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YENİ BİR ÇOK KRİTERLİ KARAR VERME YÖNTEMİ: BULUT ENDEKS-BETA (BE-β)

Year 2022, , 393 - 414, 30.07.2022
https://doi.org/10.51551/verimlilik.1031366

Abstract

Amaç: Çalışmanın temel amacı, Çok Kriterli Karar Verme (ÇKKV) problemlerinin çözümü için geliştirilen Bulut Endeks-Beta (BE-β) yöntemini hem teorik hem de uygulamalı olarak tanıtmaktır. Bu kapsamda Bulut Endeks (BE) ile bu yöntemin gelişmiş versiyonu olan BE-β karşılaştırılmıştır.


Yöntem:
Yöntemler, Türkiye’deki Temel Eczacılık Ürünlerinin ve Eczacılığa İlişkin Malzemelerin İmalatı sektörünün 2006-2019 dönemi finansal tablo verileri üzerinden test edilmiştir. BE-β versiyonunda işlem adımları hem kısaltılmış hem de sadeleştirilmiştir. Ayrıca her iki yöntemden elde edilen bulgular arasındaki ilişki, Spearman Sıra ve Kendall Tau Korelasyon yöntemleri ile ölçülmüştür.


Bulgular:
Spearman sıra ve Kendall Tau korelasyonları sonuçlarına göre BE ve BE-β sıralamaları arasında istatistiksel olarak anlamlı olmayan negatif bir ilişki vardır. Temel Eczacılık Ürünlerinin ve Eczacılığa İlişkin Malzemelerin İmalatı sektörünün 2006-2019 dönemi değerlendirildiğinde BE yöntemine göre en iyi alternatif, 57,52 BE skoruna sahip 2019 yılıdır. BE-β yönteminde ise en iyi alternatif 68,12 BE-β skoruna sahip 2014 yılıdır. BE yöntemine göre en düşük performansın gösterildiği alternatif 38,96 BE skoruna sahip 2010 yılıdır. Benzer şekilde BE-β yönteminde de en düşük performansın gösterildiği alternatif 30,72 BE-β skoruna sahip 2010 yılıdır.

Özgünlük: ÇKKV problemlerinin çözümüne yönelik dinamik ve kolay uygulanabilir özgün bir endeks ortaya konulmuştur. Ayrıca endekslerle daha alt seviyelerde çıktı üretilebilmesinden dolayı daha zengin iç görü elde edilerek derinlemesine analiz yapılabilmektedir.

References

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A NEW MULTI-CRITERIA DECISION MAKING METHOD: BULUT INDEX-BETA (BI-β)

Year 2022, , 393 - 414, 30.07.2022
https://doi.org/10.51551/verimlilik.1031366

Abstract

Purpose: The main purpose of the study is to introduce Bulut Index-Beta (BI-β) method developed to solve Multi-Criteria Decision Making (MCDM) problems, both theoretically and practically. In this context, Bulut Index (BI) and BI-β, which is the advanced version of this method, were compared.


Methodology:
The methods were tested on 2006-2019 period financial statement data of Manufacturing of Basic Pharmaceutical Products and Pharmaceutical Materials sector in Turkey. In BI-β method, implementation steps are both shortened and simplified. In addition, the relationship between the findings obtained from both methods was measured with Spearman Rank and Kendall Tau Correlation methods.

Findings: According to the findings of Spearman Rank and Kendall Tau correlations, there is a statistically insignificant negative correlation between BI and BI-β rankings. When 2006-2019 period of Manufacturing of Basic Pharmaceutical Products and Pharmaceutical Materials sector is evaluated, the best alternative according to BI method is the year 2019 with a BI score of 57,52. In BI-β method, the best alternative is the year 2014 having a BI-β score of 68,12. The alternative with the lowest performance according to BI method is the year 2010 with a BI score of 38,96. Similarly, the alternative with the lowest performance in BI-β method was the year 2010 with a BI-β score of 30,72.

Originality: A dynamic and an easy-to-apply unique index for solution of MCDM problems has been put forward. In addition, since outputs can be produced at sub-levels with the indices, richer insights can be obtained, and thus in-depth analysis can be performed.

References

  • Alexander, M. (2012). “Decision-Making Using the Analytic Hierarchy Process (AHP) and SAS/IML”. URL: https://www.lexjansen.com/nesug/nesug12/po/po04.pdf, (Erişim tarihi: 24.09.2021).
  • Almeida-Filho, A.T., Lima Silva, D.F. ve Ferreira, L. (2021). Financial Modelling with Multiple Criteria Decision Making: A Systematic Literature Review, Journal of the Operational Research Society, 72 (10), 2161-2179.
  • Andriosopoulos, D., Doumpos, M., Pardalos, P.M. ve Zopounidis, C. (2019). “Computational Approaches and Data Analytics in Financial Services: A Literature Review”, Journal of the Operational Research Society, 70 (10), 1581-1599.
  • Angilella, S. ve Mazzù, S. (2015). “The Financing of Innovative SMEs: A Multicriteria Credit Rating Model”, European Journal of Operational Research, 244 (2), 540-554.
  • Baker, D., Bridges, D., Hunter, R., Johnson, G., Krupa, J., Murphy, J. ve Sorenson, K. (2002). “Guidebook to Decision-Making Methods”, WSRC-IM-2002-00002, Department of Energy, USA.
  • Baltussen, R., Marsh, K., Thokala, P., Diaby, V., Castro, H., Cleemput, I., Garau, M., Iskrov, G., Olyaeemanesh, A., Mirelman, A., Mobinizadeh, M., Morton, A., Tringali, M., Van Til, J., Valentim, J., Wagner, M., Youngkong, S., Zah, V., Toll, A., Jansen, M., Bijlmakers, L., Oortwijn, W. ve Broekhuizen, H. (2019). “Multicriteria Decision Analysis to Support Health Technology Assessment Agencies: Benefits, Limitations, and the Way Forward”, Value Health, 22 (11), 1283-1288.
  • Baydaş, M. ve Elma, O.E. (2021). “An Objective Criteria Proposal for the Comparison of MCDM and Weighting Methods in Financial Performance Measurement: An Application in Borsa Istanbul”, Decision Making: Applications in Management and Engineering, 4(2), 257-279.
  • Beaver, W.H. (1966). “Financial Ratios as Predictors of Failure”, Journal of Accounting Research, 4, 71-111.
  • Beaver, W.H. (1968). “Alternative Accounting Measures as Predictors of Failure”, The Accounting Review, 43(1), 113-122.
  • Bulut, T. (2017). “Organize Sanayi Bölgeleri (OSB’ler) Tüzel Kişiliklerinin Finansal Performans Analizine Yönelik Endeks Önerisi: Bulut Performans Endeksi”, Verimlilik Dergisi, 3, 29-57.
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  • Carpenter J.R. ve Smuk, M. (2021). “Missing Data: A Statistical Framework for Practice”, Biometrical Journal, 2021 Jun, 63(5), 915-947.
  • Cartwright, D.S. (1957). “A Computational Procedure for Tau Correlation”, Psychometrika, 22, 97-104.
  • Ceballos, B., Lamata, M.T. ve Pelta, D.A. (2016). “A Comparative Analysis of Multi-Criteria Decision-Making Methods”, Progress in Artificial Intelligence, 5, 315-322.
  • Collins, L.M., Schafer, J.L. ve Kam, C.M. (2001). “A Comparison of Inclusive and Restrictive Strategies in Modern Missing Data Procedures”, Psychological Methods, Dec, 6 (4), 330-351.
  • Delen, D., Kuzey, C. ve Uyar, A. (2013). “Measuring Firm Performance Using Financial Ratios: A Decision Tree Approach”, Expert Systems with Applications, 40, 3970-3983.
  • Dong, Y. ve Peng, C.Y.J. (2013). “Principled Missing Data Methods for Researchers”, SpringerPlus, 2, 222.
  • Doumpos, M. ve Zopounidis, C. (2010). “A Multicriteria Decision Support System for Bank Rating”, Decision Support Systems, 50(1), 55-63.
  • Doumpos, M., Kosmidou, K., Baourakis, G. ve Zopounidis, C. (2002). “Credit Risk Assessment Using a Multicriteria Hierarchical Discrimination Approach: A Comparative Analysis”, European Journal of Operational Research, 138(2), 392-412.
  • Feng, C.M. ve Wang, R.T. (2000). “Performance Evaluation for Airlines Including the Consideration of Financial Ratios”, Journal of Air Transport Management, 6(3), 133-142.
  • Feng, S., Xinsong, M., Zhiyong, L., Zeshui, X. ve Dongliang, C. (2018). “An Extended Intuitionistic Fuzzy TOPSIS Method Based on a New Distance Measure with an Application to Credit Risk Evaluation”, Information Sciences 428, 105-119.
  • Ferreira, F.A.F., Santos, S.P., Marques, C.S.E. ve Ferreira, J. (2014). “Assessing Credit Risk of Mortgage Lending Using MACBETH: A Methodological Framework”, Management Decision, 52(2), 182-206. Forthofer, R.N. ve Lehnen, R.G. (1981). “Rank Correlation Methods”, Public Program Analysis, Springer, Boston, M.A.
  • Görener, A., Dinçer, H. ve Hacioglu, U. (2013). “Application of Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) Method for Bank Branch Location Selection”, International Journal of Finance & Banking Studies, 2147-4486, 2(2), 41-52.
  • Graham, J.W. (2009). “Missing Data Analysis: Making it Work in the Real World”, Annual Review of Psychologie, 60, 549-576.
  • Guo, Z., Wan, Y. ve Ye, H. (2019). “A Data Imputation Method for Multivariate Time Series Based on Generative Adversarial Network”, Neurocomputing, 360, 185-197.
  • Güden, M. (2021). “Metal Eşya Endeksine Kayıtlı Şirketlerin Finansal Performanslarının Bulut Endeks Performans Yöntemiyle Değerlendirilmesi”, Yüksek Lisans Tezi, Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü, Aydın.
  • Güney, Y., Hernandez-Perdomo, E. ve Rocco, C.M. (2020). “Does Relative Strength in Corporate Governance Improve Corporate Performance? Empirical Evidence Using MCDA Approach”, Journal of the Operational Research Society, 71(10), 1593-1618.
  • Hallerbach, W.G. ve Spronk, J. (2002). “The Relevance of MCDM for Financial Decisions”, Journal of Multi-Criteria Decision Analysis, 11, 187-195.
  • Hamed, K.H. (2011). “The Distribution of Kendall's Tau for Testing the Significance of Cross-Correlation in Persistent Data”, Hydrological Sciences Journal, 56(5), 841-853.
  • Horrigan, J.O. (1968). “A Short History of Financial Ratio Analysis”, The Accounting Review, 43(2), 284-294. IBM Corp. Released (2015). “IBM SPSS Statistics for Windows”, Version 23.0, Armonk, IBM Corp., N.Y.
  • Ishizaka, A. ve Siraj, S. (2018). “An Experimental Comparative Study of Three Methods”, European Journal of Operational Research, 264(2), 462-471.
  • Kang, H. (2013). “The Prevention and Handling of the Missing Data”, Korean Journal of Anesthesiology, 64(5), 402-406.
  • Kendall, M.G. (1938). “A New Measure of Rank Correlation”, Biometrika, 30(1/2), 81-93.
  • Kıran, Ş. (2018). “Sağlık kurumları finansal Çizelge analizlerinde kullanılabilecek anahtar finansal oranların belirlenmesi: Bir performans endeksi önerisi”, Yüksek Lisans Tezi, Kahramanmaraş Sütçü İmam Üniversitesi Sosyal Bilimler Enstitüsü, Kahramanmaraş.
  • Lakshminarayan, K., Harp, S.A. ve Samad, T. (1999). “Imputation of Missing Data in Industrial Databases”, Applied Intelligence, 11, 259-275.
  • Lee, P.T-W. ve Lin, C-W. (2013). “The Cognition Map of Financial Ratios of Shipping Companies Using DEMATEL and MMDE”, Maritime Policy & Management, 40(2), 133-145.
  • Locurcio, M., Tajani, F., Morano, P., Anelli, D. ve Manganelli, B. (2021). “Credit Risk Management of Property Investments through Multi-Criteria Indicators”, Risks, 9(6),106.
  • Long, J.D. ve Cliff, N. (1997). “Confidence Intervals for Kendall's Tau”, British Journal of Mathematical and Statistical Psychology, 50, 31-41.
  • Maricica, M. ve Georgeta, V. (2012). “Business Failure Risk Analysis Using Financial Ratios”, Procedia- Social and Behavioral Sciences, 62, 728-732.
  • Microsoft Corporation (2018). “Microsoft Excel”. URL: https://office.microsoft.com/excel.
  • Mousavi, M.M. ve Lin, J. (2020). “The Application of PROMETHEE Multi-Criteria Decision Aid in Financial Decision Making: Case of Distress Prediction Models Evaluation”, Expert Systems with Applications, 159, 113438.
  • Ohlson, J.A. (1980). “Financial Ratios and the Probabilistic Prediction of Bankruptcy”, Journal of Accounting Research, 18(1), 109-131.
  • Opricovic, S. ve Tzeng, G. (2004). “Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS”, European Journal of Operational Research, 156, 445-455.
  • Paradowski, B., Shekhovtsov, A., Baczkiewicz, A., Kizielewicz, B. ve Sałabun, W. (2021). “Similarity Analysis of Methods for Objective Determination of Weights in Multi-Criteria Decision Support Systems”, Symmetry 2021, 13, 1874.
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There are 66 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Mehmet Top 0000-0001-9162-4238

Tevfik Bulut 0000-0002-3668-7436

Publication Date July 30, 2022
Submission Date December 2, 2021
Published in Issue Year 2022

Cite

APA Top, M., & Bulut, T. (2022). YENİ BİR ÇOK KRİTERLİ KARAR VERME YÖNTEMİ: BULUT ENDEKS-BETA (BE-β). Verimlilik Dergisi(3), 393-414. https://doi.org/10.51551/verimlilik.1031366

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