Stratejik Yönetim Perspektifinden Türk Demiryolları Performansının Demiryolu Kazaları Dikkate Alınarak İncelenmesi

Öz

Kıyaslama (benchmarking) stratejik yönetimde sıklıkla kullanılmasına rağmen, Türkiye Cumhuriyeti Devlet Demiryolları (TCDD) gibi bir ülkedeki tüm demiryolu operasyonlarını yürüten firmaların kendilerine özgü yapıları nedeniyle rakipleri ile kıyaslanmaları pek mümkün olamayabilir. Böyle durumlarda, firmanın uzun dönem verileri ile çıktıların girdilere oranına dayanan dinamik veri zarflama analizi yöntemi kullanılabilir. Veri zarflama analizinde son yıllardaki ilerlemelerden biri de istenmeyen çıktıların analize dahil edilebilmesidir. En iyi uygulamanın belirlenmesinde, demiryollarında meydana gelen kazalar gibi istenmeyen durumların da performans değerlendirmesine dahil edilmesi gerekmektedir. Literatürde demiryolu kazalarının dikkate alındığı bir performans değerlendirme çalışmasına rastlanılmamıştır. Bu kapsamda, bu çalışmada demiryollarından istenmeyen çıktı olan demiryolu kazaları dikkate alınarak, TCDD’nin 17 yıllık (2005-2021 arası) verileri ile demiryolu performansının incelenmesi amaçlanmıştır. Bu şekilde, demiryolu performans değerlendirmesi literatürüne istenmeyen çıktıların eklenmesi ile katkı sağlanması amaçlanmaktadır. Analizde toplam raylı sistem uzunluğu (km) ve insan kaynağı girdi olarak, toplam yolcu sayısı ve toplam taşınan yük miktarı istenen çıktı olarak, demiryolu kazası sayısı ise istenmeyen çıktı olarak alınmıştır. Elde edilen bulgular, TCDD için en iyi uygulama örneğinin 2013 yılı olduğunu göstermektedir. Elde edilen bulgulardan uygulayıcılara ve karar vericilere, performans ölçümünde istenmeyen çıktıların göz önünde bulundurulması önerisinde bulunulmuştur. Ayrıca, araştırmacılara yeşil yönetim çerçevesinde gerçekleştirilecek çalışmalarda istenmeyen çıktıların performans ölçümünde dikkate alınmasına ilişkin önerilerde bulunulmuştur.

Anahtar Kelimeler

• Stratejik yönetim
• İstenmeyen çıktılı veri zarflama analizi
• Dinamik analiz
• raylı sistem performansı

Etik Beyan

During the writing and publication of this study, the rules of Research and Publication Ethics were complied with, and no falsification was made in the data obtained for the study. Ethics committee approval is not required for the study

From the Strategic Management Perspective Examining Turkish Railways’ Performance Considering Railway Accidents

Öz

Benchmarking is frequently used in strategic management, while it may not be possible to compare companies such as Turkish Railways (TCDD) with their competitors due to unique structures. In such cases, the dynamic data envelopment analysis method, which is based on the ratio of outputs to inputs with the company's long-term data, can be used. One of the advances in data envelopment analysis in recent years is the ability to include undesired outputs. While determining the best practice, undesirable factors as railway accidents should also be considered for performance evaluation. To my knowledge, there is no study considering accidents in railway performance evaluation. In this context, this study aims to evaluate the performance of railways with 17-year data of TCDD, considering railway accidents, which are undesirable outputs for railways. In this way, it is aimed to contribute to the railway performance evaluation literature by adding undesirable outputs. The total length (km) and the number of personnel are inputs, the total number of passengers and the total freight are desired outputs, and the number of railway accidents is undesired output. The findings show that the best practice example for TCDD is 2013. The findings suggest that practitioners and decision makers should consider undesirable outputs in performance measurement. In addition, suggestions made to the researchers regarding the studies to be carried out within the framework of green management considering undesirable outputs.

Anahtar Kelimeler

• Strategic management
• Data envelopment analysis with undesirable outputs
• Dynamic analysis
• railway performance

Etik Beyan

During the writing and publication of this study, the rules of Research and Publication Ethics were complied with, and no falsification was made in the data obtained for the study. Ethics committee approval is not required for the study.

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.
2. Bhanot, N. and Singh, H. (2014). Benchmarking the performance indicators of Indian Railway container business using data envelopment analysis. Benchmarking, 21(1), 101–120. https://doi.org/10.1108/BIJ-05-2012-0031/FULL/PDF
3. Bowlin, W. F. (2011). Measuring Performance: An Introduction to Data Envelopment Analysis (DEA). The Journal of Cost Analysis, 15(2), 3–27. https://doi.org/10.1080/08823871.1998.10462318
4. Charnes, A., Cooper, W. W. and Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
5. Cheng, D. Y. (2004). Using data development analysis to identify performance benchmarks. In L. Hua (Ed.), Proceedings of The 2004 International Conference On Management Science and Engineering, Vols 1 And 2 (pp. 570–574).
6. Debreu, G. (1951). The Coefficient of Resource Utilization. Econometrica, 19(3), 273. https://doi.org/10.2307/1906814
7. Djordjević, B., Krmac, E. and Mlinarić, T. J. (2018). Non-radial DEA model: A new approach to evaluation of safety at railway level crossings. Safety Science, 103, 234–246. https://doi.org/10.1016/J.SSCI.2017.12.001
8. Doomernik, J. E. (2015). Performance and efficiency of high-speed rail systems. Transportation Research Procedia, 8, 136-144.

9. Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. v., Sarrico, C. S. and Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research, 132(2), 245–259. https://doi.org/10.1016/S0377-2217(00)00149-1 EUROSTAT. (2017). Statistics | Eurostat, available at: https://ec.europa.eu/eurostat/databrowser/view/rail_ac_catnmbr/default/table?lang=en. (accessed 20 October 2022).
10. Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253–290. https://doi.org/10.2307/2343100
11. Golany, B. and Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), 237–250. https://doi.org/10.1016/0305-0483(89)90029-7
12. Hilmola, O. P. (2007). European railway freight transportation and adaptation to demand decline: Efficiency and partial productivity analysis from period of 1980-2003. International Journal of Productivity and Performance Management, 56(3), 205–225. https://doi.org/10.1108/17410400710731428/FULL/PDF
13. Kabasakal, A., Kutlar, A. and Sarikaya, M. (2015). Efficiency determinations of the worldwide railway companies via DEA and contributions of the outputs to the efficiency and TFP by panel regression. Central European Journal of Operations Research, 23(1), 69–88. https://doi.org/10.1007/S10100-013-0303-X/TABLES/8
14. Lawrence, W. and Erwin, T. (2003). Technical efficiency and service effectiveness for railways industry: DEA approaches. Journal of the Eastern Asia Society for Transportation Studies, 5(1), 2932–2947.
15. Liu, Z., Qin, C. X. and Zhang, Y. J. (2016). The energy-environment efficiency of road and railway sectors in China: Evidence from the provincial level. Ecological Indicators, 69, 559–570. https://doi.org/10.1016/J.ECOLIND.2016.05.016
16. Seiford, L. M. and Zhu, J. (2002). Modelling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16–20. https://doi.org/10.1016/S0377-2217(01)00293-4
17. Song, M., Zhang, G., Zeng, W., Liu, J. and Fang, K. (2016). Railway transportation and environmental efficiency in China. Transportation Research Part D: Transport and Environment, 48, 488–498.
18. Sun, X., Yan, S., Liu, T. and Wu, J. (2020). High-speed rail development and urban environmental efficiency in China: A city-level examination. Transportation Research Part D: Transport and Environment, 86, 102456. https://doi.org/10.1016/J.TRD.2020.102456
19. TCDD. (2010). 2005-2009 Annual Statistics, available at: https://static.tcdd.gov.tr/webfiles/userfiles/files/20052009ist.pdf. (accessed 18 October 2022).
20. TCDD. (2014). 2009-2013 Annual Statistics, available at: https://static.tcdd.gov.tr/webfiles/userfiles/files/20092013yillik.pdf. (accessed 18 October 2022).
21. TCDD. (2018). 2013-2017 Annual Statistics, available at: https://static.tcdd.gov.tr/webfiles/userfiles/files/20132017yillik.pdf. (accessed 18 October 2022).
22. TCDD. (2022). 2017-2021 Annual Statistics, available at: https://static.tcdd.gov.tr/webfiles/userfiles/files/istrapor/ist20172021.pdf. (accessed 18 October 2022).
23. Yu, M. M. and Lin, E. T. (2008). Efficiency and effectiveness in railway performance using a multi-activity network DEA model. Omega, 36(6), 1005-1017.
24. Zhou, H. and Hu, H. (2017). Sustainability Evaluation of Railways in China Using a Two-Stage Network DEA Model with Undesirable Outputs and Shared Resources. Sustainability 2017, 9(1), 150. https://doi.org/10.3390/SU9010150.