Bulanık FUCOM Metodu ile Tedarikçi Değerlendirme Kriterlerinin Ağırlıklarının Belirlenmesi

Year 2021, Issue: 28, 863 - 868, 30.11.2021

Merve Ceren Taşkent , Elif Kılıç Delice

https://doi.org/10.31590/ejosat.1011756

Cited By: 4

Abstract

E-ticaret ile birlikte firmaların ulaşabileceği pazar alanı uluslararası alana taşınmış ve bu durum firmaları, rekabet ortamında başarılı olabilmek için çeşitli iyileştirme çalışmalarını gerçekleştirmeye itmiştir. Bu iyileştirme çalışmalarından bir tanesi tedarik zinciri ağı içerisinde yer alan ve tedarik zinciri sürecini başlatan tedarikçilerdir. Tedarikçiler ürünün kalitesini, üretim süresini, üretim maliyetini ve hatta nihai ürünün son müşteriye teslimine kadar olan süreci doğrudan etkilemektedir. Bu sebeple işletmeler için uygun tedarikçileri seçmek veya mevcut tedarikçilerin performanslarını değerlendirmek için çaba göstermektedirler. Tedarikçi seçim ve değerlendirmesi sürecinde kullanılacak kriterler ve bu kriterlerin önem ağırlıklarının son derece etkilidir. Bu çalışmada, tedarikçi seçim sürecinde kriter ağırlıklarının belirlenmesi için bulanık Full Consistency Method (FUCOM) metodu kullanılmıştır. Literatürde genellikle karar vericilerin sübjektif değerlendirmelerine bağlı olarak Çok Kriterli Karar Verme (ÇKKV) yöntemleri ile uygun kriter ağırlıkları belirlenmekte ve kesin sonuçlar elde edilememektedir. Bu çalışmada ise Bulanık FUCOM metodu ile sübjektif değerlendirmeler kullanılarak optimal kriter ağırlıkları belirlenmiştir. Dekoratif taş sağlayıcısı bir firmanın mevcut tedarikçilerinin değerlendirilmesi üzerine bir uygulama çalışması yapılmıştır.

Keywords

Bulanık FUCOM, Tedarikçi Değerlendirmesi, Kriter Ağırlıklandırması.

Determination the Weight of Supplier Evaluation Criteria with Fuzzy FUCOM Method

Abstract

With e-commerce, the market area that companies can reach has been moved to the international arena and this situation has pushed companies to carry out various improvement studies in order to be successful in the competitive environment. One of these improvement efforts is the suppliers that are in the supply chain network and start the supply chain process. Suppliers directly affect the quality of the product, production time, production cost and even the process until the delivery of the final product to the end customer. For this reason, they strive to select suitable suppliers for businesses or to evaluate the performance of existing suppliers. The criteria to be used in the supplier selection and evaluation process and the importance weights of these criteria are extremely effective. In this study, the fuzzy Full Consistency Method (FUCOM) method was used to determine the criterion weights in the supplier selection process. In the literature, appropriate criteria weights are determined by Multi-Criteria Decision Making (MCDM) methods, depending on the subjective evaluations of decision makers, and definite results cannot be obtained. In this study, the optimal criterion weights were determined by using the fuzzy FUCOM method and subjective evaluations. An application study was conducted on the evaluation of the existing suppliers of a decorative stone supplier company.

Keywords

Fuzzy FUCOM, Supplier Evaluation, Criteria Weighting.

References

• Abdollahi, M., Arvan, M., & Razmi, J., 2015. An integrated approach for supplier portfolio selection: lean or agile. Expert Systems with Applications, 42(1), 679-690. doi:10.1016/j.eswa.2014.08.019
• Alavi, B., Tavana, M., & Mina, H., 2021. A dynamic decision support system for sustainable supplier selection in circular economy. Sustainable Production and Consumption, 27, 905-920. doi:10.1016/j.spc.2021.02.015
• Cao, Esangbedo, Bai, & Esangbedo., 2019. Grey SWARA-FUCOM weighting method for contractor selection mcdm problem: a case study of floating solar panel energy system ınstallation. Energies, 12(13). doi:10.3390/en12132481
• Cengiz, A. E., Aytekin, O., Ozdemir, I., Kusan, H., & Cabuk, A., 2017. A multi-criteria decision model for construction material supplier selection. Procedia Engineering, 196, 294-301. doi:10.1016/j.proeng.2017.07.202
• Chou, Y.-C., Sun, C.-C., & Yen, H.-Y., 2012. Evaluating the criteria for human resource for science and technology (HRST) based on an integrated fuzzy AHP and fuzzy DEMATEL approach. Applied Soft Computing, 12(1), 64-71. doi:10.1016/j.asoc.2011.08.058
• Dargi, A., Anjomshoae, A., Galankashi, M. R., Memari, A., & Tap, M. B. M., 2014. Supplier selection: a Fuzzy-ANP approach. Procedia Computer Science, 31, 691-700. doi:10.1016/j.procs.2014.05.317
• Dickson, G. W. (1966). An analysis of vendor selection systems and decisions. Journal of purchasing, 2(1), 5-17.
• Durmić, E., Stević, Ž., Chatterjee, P., Vasiljević, M., & Tomašević, M., 2020. Sustainable supplier selection using combined FUCOM – Rough SAW model. Reports in Mechanical Engineering, 1(1), 34-43. doi:10.31181/rme200101034c
• Dweiri, F., Kumar, S., Khan, S. A., & Jain, V., 2016. Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications, 62, 273-283. doi:10.1016/j.eswa.2016.06.030
• Erceg, Ž., & Mularifović, F., 2019. Integrated MCDM model for processes optimization in the supply chain management in the wood company. Operational Research in Engineering Sciences: Theory and Applications, 2(1). doi:10.31181/oresta1901015e
• Govindan, K., Kadziński, M., & Sivakumar, R., 2017. Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain. Omega, 71, 129-145. doi:10.1016/j.omega.2016.10.004
• Govindan, K., Khodaverdi, R., & Jafarian, A., 2013. A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. Journal of Cleaner Production, 47, 345-354. doi:10.1016/j.jclepro.2012.04.014
• Gören, H. G., 2018. A decision framework for sustainable supplier selection and order allocation with lost sales. Journal of Cleaner Production, 183, 1156-1169. doi:10.1016/j.jclepro.2018.02.2
• Guo, X., Yuan, Z., & Tian, B., 2009. Supplier selection based on hierarchical potential support vector machine. Expert Systems with Applications, 36(3), 6978-6985. doi:10.1016/j.eswa.2008.08.074
• Hu, K.-J., & Yu, V. F., 2016. An integrated approach for the electronic contract manufacturer selection problem. Omega, 62, 68-81. doi:10.1016/j.omega.2015.08.010
• Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A., & Diabat, A., 2013. Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain. Journal of Cleaner Production, 47, 355-367. doi:10.1016/j.jclepro.2013.02.010
• Kumar, S., Kumar, S., & Barman, A. G., 2018. Supplier selection using fuzzy TOPSIS multi criteria model for a small scale steel manufacturing unit. Procedia Computer Science, 133, 905-912.
• Lam, K.-C., Tao, R., & Lam, M. C.-K., 2010. A material supplier selection model for property developers using Fuzzy Principal Component Analysis. Automation in Construction, 19(5), 608-618. doi:10.1016/j.autcon.2010.02.007
• Liao, C.-N., & Kao, H.-P., 2010. Supplier selection model using Taguchi loss function, analytical hierarchy process and multi-choice goal programming. Computers & Industrial Engineering, 58(4), 571-577. doi:10.1016/j.cie.2009.12.004
• Liao, C.-N., & Kao, H.-P., 2011. An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management. Expert Systems with Applications, 38(9), 10803-10811. doi:10.1016/j.eswa.2011.02.031
• Lin, Y.-T., Lin, C.-L., Yu, H.-C., & Tzeng, G.-H., 2010. A novel hybrid MCDM approach for outsourcing vendor selection: a case study for a semiconductor company in Taiwan. Expert Systems with Applications, 37(7), 4796-4804. doi:10.1016/j.eswa.2009.12.036
• Liu, Y., Eckert, C., Yannou-Le Bris, G., & Petit, G., 2019. A fuzzy decision tool to evaluate the sustainable performance of suppliers in an agrifood value chain. Computers & Industrial Engineering, 127, 196-212. doi:10.1016/j.cie.2018.12.022
• Matić, B., Jovanović, S., Das, D. K., Zavadskas, E. K., Stević, Ž., Sremac, S., & Marinković, M., 2019. A new hybrid mcdm model: sustainable supplier selection in a construction company. Symmetry, 11(3). doi:10.3390/sym11030353
• Mubarik, M. S., Kazmi, S. H. A., & Zaman, S. I., 2021. Application of gray DEMATEL-ANP in green-strategic sourcing. Technology in Society, 64. doi:10.1016/j.techsoc.2020.101524
• Nielsen, I. E., Banaeian, N., Golińska, P., Mobli, H., & Omid, M., 2014. Green Supplier Selection Criteria: From a Literature Review to a Flexible
• Pamučar, D., & Ecer, F., 2020. Prioritizing the weights of the evaluation criteria under fuzziness: the fuzzy full consistency method – FUCOM-F. Facta Universitatis, Series: Mechanical Engineering, 18(3). doi:10.22190/fume200602034p
• Pamučar, D., Deveci, M., Canıtez, F., & Božanić, D., 2020. A fuzzy Full Consistency Method-Dombi-Bonferroni model for prioritizing transportation demand management measures. Applied Soft Computing, 87. doi:10.1016/j.asoc.2019.105952,
• Pamučar, D., Stević, Ž., & Sremac, S., 2018. A new model for determining weight coefficients of criteria in MCDM models: full consistency method (FUCOM). Symmetry, 10(9). doi:10.3390/sym10090393
• Rezaei, J., Fahim, P. B. M., & Tavasszy, L., 2014. Supplier selection in the airline retail industry using a funnel methodology: Conjunctive screening method and fuzzy AHP. Expert Systems with Applications, 41(18), 8165-8179. doi:10.1016/j.eswa.2014.07.005
• Rouyendegh, B. D., & Saputro, T. E., 2014. Supplier selection using ıntegrated fuzzy TOPSIS and MCGP: a case study. Procedia - Social and Behavioral Sciences, 116, 3957-3970. doi:10.1016/j.sbspro.2014.01.874
• Stević, Ž., Durmić, E., Gajić, M., Pamučar, D., & Puška, A., 2019. a novel multi-criteria decision-making model: ınterval rough SAW method for sustainable supplier selection. Information, 10(10). doi:10.3390/info10100292
• Sureeyatanapas, P., Sriwattananusart, K., Niyamosoth, T., Sessomboon, W., & Arunyanart, S., 2018. Supplier selection towards uncertain and unavailable information: an extension of TOPSIS method. Operations Research Perspectives, 5, 69-79. doi:10.1016/j.orp.2018.01.005