Tedarikçi Değerlendirmesinde EATWOS ve OCRA Yöntemlerine Dayalı Karşılaştırmalı Bir Analiz

Year 2019, Volume: 7 Issue: 1, 103 - 112, 30.06.2019

Nilsen Kundakcı

https://doi.org/10.17093/alphanumeric.477322

Cited By: 7

Abstract

Artan rekabet koşullarında, tedarik zincirinin en önemli parçalarından biri olan tedarikçileri değerlendirme ve seçme yöntemleri şirketler için önem kazanmıştır. Potansiyel veya mevcut tedarikçileri değerlendirmek için, nicel analizlerin uygulanması şirket yönetimine yardımcı olabilir. Bu yazıda, tedarikçilerin verimliliği EATWOS ve OCRA yöntemleri ile değerlendirilmiştir. Tedarikçilerin sıralaması verimlilik puanlarına göre belirlenmiş ve elde edilen sonuçlar karşılaştırılmıştır.

Keywords

Tedarikçi Değerlendirme, Etkinlik, EATWOS, OCRA

A Comparative Analyze Based On EATWOS and OCRA Methods For Supplier Evaluation

Abstract

In the conditions of increasing competition, the methods of evaluating and selecting suppliers which are one of the most important part of the supply chains have gained importance for the companies. To evaluate the potential or current suppliers, applying quantitative analysis can be helpful for the company management. In this paper, efficiencies of suppliers are evaluated with EATWOS (Efficiency Analysis Technique With Output Satisficing) and OCRA (Operational Competitiveness RAting) methods. The ranking of the suppliers are determined based on their efficiency scores then the obtained results are compared.

Keywords

Supplier Evaluation, Efficiency, EATWOS, OCRA

References

• Bakucs, Z., Fertő, I., Fogarasi, J., Tóth. J. & Latruffe, L. (2011). Assessment of the impact of EU accession upon farms’ performance in the New Member States with special emphasis on the farm type (Rapport N° FACEPA Deliverable No. D 5.3 – February 2011). 46 p.http://prodinra.inra.fr/record/266323.
• Bansal, A, Singh R. Kr., Issar, S. & Varkey, J. (2014). Evaluation of vendors ranking by EATWOS approach. Journal of Advances in Management Research, 11(3), 290-311.Chatterjee, P. & Chakraborty, S. (2012). Material selection using preferential ranking methods. Material and Design, 35, 384-393.
• Chatterjee, P. & Chakraborty, S. (2014). Flexible manufacturing system selection using preference ranking methods: A comparative study. International Journal of Industrial Engineering Computations, 5, 315–338.
• Chatterjee, P. (2013). Applications of preference ranking-based methods for decision-making in manufacturing environment. PhD Thesis, Jadavpur University, Kolkata.
• Darji, V.P. & Rao, R.V. (2014). Intelligent multi criteria decision making methods for material selection in sugar industry. Procedia Materials Science, 5, 2585 – 2594.
• Ho, W., Xu, X. & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202, 16–24.
• Jayanthi, S., Kocha, B. & Sinha, K.K. (1996). Competitive analysis of U.S food processing plants. The Retail Food Industry Center, Working Paper, 96-04.
• Jayanthi, S., Kocha, B. & Sinha, K.K. (1999). Competitive analysis of manufacturing plants: an application to the US processed food industry. European Journal of Operational Research, 118 (2), 217–234.
• Kumar, N., Singh, A., Verma, A. & Sonal T. (2016). Measuring efficiency of IPL players using EATWOS. International Journal of Advanced Production and Industrial Engineering, 1(2), 13-16.
• Madić, M., Petković, D. & Radovanović, M. (2015). Selection of non-conventional machining processes using the OCRA method. Serbian Journal of Management, 10 (1), 61 – 73.
• Özbek, A. (2015a). Analysis of private pension companies in Turkey by EATWOS. European Journal of Business and Management, 7(26), 31-43.
• Özbek, A. (2015b). Efficiency analysis of the Turkish Red Crescent between 2012 and 2014. International Business Research, 7(9), 322-334.
• Özbek, A. (2015c). Efficiency analysis of non-governmental organizations based in Turkey. International Business Research, 8(9), 95-104.
• Özbek, A. (2015d). Operasyonel rekabet değerlendirmesi (OCRA) yöntemiyle mevduat bankalarinin etkinlik ölçümü. NWSA-Social Sciences, 10(3), 120-134.
• Özbek, A. (2015e). Performance analysis of public banks in Turkey. International Journal of Business Management and Economic Research, 6(3), 178–186.
• Özbek, A. (2015f). Efficiency analysis of foreign-capital banks in Turkey by OCRA and MOORA environment. Research Journal of Finance and Accounting, 6(13), 21–31.
• Özbek, A. (2016). Efficiency analysis of gold mining companies through financial statements. International Journal of Academic Research in Business and Social Sciences, 6(10), 273-287.
• Özbek, A. (2018). Efficiency analysis in charity organizations by multiple criteria decision making methods, Anadolu University Journal of Social Sciences, 18(2), 99-113.
• Parkan, C. & Wu M.L. (1996). Selection of a manufacturing process with multiple benefit attributes. International Conference on Engineering and Technology Management. Managing Virtual Enterprises: A Convergence of Communications, Computing, and Energy Technologies. IEMC 96 Proceeding Book, pp. 447-452.
• Parkan, C. & Wu, M.L. (1998). Process selection with multiple objective and subjective attributes. Production Planning & Control, 9(2), 189–200.
• Parkan, C. & Wu, M.L. (1999a). Measurement of the performance of an investment bank using the operational competitiveness rating procedure. Omega, 27(2), 201-217.
• Parkan, C. & Wu, M.L. (1999b). Measuring the performance of operations of Hong Kong’s manufacturing industries. European Journal of Operational Research, 118(2), 235-258.
• Parkan, C. & Wu, M.L. (1999c). Decision-making and performance measurement models with applications to robot selection. Computers & Industrial Engineering, 36(3), 503–523.
• Parkan, C. & Wu, M.L. (2000). Comparison of three modern multicriteria decision-making tools. International Journal of Systems Science, 31(4), 497-517.
• Parkan, C. (1994). Operational competitiveness ratings of production units. Managerial and Decision Economics, 15(3), 201-221.
• Parkan, C. (1996a). Performance measurement for a subway system in Hong Kong. The Georgia Productivity Workshop II, Athens, GA.
• Parkan, C. (1996b). Measuring the performance of hotel operations. Socio-Economic Planning Sciences, 30(4), 257–292.
• Parkan, C. (2002). Measuring the operational performance of public transit company. International Journal of Operations & Production Management, 22(6), 693-720.
• Parkan, C. (2003). Measuring the effect of a new point of sale system on the performance of drugstore operations. Computers & Operations Research, 30(4), 729-744.
• Parkan, C. (2005). Benchmarking operational performance: the case of two hotels. International Journal of Productivity and Performance Management, 54(8), 679 – 696.
• Parkan, C., Lam, K. & Hang, G. (1997). Operational competitiveness analysis on software development. The Journal of the Operational Research Society, 48(9), 892-905.
• Peters, M. L. and Zelewski, S., (2006). Efficiency analysis under consideration of satisficing levels for output quantities. In Proceedings of the 17th Annual Conference of the Production and Operations Management Society, 28.04-01.05.2006 in Boston, pp.2-18.
• Talluri, S. & Narasimhan R. (2003). Vendor evaluation with performance variability: A max–min approach. European Journal of Operational Research, 146, 543–552.
• Tóth. J. (2005). Működési versenyképesség és hajtóerői a hazai húsiparban (Operational competitiveness and its driving forces in the Hungarian meat industry). Közgazdasági Szemle, LII. évf.. július–augusztus, 743-762.
• Tuş Işık, A. & Aytaç Adalı, E. (2016). A new integrated decision making approach based on SWARA and OCRA methods for the hotel selection problem. International Journal of Advanced Operations Management, 8 (2), 140-151.