Ana Sayfa Manşet Temel Eczacılık Ürünlerinin ve Eczacılığa İlişkin Malzemelerin İmalatı sektörünün analizi

Temel Eczacılık Ürünlerinin ve Eczacılığa İlişkin Malzemelerin İmalatı sektörünün analizi

Yazar
winally
-
Paylaş Paylaş Paylaş Paylaş

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

Mehmet TOP & Tevfik BULUT

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.

Bu çalışma Verimlilik Dergisi‘nin 2022 yılı 3. sayısında yayınlanmıştır.

Tevfik Bulut

Anahtar Kelimeler

Çok Kriterli Karar Verme, ÇKKV, Bulut Endeksi, BE, Bulut Endeks-Beta, BE-β

Kaynakça

  • 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.
  • Bülbül, S. ve Köse, A. (2011). “Türk Gıda Şirketlerinin Finansal Performansının Çok Amaçlı Karar Verme Yöntemleriyle Değerlendirilmesi”, Atatürk Üniversitesi İİBF Dergisi, Ekonometri ve İstatistik Sempozyumu Özel Sayısı, 25, 71-97.
  • 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.
  • Pätäri, E., Karell, V., Luukka, P. ve Yeomans, J.S. (2018). “Comparison of the Multicriteria Decision-Making Methods for Equity Portfolio Selection: The U.S. Evidence”, European Journal of Operational Research, 265(2), 655-672.
  • Pohekar, S.D. ve Ramachandran, M. (2004). “Application of Multi-Criteria Decision Making to Sustainable Energy Planning, A Review”, Renewable & Sustainable Energy Reviews, 8, 365-381.
  • Puth, M., Neuhäuser, M. ve Ruxton, G.D. (2015). “Effective Use of Spearman’s and Kendall’s Correlation Coefficients for Association between two Measured Traits”, Animal Behaviour, 102, 77-84.
  • Rahman, M.G. ve Islam, M.Z. (2013). “Missing Value Imputation Using Decision Trees and Decision Forests by Splitting and Merging Records: Two Novel Techniques”, Knowledge-Based Systems, 53, 51-65.
  • Sałabun, W. ve Urbaniak, K. (2020). “A New Coefficient of Rankings Similarity in Decision-Making Problems”, In: Krzhizhanovskaya V. et al. (eds) Computational Science-ICCS 2020, Lecture Notes in Computer Science, 12138.
  • Sanayi ve Teknoloji Bakanlığı (STB), (2021a). “Girişimci Bilgi Sistemi (GBS)”. URL: https://gbs.sanayi.gov.tr/docs/GBS_Metaveri.pdf. (Erişim Tarihi: 17.10.2021).
  • Sanayi ve Teknoloji Bakanlığı (STB), (2021b). “Girişimci Bilgi Sistemi (GBS)”, (Erişim Tarihi: 24.09.2021).
  • Schafer, J.L. ve Olsen, M.K. (1998). “Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst’s Perspective”, Multivariate Behavioral Research, 33(4), 545-571.
  • Sedgwick, P. (2014). Spearman’s Rank Correlation Coefficient, The BMJ, 349, g7327.
  • Shekhovtsov, A. (2021). “How Strongly Do Rank Similarity Coefficients Differ Used in Decision Making Problems?”, Procedia Computer Science, 192, 4570-4577.
  • Sinharay, S., Stern, H.S. ve Russell, D. (2001). “The Use of Multiple Imputation for the Analysis of Missing Data”, Psychol Methods, 6(4), 317-29.
  • Silva-Ramírez, E., Pino-Mejías, R. ve López-Coello, M. (2015). “Single Imputation with Multilayer Perceptron and Multiple Imputation Combining Multilayer Perceptron and K-Nearest Neighbours for Monotone Patterns”, Applied Soft Computing, 29, 65-74.
  • Spathis, C., Kosmidou, K. ve Doumpos, M. (2002). “Assessing Profitability Factors in the Greek Banking System: A Multicriteria Methodology”, International Transactions in Operational Research, 9, 517-530.
  • Spearman, C. (1987). “The Proof and Measurement of Association between Two Things”, The American Journal of Psychology, 100 (3/4), 441-471.
  • Taylor, J.M.G. (1987). “Kendall’s and Spearman’s Correlation Coefficients in the Presence of a Blocking Variable”, Biometrics, 43(2), 409-416.
  • Uygurtürk, H. ve Korkmaz, T. (2012). “Finansal Performansın TOPSIS Çok Kriterli Karar Verme Yöntemi ile Belirlenmesi: Ana Metal Sanayi İşletmeleri Üzerine Bir Uygulama”, Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 7(2), 95-115.
  • Wang, J-J., Jing, Y-Y., Zhang, C-F. ve Zhao, J-H. (2009). “Review on Multi-Criteria Decision Analysis Aid in Sustainable Energy Decision-Making”, Renewable and Sustainable Energy Reviews,13(9), 2263-2278.
  • Wang, Y. (2009). “Combining Grey Relation Analysis with FMCGDM to Evaluate Financial Performance of Taiwan Container Lines”, Expert Systems with Applications, Part 1, 36(2), 2424-2432.
  • Wang, Y. (2014). “The Evaluation of Financial Performance for Taiwan Container Shipping Companies by Fuzzy TOPSIS”, Applied Soft Computing, 22, 28-35.
  • Wanke, P., Abul Kalam Azad, M.D. ve Barros, C.P. (2016). “Predicting Efficiency in Malaysian Islamic Banks: A Two-Stage TOPSIS and Neural Networks Approach”, Research in International Business and Finance, 2016, 36, 485-498.
  • Wu, W., Sukoco, B.M., Li, C., ve Chen, S.H. (2009). “An Integrated Multi-Objective Decision-Making Process for Supplier Selection with Bundling Problem”, Expert Systems with Applications, 36(2), 2327-2337.
  • Xidonas, P., Mavrotas, G. ve Psarras, J.E. (2009). “A Multicriteria Methodology for Equity Selection Using Financial Analysis”, Computers & Operations Research, 36(12), 3187-3203.