling of efficient and inefficient units, Journal of Industrial Management Perspective, 4(39) (2019) 107-129, doi:10.52547/jimp.9.2.107.

[23] J. Melai Bousari, S. Limaei, A. Eslam bonyad and A. Amirteymoori, Measuring the efficiency of forest
exploitation companies with the presence of undesirable output (case study: Gilan Proviance), Journal of Iran forest, 14(2) (2021) 213-226,doi:10.22034/ijf.2022.317046.1824.

[24] C. Parker, Performance Measurement, The Journal Available, 49 (2000), 56-70.

[25] M. Ramazaniyan, P. keikhosro and L. Akhavan deylami, Investigating the efficiency of bank management using the DEA technique (a case study of different branches of Tehran bank), Journal of productivity management, 2(49) (2019) 123-144, doi:10.30495/qjopm.2019.666889.

[26] L.D. Richard, Fundamentals of organizational theory translated by Ali parsian, Tehran: university book publishing,(1988).

[27] F. Rezaei, M. Khalilzadeh and P. Soleimani, Factors Affecting Knowledge Management and Its Effect on Organizational Performance: Mediating the Role of Human Capital. Advances in Human-Computer Interaction(2021).

[28] M. Sadeghi and H. Noor alizadeh, Identifying factors affecting the efficiency of insurance desks: case study bank Melat & Ma, Journal of management of tomorrow, (2017) 55(17).

[29] A. Shariatnia and R. sheikh, Evaluation and classification of higher education institutions base on Deming’s process model using data envelopment analysis model, Journal of new research approaches in management and accounting, 4(15) (2019), 177-197, doi:10.17722/ijme.v4i2.188.

[30] D. Soleimani, A. Mostafaei, M. Momeni and M. Rostami mal khalifeh, Development of a dynamic network data envelopment analysis model to evaluate the performance of banks, Journal of Industrial Management Perspective, 7(1) (2017) 67-89, doi:10.1016/j.amc.2003.09.026.

[31] M. Sheykh moradi and GH. Mahdavi, Evaluation of Iran’s Insurance branches in Tehran using fuzzy linear
programming. Conference insurance industry challenges and opportunities, Faculty of Economic since (2018).

[32] A. Valizadeh oghani, N. faghhi farahmand and F. Modares khiyabsani, Evaluating the efficiency of management in Iran's cement industry using the technique of data envelopment analysis, Industrial Management (Azad Sanandaj), 2017 12(42), https://doi.org/10.1080/23311975.2020.1801960%0A.

[33] K. Wang, W. Huang and J. Wu, Y. Liu, Efficiency measures of the Chinese commercia banking system using an additive two-stage DEA, OMEGA, 44(2014) 5-20, doi:10.1016/j.omega.2013.09.005.

[34] C. ye, w. Liangpeng and L. Bo, Data envelopment analysis procedure with two non-homogeneous DMU groups, Journal of Systems Engineering and Electronics, 29(4) (2018), 780-788, doi:10.21629/JSEE.2018.04.12.

[35] S. Yousefi and Z. Mousavi Kashi, Explaining the evaluation model of sustainable development of countries and providing performance improvement using DEA, Journal of Program and development researches, 4(8) (2021) 7-44. doi:10.22034/pbr.2022.300018.1156.

[36] w. Zhu, Y. Yu and P. Sun, Data envelopment analysis cross-like efficiency model for non-homogeneous decision-making units: The case of United States companies, low-carbon investment to attain corporate sustainability.European Journal of Operational Research, 269(1)(2018) 99-110, doi: 10.1016/j.ejor.2017.08.007.