Mehmet ERDEM, Turan Erman ERKAN

TEDARİK ZİNCİRİ PERFORMANSININ ENTEGRE İKİ AŞAMALI KÜMELEME VE ARALIK TİP-2 BULANIK TOPSİS YÖNTEMİ KULLANARAK DEĞERLENDİRİLMESİ: BİR VAKA ÇALIŞMASI

Tedarik zinciri (TZ) yönetiminde birçok araştırmacı ve kuruluş sistemin performansını iyileştirmek için farklı perspektiflerde çaba sarfetmektedir. Farklı sektörlerde TZ performansını değerlendirmek için çeşitli metrikler ve karar verme metodolojileri önerilmiştir. Bu çalışmada, TZ performans değerlendirme süreçleri için bütünleşik iki aşamalı kümeleme ve aralık tip-2 bulanık TOPSIS yöntemi önerilmektedir. Önerilen bu yaklaşımın ilk adımında, iki aşamalı kümeleme analizi ile sektörlerin homojen olarak bölümlendirilmesi ve aynı zamanda problemin boyutunun azaltılması sağlanmaktadır. İkinci adımda, bulanık TOPSIS ile oluşturulan kümeler değerlendirilmektedir. Yaklaşımın sonuçları, örgütsel etkinlik ve şirket performansı gibi konularda makro bakış açısı sağlamaktadır. Ayrıca sonuçlar, her şirketin bulunduğu kümelerdeki rakipleri arasında ve diğer sektörlerdeki rakiplere karşı kendini değerlendirme fırsatı sağlamaktadır.

EVALUATION OF SUPPLY CHAIN PERFORMANCE USING AN INTEGRATED TWO-STEP CLUSTERING AND INTERVAL TYPE-2 FUZZY TOPSIS METHOD: A CASE STUDY

Supply chain management (SCM) is an important subject for many researchers and organizations striving to improveperformance within different contexts. Various metrics and decision making methodologies have been proposed to evaluatesupply chain (SC) performance in different sectors. This paper introduces an integration of the Two-Step Clustering and theinterval type-2 (IT2) Fuzzy TOPSIS methods for the SC performance evaluation process. In the first step of the proposedintegrated approach, Two-Step Clustering analysis (CA) is employed not only to classify the sectors, i.e., manufacturingand service, but also to decrease the dimension of the problem. After obtaining the results, IT2 Fuzzy TOPSIS is used toevaluate each company within its cluster. The results of the integrated approach offer a macro perspective on some issues,such as organizational efficiency and performance. Moreover, valuable insight is provided that each company can have theopportunity to evaluate itself both against the rivals within clusters and inter-sectoral rivals.

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Ahi, P., and Searcy, C. (2015). “Assessing sustainability in the supply chain: A triple bottom line approach”. Applied Mathematical Modelling, 39(10), 2882-2896.
Akman, G. (2015). “Evaluating suppliers to include green supplier development programs via fuzzy c-means and VIKOR methods”. Computers & Industrial Engineering, 86, 69-82.
Akyuz, G. A., and Erkan, T. E. (2010). “Supply chain performance measurement: a literature review”. International Journal of Production Research, 48(17), 5137-5155.
Bai, C. G., Dhavale, D., and Sarkis, J. (2014). “Integrating Fuzzy C-Means and TOPSIS for performance evaluation: An application and comparative analysis”. Expert Systems with Applications, 41(9), 4186-4196.
Bernardes, E. S., and Zsidisin, G. A. (2008). “An examination of strategic supply management benefits and performance implications”. Journal of Purchasing and Supply Management, 14(4), 209-219.
Boley, D., Gini, M., Gross, R., Han, E.-H. S., Hastings , K., Karypis , G., Kumar , V., Mobasher, B., and Moore, J. (1999). “Partitioning-Based Clustering for Web Document Categorization “. Decision Support Systems, 27, 329-341.
Boran, F. E., Genc, S., Kurt, M., and Akay, D. (2009). “A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method”. Expert Systems with Applications, 36(8), 11363-11368.
Bottani, E., and Rizzi, A. (2008). “An adapted multi-criteria approach to suppliers and products selection - An application oriented to lead-time reduction”. International Journal of Production Economics, 111(2), 763- 781.
Buyukozkan, G., and Cifci, G. (2012). “A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers”. Expert Systems with Applications, 39(3), 3000-3011.
Celik, E., and Akyuz, E. (2018). “An interval type-2 fuzzy AHP and TOPSIS methods for decision-making problems in maritime transportation engineering: The case of ship loader”. Ocean Engineering, 155, 371-381.
Chan, F. T. S. (2003). “Performance Measurement in a Supply Chain”. The International Journal of Advanced Manufacturing Technology, 21(7), 534-548.
Che, Z. H., and Wang, H. S. (2010). “A hybrid approach for supplier cluster analysis”. Computers & Mathematics with Applications, 59(2), 745-763.
Chen, C. T. (2000). “Extensions of the TOPSIS for group decision-making under fuzzy environment”. Fuzzy Sets and Systems, 114(1), 1-9.
Chen, C. T., Lin, C. T., and Huang, S. F. (2006). “A fuzzy approach for supplier evaluation and selection in supply chain management”. International Journal of Production Economics, 102(2), 289-301.
Chen, S.-M., and Lee, L.-W. (2010). “Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method”. Expert Systems with Applications, 37(4), 2790-2798.
Chiu, T., Fang, D., Chen, J., Wang, Y., and Jeris, C. (2001). “A robust and scalable clustering algorithm for mixed type attributes in large database environment”. Paper presented at the Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining San Francisco.
Ding, H. W., Benyoucef, L., and Xie, X. L. (2005). “A simulation optimization methodology for supplier selection problem”. International Journal of Computer Integrated Manufacturing, 18(2-3), 210-224.
Dos Santos, B. M., Godoy, L. P., and Campos, L. M. S. (2019). “Performance evaluation of green suppliers using entropy-TOPSIS-F”. Journal of Cleaner Production, 207, 498-509.
Estampe, D., Lamouri, S., Paris, J.-L., and Brahim-Djelloul, S. (2013). “A framework for analysing supply chain performance evaluation models”. International Journal of Production Economics, 142(2), 247-258.
Faez, F., Ghodsypour, S. H., and O’Brien, C. (2009). “Vendor selection and order allocation using an integrated fuzzy case-based reasoning and mathematical programming model”. International Journal of Production Economics, 121(2), 395-408.
Fu, G. (2008). “A fuzzy optimization method for multicriteria decision making: An application to reservoir flood control operation”. Expert Systems with Applications, 34(1), 145-149.
Gencer, C., and Gürpinar, D. (2007). “Analytic network process in supplier selection: A case study in an electronic firm”. Applied Mathematical Modelling, 31(11), 2475-2486.
Ghodsypour, S. H., and O’Brien, C. (2001). “The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint”. International Journal of Production Economics, 73(1), 15-27.
Gong, Y., Yang, S., Dai, L., and Hu, N. (2017). A new approach for ranking of interval type-2 trapezoidal fuzzy numbers (Vol. 32).
Gunasekaran, A., and Kobu, B. (2007). “Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications”. International Journal of Production Research, 45(12), 2819-2840.
Gunasekaran, A., Patel, C., and McGaughey, R. E. (2004). “A framework for supply chain performance measurement”. International Journal of Production Economics, 87(3), 333-347.
Gunasekaran, A., Williams, H. J., and McGaughey, R. E. (2005). “Performance measurement and costing system in new enterprise”. Technovation, 25(5), 523-533.
Guo, X., Yuan, Z., and Tian, B. (2009). “Supplier selection based on hierarchical potential support vector machine”. Expert Systems with Applications, 36(3, Part 2), 6978-6985.
Heckmann, I., Comes, T., and Nickel, S. (2015). “A critical review on supply chain risk – Definition, measure and modeling”. Omega, 52, 119-132.
Heidarzade, A., Mandavi, I., and Mandavi-Amiri, N. (2016). “Supplier selection using a clustering method based on a new distance for interval type-2 fuzzy sets: A case study”. Applied Soft Computing, 38, 213-231.
Ho, C.-J. (2007). “Measuring system performance of an ERP-based supply chain”. International Journal of Production Research, 45(6), 1255-1277.
Ho, W., Xu, X. W., and Dey, P. K. (2010). “Multi-criteria decision making approaches for supplier evaluation and selection: A literature review”. European Journal of Operational Research, 202(1), 16-24.
Hong, G. H., Park, S. C., Jang, D. S., and Rho, H. M. (2005). “An effective supplier selection method for constructing a competitive supply-relationship”. Expert Systems with Applications, 28(4), 629-639.
Hwang, C.-L., and Yoon, K. (1981). Multiple attributes decision making methods and applications. Berlin: SpringerVerlag Berlin Heidelberg.
Kahraman, C., Öztayşi, B., Uçal Sarı, İ., and Turanoğlu, E. (2014). “Fuzzy analytic hierarchy process with interval type-2 fuzzy sets”. Knowledge-Based Systems, 59, 48-57.
Khan, S. A., Kusi-Sarpong, S., Arhin, F. K., and Kusi-Sarpong, H. (2018). “Supplier sustainability performance evaluation and selection: A framework and methodology”. Journal of Cleaner Production, 205, 964-979.
Kirytopoulos, K., Leopoulos, V., and Voulgaridou, D. (2008). “Supplier selection in pharmaceutical industry: An analytic network process approach”. Benchmarking: An International Journal, 15(4), 494-516.
Li-Wei, L., and Shyi-Ming, C. (2008, 12-15 July 2008). “Fuzzy multiple attributes group decision-making based on the extension of TOPSIS method and interval type-2 fuzzy sets”. Paper presented at the Machine Learning and Cybernetics, 2008 International Conference on.
Li, S., Ragu-Nathan, B., Ragu-Nathan, T. S., and Subba Rao, S. (2006). “The impact of supply chain management practices on competitive advantage and organizational performance”. Omega, 34(2), 107-124.
Lima-Junior, F. R., and Carpinetti, L. C. R. (2017). “Quantitative models for supply chain performance evaluation: A literature review”. Computers & Industrial Engineering, 113, 333-346.
Lima Junior, F. R., Osiro, L., and Carpinetti, L. C. R. (2014). “A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection”. Applied Soft Computing, 21, 194-209.
Lin, C.-J., and Wu, W.-W. (2008). “A causal analytical method for group decision-making under fuzzy environment”. Expert Systems with Applications, 34(1), 205-213.
Lin, R. H. (2009). “An integrated FANP-MOLP for supplier evaluation and order allocation”. Applied Mathematical Modelling, 33(6), 2730-2736.
Liu, J., Ding, F. Y., and Lall, V. (2000). “Using data envelopment analysis to compare suppliers for supplier selection and performance improvement”. Supply Chain Management: An International Journal, 5(3), 143-150.
Madhukumar, S., and Santhiyakumari, N. (2015). “Evaluation of k-Means and fuzzy C-means segmentation on MR images of brain”. The Egyptian Journal of Radiology and Nuclear Medicine, 46(2), 475-479.
Martin, P. R., and Patterson, J. W. (2009). “On measuring company performance within a supply chain”. International Journal of Production Research, 47(9), 2449-2460.
Matas, J., and Kittler , J. (1995). “Spatial and Feature Space Clustering: Applications in Image Analysis”. Paper presented at the CAIP Computer Analysis of Images and Patterns.
Mendel, J. M., John, R. I., and Feilong, L. (2006). “Interval Type-2 Fuzzy Logic Systems Made Simple”. Fuzzy Systems, IEEE Transactions on, 14(6), 808-821.
Michailidou, C., Maheras, P., Arseni-Papadimititriou, A., Kolyva-Machera, F., and Anagnostopoulou, C. (2009). “A study of weather types at Athens and Thessaloniki and their relationship to circulation types for the cold-wet period, part I: two-step cluster analysis”. Theoretical and Applied Climatology, 97(1-2), 163-177.
Mokhtarian, M. N., and Hadi-Vencheh, A. (2012). “A new fuzzy TOPSIS method based on left and right scores: An application for determining an industrial zone for dairy products factory”. Applied Soft Computing, 12(8), 2496-2505.
Panda, S., Sahu, S., Jena, P., and Chattopadhyay, S. (2012). “Comparing Fuzzy-C Means and K-Means Clustering Techniques: A Comprehensive Study”, Berlin, Heidelberg.
Peric, T., Babic, Z., and Veza, I. (2013). “Vendor selection and supply quantities determination in a bakery by AHP and fuzzy multi-criteria programming”. International Journal of Computer Integrated Manufacturing, 26(9), 816-829.
Petrakis, E. G. M., and Faloutsos, C. (1997). “Similarity Searching in Medical Image Databases”. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 9(3), 435-447.
Ramezankhani, M. J., Torabi, S. A., and Vahidi, F. (2018). “Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach”. Computers & Industrial Engineering, 126, 531-548.
Rezaei, J., and Ortt, R. (2012). “A multi-variable approach to supplier segmentation”. International Journal of Production Research, 50(16), 4593-4611.
Rezaei, J., and Ortt, R. (2013). “Multi-criteria supplier segmentation using a fuzzy preference relations based AHP”. European Journal of Operational Research, 225(1), 75-84.
Rezaei, J., and Ortt, R. (2013). “Supplier segmentation using fuzzy logic”. Industrial Marketing Management, 42(4), 507-517.
Saen, R. F. (2007). “Suppliers selection in the presence of both cardinal and ordinal data”. European Journal of Operational Research, 183(2), 741-747.
Satish, S. M., and Bharadhwaj, S. (2010). “Information search behaviour among new car buyers: A two-step cluster analysis”. IIMB Management Review, 22(1–2), 5-15.
Shemshadi, A., Shirazi, H., Toreihi, M., and Tarokh, M. J. (2011). “A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting”. Expert Systems with Applications, 38(10), 12160-12167.
Shyi-Ming, C. (1988). “A new approach to handling fuzzy decision-making problems”. Systems, Man and Cybernetics, IEEE Transactions on, 18(6), 1012-1016.
Talluri, S., and Narasimhan, R. (2003). “Vendor evaluation with performance variability: A max-min approach”. European Journal of Operational Research, 146(3), 543-552.
Tam, M. C. Y., and Tummala, V. M. R. (2001). “An application of the AHP in vendor selection of a telecommunications system”. Omega-International Journal of Management Science, 29(2), 171-182.
Tan, P.-N., Steinbach, M., and Kumar, V. (2005). Introduction to Data Mining Pearson.
Triantaphyllou, E., and Lin, C.-T. (1996). “Development and evaluation of five fuzzy multiattribute decisionmaking methods”. International Journal of Approximate Reasoning, 14(4), 281-310.
Tsabadze, T. (2006). “A method for fuzzy aggregation based on group expert evaluations”. Fuzzy Sets and Systems, 157(10), 1346-1361.
Wang, T.-C., and Chang, T.-H. (2007). “Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment”. Expert Systems with Applications, 33(4), 870-880.
Wang, Y.-M., and Parkan, C. (2005). “Multiple attribute decision making based on fuzzy preference information on alternatives: Ranking and weighting”. Fuzzy Sets and Systems, 153(3), 331-346.
Wang, Y.-M., and Parkan, C. (2006). “A general multiple attribute decision-making approach for integrating subjective preferences and objective information”. Fuzzy Sets and Systems, 157(10), 1333-1345.
Wu, D. (2013). Two Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers: Adaptiveness and Novelty. In A. Sadeghian, J. M. Mendel, & H. Tahayori (Eds.), Advances in Type-2 Fuzzy Sets and Systems: Theory and Applications (pp. 33-48). New York, NY: Springer New York.
Yager, R. R., and Xu, Z. (2006). “The continuous ordered weighted geometric operator and its application to decision making”. Fuzzy Sets and Systems, 157(10), 1393-1402.
Yao, K., and Liu, C. (2006). “An integrated approach for measuring supply chain performance”. Journal of Modern Accounting and Auditing, 2(10), 17.
Zadeh, L. A. (1965). “Fuzzy sets”. Information and Control, 8(3), 338-353.
Zamri, N., Naim, S., and Abdullah, L. (2015, 2-5 Aug. 2015). “A new linguistic scale for Interval Type-2 Trapezoidal Fuzzy Number based Multiple Criteria Decision Making method”. Paper presented at the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
Zeynel, C., and Figen, Y. (2015). “Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures”. Journal of Agricultural Informatics, 6, 13-23.
Zhang, T., Ramakrishnan, R., and Livny, M. (1996). “ACM SIGMOD international conference on Management of data “. Paper presented at the SIGMOD.