VERİ ZARFLAMA ANALİZİ YÖNTEMİ KULLANILARAK İNŞAAT SEKTÖRÜNDE ETKİNLİK ÖLÇÜMÜ

Öz

İnsanların açlık susuzluk gibi fizyolojik ihtiyaçlarına ek olarak barınma ihtiyaçları da bulunmaktadır. Yangın, sel, deprem gibi doğal afetler, salgınlar ve savaşlar neticesinde insanlar farklı bölgelere yerleşmektedirler. Dünya genelinde artan nüfus, salgın hastalıklar ve felaketler inşaat sektörünün ne kadar öncelikli ve hayati öneme sahip bir sektör olduğunu göstermektedir. Türkiye’de yaşanan deprem felaketi ve sınır komşularında yaşanan savaşlara bağlı olarak artan göç inşaat sektörünün Türkiye açısından ne kadar önemli ve öncelikli olduğunu bir kez daha gözler önüne sermektedir. Diğer birçok sektörde olduğu gibi inşaat sektöründe de işgücü ve finansal kaynakların etkin bir şekilde kullanılması gerekmektedir. Türkiye’de faaliyet gösteren inşaat firmalarının etkinliklerinin ölçülmesi inşaat sektörünün mevcut durumu açısından oldukça önemlidir. Bu çalışmanın amacı Fortune 500 listesinde bulunan ve Türkiye’de inşaat ve taahhüt alanında faaliyet gösteren on firmanın etkinlik değerlendirmesini gerçekleştirmektir. Etkinlik ölçümünde genellikle Veri Zarflama Analizi (VZA) yöntemi yaygın bir şekilde kullanılmaktadır. Çalışmada etkinlik analizleri için girdi odaklı CCR modeli ve Süper Etkinlik (SE)-CCR modeli kullanılmıştır. VZA analizi için "toplam aktifler" ve "çalışan sayısı" girdi değişkenleri, "net satış" ise çıktı değişkeni olarak kullanılmıştır. VZA analizi sonuçlarına göre karar verme birimi (KVB) olarak ele alınan on firma arasından üçünün etkin olduğu tespit edilmiştir. Etkin çıkan firmalar SE CCR modeli sonuçlarına göre kendi aralarında sıralanmıştır.

Anahtar Kelimeler

• Etkinlik
• İnşaat
• İnşaat ve taahhüt
• Performans
• Veri Zarflama Analizi (VZA)

EFFICIENCY MEASUREMENT IN CONSTRUCTION SECTOR USING DATA ENVELOPMENT ANALYSIS METHOD

Öz

In addition to physiological needs such as hunger and thirst, people also have shelter needs. As a result of natural disasters such as fire, flood, earthquake, epidemics and wars, people settle in different regions. The world's increasing population, epidemics and disasters show how priority and vital the construction industry is. The earthquake disaster in Turkey and the increasing migration due to wars in the border regions once again reveal how important and priority the construction sector is for Turkey. As in many other sectors, labor and financial resources must be used efficiently in the construction industry. Measuring the efficiency of construction companies operating in Turkey is very important for the current situation of the construction industry. This study aims to evaluate the efficiency of ten companies on the Fortune 500 list operating in the field of construction and contracting in Turkey. The Data Envelopment Analysis (DEA) method is generally widely used in efficiency measurement. In the study, input-oriented CCR model and Super Efficiency (SE)-CCR model were used for efficiency analysis. For DEA analysis, "total assets" and "number of employees" were used as input variables, and "net sales" were used as output variables. According to the DEA analysis results, three of the ten companies considered as decision-making units (DMU) were efficient. Efficient companies are ranked among themselves according to the results of the SE CCR model.

Anahtar Kelimeler

• Efficiency
• Construction
• Construction and Contracting
• Performance
• Data Envelopment Analysis (DEA)

Kaynakça

1. Banker, R.D., Charnes, A., and Cooper, W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078
2. Bierens, H. J. (2014). The Tobit Model. [Electronic source] Url: http://grizzly.la.psu.edu/~hbierens/EasyRegTours/TOBIT_Tourfiles/TOBIT.PDF (Date of access: 05.05.2024)
3. Charnes, A., Cooper, W.W. & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
4. Coelli, T. J., et al. (1998). An Introduction to Efficiency and Productivity Analysis. Second Edition. New York: Springer,
5. Cooper, W.W., Seiford, L.M., and Zhu, J. (2011). Data envelopment analysis: History, models, and interpretations. In W. Cooper, L. Seiford, & J. Zhu (Eds.), Handbook on data envelopment analysis. Springer
6. Dalir, O., Torabi, T., Rabiei, M., Jahromi, M. (2024). Application of data envelopment analysis in determining the efficiency of management and company. International Journal of Nonlinear Analysis and Applications (IJNAA), 15(6), 237-243. http://dx.doi.org/10.22075/ijnaa.2023.31175.4582
7. De Leone, R. (2008). Data Envelopment Analysis . In: Floudas, C., Pardalos, P. (eds) Encyclopedia of Optimization. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74759-0_107
8. Dzeng, R. J., & Wu, J. S. (2012). The cost efficiency of construction industry in Taiwan. The Open Construction & Building Technology Journal, 6(1), 8-19.

9. Ersoy, Y. (2021). Performance Evaluation in Distance Education by Using Data Envelopment Analysis (DEA) and TOPSIS Methods. Arabian Journal for Science and Engineering, 46, 1803-1807. https://doi.org/10.1007/s13369-020-05087-0
10. Ersoy, Y., & Tehci, A. (2023). Efficiency Evaluation of Energy Companies with Data Envelopment Analysis. The Journal of International Scientific Researches, 8(3), 360-366. https://doi.org/10.23834/isrjournal.1331147
11. Ersoy, T.H., and Küsbeci, P. (2023). Performance Evaluation of Public Hospitals with Data Envelopment Analysis (DEA) Method. The Journal of International Scientific Researches, 8(3), 333-351.
12. https://doi.org/10.23834/isrjournal.1328311 EViews 9 (2016). User’s Guide I-II. California: IHS Global Inc,
13. FORTUNE 500 Türkiye https://www.fortuneturkey.com/fortune500 Erişim 20/12/2023
14. Horta, I.M., Camanho, A.S. and Da Costa, J.M. (2010). Performance assessment of construction companies integrating key performance indicators and data envelopment analysis. Journal of Construction Engineering and Management, 136(5), 581-594. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000145
15. Henningsen, A. (2012). Estimating Censored Regression Models in R using the censReg Package. University of Copenhagen.
16. Hu, X., & Liu, C. (2018). Measuring efficiency, effectiveness and overall performance in the Chinese construction industry. Engineering, Construction and Architectural Management, 25(6), 780-797. https://doi.org/10.1108/ECAM-06-2016-0131
17. Hu, X., & Liu, C. (2016). Profitability performance assessment in the Australian construction industry: a global relational two-stage DEA method. Construction Management and Economics, 34(3), 147–159. https://doi.org/10.1080/01446193.2016.1180415
18. Kyrgiakos, L.S.., Kleftodimos, G., Vlontzos, G., Pardalos, P.M. (2023). A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability. Operational Research, 23(7). 1-38. https://doi.org/10.1007/s12351-023-00741-5
19. Lee Y-S. (2014). A survey of DEA applications in measuring the efficiency performance of construction organizations. Korean Journal of Construction Engineering and Management, 15(5),103–14. https://doi.org/10.6106/KJCEM.2014.15.5.103
20. Luo, L., Chen, H., Wu, G., Chen, L. (2022). A three-stage network DEA approach for performance evaluation of BIM application in construction projects. Technology in Society, 71, https://doi.org/10.1016/j.techsoc.2022.102105
21. Malik, M., Efendi, S., Zarlis, M. (2018). Data Envelopment Analysis (DEA) Model in Operation Management. IOP Conf. Series: Materials Science and Engineering, 300(2018), 1-7. doi:10.1088/1757-899X/300/1/012008
22. Narayaran, E., Ismail, W.R.B., Mustafa, Z.B. (2022). A data-envelopment analysis-based systematic review of the literature on innovation performance. Heliyon, 8(12), 1-15. https://doi.org/10.1016/j.heliyon.2022.e11925
23. Noorizadeh, A., Rashidi, K., & Peltokorpi, A. (2018). Categorizing suppliers for development investments in construction: application of DEA and RFM concept. Construction Management and Economics, 36(9), 487–506. https://doi.org/10.1080/01446193.2017.1416151
24. Obi, O.F., Lebel, L. & Latterini, F. (2023). A Review of Applications of Data Envelopment Analysis in Forest Engineering. Current Forestry Reports 9, 171–186. https://doi.org/10.1007/s40725-023-00183-4
25. Prabhu, P. G., & Ambika, D. (2013). Study on behaviour of workers in construction industry to improve production efficiency. International Journal of Civil, Structural, Environmental and Infrastructure Engineering Research and Development (IJCSEIERD), 3(1), 59-66.
26. Rostamzadeh, R., Akbarian, O., Banaitis, A., Soltani, Z. (2021). Application of DEA in Benchmarking: A Systematic Literature Review from 2003-2020. Technological and Economic Development of Economy, 27(1), 175-222. https://doi.org/10.3846/tede.2021.13406
27. Seiford, L.M, Zhu, J. (1999). Infeasibility of super-efficiency data envelopment analysis models. INFOR: Information Systems and Operational Research, 37(2), 174–187. https://doi.org/10.1080/03155986.1999.11732379 Sarı, E.B. (2018). Organize sanayi bölgelerinin etkinliklerinin veri zarflama analizi ile değerlendirilmesi. Ekonomi Bilimleri Dergisi, 10(2), 17-32.
28. Selamzade F., and Baghirov, A. (2022). Measuring the Efficiency of the Azerbaijan Banking System with Data Envelopment Analysis (2015-2019). Journal of Social Sciences of Mus Alparslan University, 10(1), 119-137. https://doi.org/10.18506/anemon.888731
29. Selamzade, F., Ersoy, Y., Ozdemir, Y., Celik, M.Y. (2023). Health Efficiency Measurement of OECD Countries Against the COVID-19 Pandemic by Using DEA and MCDM Methods. Arabian Journal for Science and Engineering, 48(11), 15695-15712. https://doi.org/10.1007/s13369-023-08114-
30. Shureshjani, R. A., Askarinejad, S., & Foroughi, A. A. (2022). Evaluating the Efficiency of Decision Making Units in Fuzzy two-stage DEA Models. Fuzzy Information and Engineering, 14(3), 291–313. https://doi.org/10.1080/16168658.2022.2152921
31. Sueyoshi, T. & Goto, M. (2009). DEA–DA for bankruptcy-based performance assessment: Misclassification analysis of Japanese construction industry. European Journal of Operational Research, 199(2), https://doi.org/10.1016/j.ejor.2008.11.039
32. Şahin, İ. E., & Karacan, K. B. (2020). Entropi Temelli Copras ve Aras Yöntemleri ile Borsa İstanbul İnşaat Endeksi XINSA Firmalarının Finansal Performans Analizi. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (44), 171-183.
33. Tobin, J. (1958) Estimation of Relationships for Limited Dependent Variables. Econometrica. Vol. 26.1.: 24-36. [Electronic source] Url: https://cran.r-project.org/web/packages/censReg/vignettes/censReg.pdf (Date of access: 05.05.2024).
34. Türkiye İnşaat Malzemesi Sanayicileri Derneği-İMSAD (2023) https://www.imsad.org Uploads/Files/ Turkiye_IMSAD_Aylik_Sektor_Raporu_NISAN2023.pdf Erişim 20/12/2023.
35. Türkiye Müteahhitler Birliği-TMB (2023) https://www.tmb.org.tr/files/doc/YDMH_01022023.pdf Erişim 20/12/2023
36. Xu, B., & Ouenniche, J. (2012) A data envelopment analysis-based framework for the relative performance evaluation of competing crude oil prices volatility forecasting models. Energy Economics, 34(2), 576–583. https://doi.org/10.1016/j.eneco.2011.12.005
37. Yang, Z., Fang, H., & Xue, X. (2021). Sustainable efficiency and CO2 reduction potential of China’s construction industry: application of a three-stage virtual frontier SBM-DEA model. Journal of Asian Architecture and Building Engineering, 21(2), 604–617. https://doi.org/10.1080/13467581.2020.1869019
38. Yesilyurt, O., & Salamov, F. (2017). Evaluation of Efficiency and Factors Influencing the Efficiency in the Health Systems of Turkic States with Super-efficiency and Tobit Models. BNEJSS. 03(02), 128-138.