Efficiency measurement in Turkish manufacturing sector using Data Envelopment Analysis (DEA) and Artificial Neural Networks (ANN)

Authors

  • Ömer Akgöbek Department of Industry Engineering, Faculty of Engineering, Zirve University.
  • Emre Yakut

DOI:

https://doi.org/10.18533/jefs.v2i02.138

Keywords:

Artificial Neural Networks, Data Envelopment Analysis, Efficiency Measurement, Manufacture sector.

Abstract

Data Envelopment Analysis (DEA) is a non-parametric measurement technique based on mathematical programming to measure the efficiency level of the firms by determining multiple input and output variables. Artificial neural network (ANN) is information processing system and computer program that imitates human brain’s neural network system. By entering the information from outside, ANN can be trained on examples related to the problem so that modeling of the problem can be provided. This study aims to examine the efficiency level of sectors operating in manufacturing industry in Turkey regarding the years between 1996-2008 via DEA and ANN to evaluate it from the financial aspect.

References

Adler, N., Friedman, L., & Sinuany-Stern, Z., 2002. Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research, 140: 249–265.

Aras, G., 2006. Avrupa Birliği Açısından ve Dünya Pazarlarına Uyum Açısından Türk Tekstil ve Konfeksiyon Sektörünün Rekabet Yeteneği (Finansal Yaklaşım), Mart Matbaası, İstanbul.

Azadeh, A., Amalnick, M.S., Ghaderi, S.F. and Asadzadeh, S.M., 2001. An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy-intensive manufacturing sectors. Energy Policy, 35: 3792-3806.

Banker, R., Charnes, A., & Cooper, W. W. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30: 1078–1092.

Bayyurt, N. &Duzu, G., 2008. Performance measurement of Turkish and Chinese manufacturing firms: A comparative analysis, Eurasian Journal of Business and Economics 2008, 1 (2): 71-83.

Charnes, A., Cooper, W. W., & Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operation Research, 2: 429–444.

Destafanis, S. ve Sena, V., 2007. Patterns of corporate governance and technical efficiency in Italian manufacturing. Published Online in Wiley Inter Science, 28: 27-40.

Esenbel, M., Erkin, M. O., Aydın, F.K., 2007. Veri Zarflama Analizi ile Dokuma, Giyim Eşyası ve Deri Sektöründe Faaliyet Gösteren Firmaların Etkinliğinin Karşılaştırılması”, (http://www.analiz.com/eğitim/gazi/001.html).

Farrel, M.J. 1957. The measurement of productive efficiency.Journal of Royal Statistical Society, A, 120: 253-281.

Hopfield, J.J., 1982.Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy Of Sciences,79: 2554-2558.

Karacabey, A. A., 2001. Veri Zarflama Analizi, Tartışma Metinleri, Ankara Üniversitesi Siyasal Bilgiler Fakültesi, Ankara, No 33: 1-4.

Kargın, S. ve Kayalıdere, K., 2004. Çimento ve Tekstil Sektörlerinde Etkinlik Çalışması ve Veri Zarflama Analizi. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 6(1): 199-201.

Lippman, R., 1987. An introduction to computing with neural nets. IEEE ASSP Mag., 4: 4-22.

Mahadevan, R., 2002. A DEA approach to understanding the productivity growth of Malaysia’s manufacturing industries. Asia Journal of Management, 19: 587-600.

Öztemel E., 2003. Yapay Sinir Ağları, Papatya Yayıncılık, Istanbul.

Park, Y., Choi, J., Seol, H., and Park, G., 2007. A framework for benchmarking service process using data envelopment analysis and decision tree.Expert Systems with Applications, 32: 432-440.

Pissarenko, Dimitri, 2002. Neural networks for financial time series prediction: overview over recent research. Accessed from http://members.inode.at/d.pissarenko/fyp/Pissarenko2002.pdf

Rumelhart, D.E., Hinton, D.E. and Williams R.J., 1986.Learning representation by back-propagating errors. Nature 323(9): 533-536.

Sabutra, P.M.A, 2011. Analysis of technical efficiency of Indonesian manufacturing industries: An application of DEA. International Research Journal of Finance and Economics, 66:1450-2887.

Sayım, M. ve Yalama, A., 2006. Veri Zarflama Analizi (VZA) Yöntemi İle Temel Analiz: Türkiye’de IMKB’ ye Kote İmalat Sektörü Üzerine Ampirik Bir Uygulama,10. Ulusal Finans Sempozyumu, İzmir.

Schaffnit, Claire; Dan Rosen; Joseph C. Paradi, 1997. Best practice analysis of bank branches: An application of DEA in a large Canadian bank.European Journal of Operational Research, 98: 269–289.

Şen Z., 2004. Yapay Sinir Ağlarının İlkeleri, Özener Matbacılık, İstanbul.

Shahmohammadi, Faramarz; Morteza Charmi, 2004. Using DEA to measure performance of science parks: case of Iran. Data Envelopment Analysis and Performance Management, 4th International Symposium of DEA, Aston University, 5–6 September 2004, pp. 396–402. (www.DEAzone.com/DEA2004).

Sueyoshi, T.& Goto, M., 2009. Can R&D expenditure avoid corporate bankruptcy? Comparison between Japanese machinery and electric equipment industries using DEA–discriminant analysis. European Journal of Operational Research, 196: 289–311.

Tarım, A., 2001. Veri Zarflama Analizi Matematiksel Programlama Tabanlı Göre Etkinlik Ölçüm Yaklaşımı, Sayıştay Yayınları, Ankara.

Ulucan, A., 2000. Şirket Performanslarının Ölçülmesinde Veri Zarflama Analizi Yaklaşımı: Genel ve Sektörel Bazda Değerlendirmeler, Hacettepe Üniversitesi İktisadi ve İdari Bilimler Dergisi, 18(1): 406-407.

Yakut, E., 2012. Veri Madenciliği Tekniklerinden C5.0 Algoritması, Destek Vektör Makineleri ile Yapay Sinir Ağlarının Sınıflandırma Başarılarının Karşılaştırılması: İmalat Sektöründe Bir Uygulama. (Yayımlanmamış Doktora Tezi), Atatürk Üniversitesi Sosyal Bilimleri Enstitütüsü, Erzurum.

Yıldız B., 2001. Finansal Başarısızlığın Öngörülmesinde Yapay Sinir Ağı Kullanımı ve Halka Açık Şirketlerde Ampirik Bir Uygulama. İMKB Dergisi, 17: 51-67.

Yolalan, R. 1993. İşletmeler Arası Göreceli Etkinlik Ölçümü, Ankara: Milli Prodüktivite Merkezi Yayınları, No 483.

Downloads

Published

2014-06-20

Issue

Section

Articles