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Journals / Yaşar Üniversitesi E-Dergisi / 2026 / Cilt: 21 Sayı: Special Issue on 24th International Business Congress Özel Sayı

Analysis of the Effectiveness of Company-Based Production Functions in the Turkish Automotive Industry: The Case of TOFAŞ and Ford Otosan

Türkiye Otomotiv Endüstrisinde Şirket Bazlı Üretim Fonksiyonunun Etkinlik Analizi: TOFAŞ ve Ford Otosan Örneği

Onur Akkaya(KİLİS 7 ARALIK ÜNİVERSİTESİ)0000-0003-2694-9073
Abdurrahim Hocagil(OSTİM TEKNİK ÜNİVERSİTESİ)0000-0002-5734-6526
Pages
368–383
Publication Date
2026-03-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results indicate that TOFAŞ generally demonstrates high efficiency and optimal scale performance, whereas Ford Otosan experiences fluctuations in efficiency during certain periods.

📌 3 Key Takeaways

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Objective & Hypothesis:This study employs Data Envelopment Analysis (DEA) and Stochastic Frontier Approach (SFA) to evaluate company-level production functions within the Turkish automotive industry.
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Key Findings & Data:Using firm-level input–output data from 2011 to 2023 for TOFAŞ and Ford Otosan with efficiency and scale performance being measured and compared.
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Conclusion & Impact:The results indicate that TOFAŞ generally demonstrates high efficiency and optimal scale performance, whereas Ford Otosan experiences fluctuations in efficiency during certain periods.
🔬Design:Araştırma Makalesi
📍Scope:2011 Verileri
⚙️Variables:Türkiye otomotiv Endüstrisi · Veri Zarflama · Etkinlik

Abstract

This study employs Data Envelopment Analysis (DEA) and Stochastic Frontier Approach (SFA) to evaluate company-level production functions within the Turkish automotive industry. Using firm-level input–output data from 2011 to 2023 for TOFAŞ and Ford Otosan with efficiency and scale performance being measured and compared. The results indicate that TOFAŞ generally demonstrates high efficiency and optimal scale performance, whereas Ford Otosan experiences fluctuations in efficiency during certain periods. The study sheds light on the competitive production processes in the automotive sector and offers suggestions for future research, including broader sectoral comparisons and the integration of digital transformation and sustainability factors.

Özet

Bu çalışma, Türkiye otomotiv endüstrisinde şirket bazlı üretim fonksiyonlarının verimliliğini değerlendirmek amacıyla Veri Zarflama Analizi (DEA) ve Stokastik Sınır Analizi (SFA) yöntemlerini kullanmaktadır. TOFAŞ ve Ford Otosan örnekleri üzerinden 2011–2023 dönemine ait firma bazlı girdi-çıktı verileri analiz edilmiştir. Elde edilen sonuçlar, TOFAŞ’ın genel olarak yüksek verimlilik ve optimum ölçek etkinliği sergilediğini, Ford Otosan’ın ise belirli dönemlerde verimlilik dalgalanmaları yaşadığını ortaya koymaktadır. Çalışma, otomotiv sektöründeki rekabetçi üretim süreçlerine ışık tutarken, gelecekte genişletilmiş sektörel karşılaştırmalar ve dijital dönüşüm ile sürdürülebilirlik unsurlarının entegrasyonu gibi araştırma alanlarına yönelik öneriler sunmaktadır.

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Cite This Article

Akkaya, O., Hocagil, A., *, O. A. (2026). Analysis of the Effectiveness of Company-Based Production Functions in the Turkish Automotive Industry: The Case of TOFAŞ and Ford Otosan. Yaşar Üniversitesi E-Dergisi, Cilt: 21 Sayı: Special Issue on 24th International Business Congress Özel Sayı, 368–383. https://doi.org/10.19168/jyasar.1749766

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References (24)

  1. Afrifa, G. A. (2022). Stochastic frontier modelling of working capital efficiency. Journal of Risk and Financial Management, 15(4), 170. https://doi.org/10.3390/jrfm15040170
  2. Burkert, A. (2024). The automotive industry in Turkey. MTZ Worldw, 84(5), 8-13. https://doi.org/10.1007/s38313-024-1934-0.
  3. Chen Y., Liang, L.Yang, F., Zhu, J. (2006) Evaluation of Information Technology Investment: A Data Envelopment Analysis Approach, Computers and Operations Research, 33(5),1368-1379.
  4. Coelli, T. (2007) DEAP Programming, University of Quesland.
  5. Çalmaşur, G. (2016). Technical efficiency analysis in the automotive industry: A stochastic frontier approach. International Journal of Economics, Commerce and Management, IV(4), 1–16. https://ijecm.co.uk/wp-content/uploads/2016/04/446.pdf
  6. Çoban, A., Çoban, O., & Baysal Kurt, D. (2018). Technical and scale efficiency of the Turkish automotive industry using data envelopment analysis. Çankırı Karatekin Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(2), 58-71. https://dergipark.org.tr/en/download/article-file/621324.
  7. Dolanay, S. S. (2021). Rapid rise of China automotive industry in the 2000s and history of Turkey automotive industry. China-USA Business Review, 20(1), 46-64. doi: 10.17265/1537-1514/2021.01.003.
  8. Ford Otosan. (2017). A case study of digital journey: Ford Otosan. Retrieved from: https://fplreflib.findlay.co.uk/images/pdf/ems/Sabri%20Cimen%20-%20Ford%20-%20Berlin_november%202017_22_11_videolu%20-%20to%20be%20shared_pdf%20icin.pdf
  9. Ford Otosan. (2022). Case study report: CFD simulations for powertrain test labs. TRUBA. Retrieved from: https://eurocc.truba.gov.tr/wp-content/uploads/2022/11/Fordotosan-sonuc.pdf.
  10. Fusco, E., Balasubramanian, N., & Migliardo, C. (2023). Stochastic frontier estimation through parametric modelling. Empirical Economics, 65(2), 987–1009. https://doi.org/10.1007/s00181-022-02277-9
  11. Günay, G., Çebi, F., & Topal, B. (2020). Innovation efficiency in automotive industry: The case of Turkey. In N. M. Durakbasa & M. Gençyılmaz (Eds.), Digital Conversion on the Way to Industry 4.0 (pp. 643-652). https://doi.org/10.1007/978-3-030-62784-3_55.
  12. Hajihassaniasl, S., & Kök, R. (2016). Scale effect in Turkish manufacturing industry: Stochastic metafrontier analysis. Journal of Economic Structures, 5(13), 1–19. https://doi.org/10.1186/s40008-016-0044-9
  13. Honma, S., & Hu, J. (2018). A meta-stochastic frontier analysis for energy efficiency of regions in Japan. Journal of Economic Structures, 7(19), 1–18. https://doi.org/10.1186/s40008-018-0119-x
  14. Jofree, A. M., Wahab, A. A., & Rahman, R. A. (2021). Efficiency assessment of transport manufacturing firms using a stochastic frontier analysis approach. Sains Malaysiana, 50(8), 2445–2453. https://doi.org/10.17576/jsm-2021-5008-25
  15. Mete, H., & Belgin, Ö. (2021). Impact of knowledge management performance on the efficiency of R&D activities in the Turkish automotive industry: A data envelopment analysis approach. Journal of the Knowledge Economy, 12, 688-708. https://doi.org/10.1007/s13132-021-00758-1.
  16. Oh, D. H., & Hildreth, A. J. (2014). Estimating the technical improvement of energy efficiency in the automotive industry—Stochastic and deterministic frontier benchmarking approaches. Energies, 7(9), 6196–6213. https://doi.org/10.3390/en7096196
  17. Oh, D. H., & Shin, S. K. (2021). The assessment of car making plants with an integrated stochastic frontier analysis model. Mathematics, 9(11), 1296. https://doi.org/10.3390/math9111296
  18. Paker, F. A. (2021). The Republic period of the Turkish automotive industry and product design. Art and Design Review, 9(1), 27-45.
  19. Sun, Y. C. (2024). A stochastic production frontier model for evaluating the effect of AI innovation on production efficiency. Technological Forecasting and Social Change, 198, 122978. https://doi.org/10.1016/j.techfore.2024.122978
  20. Şahin, İ. E., & Akkoyuncu, H. (2019). Türkiye’de Otomotiv Sektöründe Faaliyet Gösteren Şirketlerin Etkinlik Analizi. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 42 (1), 339-347.
  21. Yasar, O. (2013). Automotive Main and Component Industriesin Turkey and Clustering in the Marmara Region. Turkish Studies - International Periodical for The Languages, Literature and History of Turkish or Turkic, 8(6) 779-805.
  22. Yenilmez, İ. (2024). Exploring the Lindley distribution in stochastic frontier models. Communications in Statistics - Simulation and Computation, 53(4), 1102–1119. https://doi.org/10.1080/03610918.2023.2165304
  23. Yıldız, H., & Taştan, H. (2021). Energy efficiency in the Turkish manufacturing industry using firm-level microdata: A stochastic frontier approach. ICE-TEA 2021 Conference Proceedings, 1–14.
  24. Yiğiteli, N. G. (2024). Decomposition of total factor productivity growth in Türkiye: A stochastic frontier analysis approach. Economic Modelling, 125, 106339. https://doi.org/10.1016/j.econmod.2024.106339