Gülçin BÜYÜKÖZKAN, Merve GÜLER, Esin MUKUL, Fethullah GÖÇER

ENTEGRE BULANIK ÇKKV YAKLAŞIMIYLA TEDARİK ZİNCİRİ ANALİTİĞİNİN DEĞERLENDİRİLMESİ

Son zamanlarda, teknolojideki hızlı ve çarpıcı gelişmelerle birlikte büyük veri ve iş analitiğinin popülerliği artmıştır. Tedarik zinciri analitiği (TZA) kavramı, dijital teknolojilerin her geçen gün daha küresel, daha karmaşık, daha kapsamlı ve daha bağlantılı hale gelen tedarik zincirlerinde uygulanması ile ortaya çıkmıştır. TZA, istatistik, matematik, makine öğrenme teknikleri ve öngörücü modelleme uygulamasıyla tedarik zinciri süreçlerinde çıkarım yapmayı amaçlamaktadır. Bu bağlamda, şirketler tedarik zincirleri için TZA'yı kullanarak işlerine değer yaratmanın yollarını bulmaya çalışmaktadır. Ancak, en uygun TZA aracının seçimi, kararı etkileyen birçok faktör içeren karmaşık bir süreçtir. Örneğin, grafiksel ve sezgisel özellikler, veri çıkarma yöntemi ve gerçek zamanlı çalışabilirlik, bu seçimi etkileyen faktörler olabilir. Bu nedenle, bu çalışmada, TZA başarı faktörlerini belirlemek ve TZA araçlarını değerlendirmek için entegre bir teknik sunmak amaçlanmıştır. Bu problemin çözümü için bulanık mantık ve çok kriterli karar verme (ÇKKV) teknikleri kullanılmıştır. Entegre bulanık basit toplamlı ağırlıklandırma (SAW) - TOPSIS yaklaşımı uygulanmıştır. Başarı faktörlerinin ağırlıkları bulanık SAW tekniği kullanılarak hesaplanmış ve TZA araçları bulanık TOPSIS tekniği ile değerlendirilerek sıralanmıştır. Başarı faktörleri ve TZA aracı alternatifleri akademik yazın ve endüstri raporları gözden geçirilerek belirlenmiş ve uzmanların görüşleri ile finalize edilmiştir. Önerilen yaklaşımın potansiyelini göstermek için bir uygulama gerçekleştirilmiş; çalışmanın sonunda ise gelecek çalışmalar için öneriler sunulmuştur.

EVALUATION OF SUPPLY CHAIN ANALYTICS WITH AN INTEGRATED FUZZY MCDM APPROACH

Recently, the popularity of big data and business analytics has increased with advancedtechnological developments. Supply chain analytics (SCA) notion was born with theimplementation of these technologies in supply chains that become more global, more complex,more extended, and more connected each day. SCA aims to find meaningful patterns in supplychain processes with the application of statistics, mathematics, machine-learning techniques,and predictive modeling. In this context, companies try to find ways to create business value fortheir supply chains by leveraging SCA. However, the selection of the most appropriate SCAtool is a complicated process that contains many influencing factors. For instance, the graphicaland intuitive features, the data extraction method and real-time operability can be theinfluencing factors for such a selection. Therefore, in this study, it is aimed to provide anintegrated technique for prioritizing SCA success factors and for evaluating SCA tools. Foraddressing these problems, fuzzy logic and multi-criteria decision making (MCDM) techniquesare used. An integrated fuzzy simple additive weighting (SAW) - a technique for orderpreference by similarity to ideal solution (TOPSIS) approach is applied. The weights of thesuccess factors are calculated by using fuzzy SAW technique, and the SCA tools are evaluatedby using fuzzy TOPSIS technique. The success factors and the SCA tool alternatives aredetermined by reviewing the literature and industry reports, and by collecting experts' opinions.An application is given to illustrate the potential of the proposed approach. At the end of thestudy, the suggestions for future studies are presented.

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