Journals / İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi / 2020 / Cilt: 19 - Sayı: 37

SUPPLIER SELECTİON BY FUZZY AHP AND FUZZY TOPSIS METHOD: AN APPLICATION IN RETAIL SECTOR

BULANIK AHP ve BULANIK TOPSIS YÖNTEMİ İLE TEDARİKÇİ SEÇİMİ: PERAKENDE SEKTÖRÜNDE BİR UYGULAMA

Pages
65–79
Publication Date
2020-06-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results were interpreted and recommendations were made.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Considering the supply chain as a whole and contributing to every link of the chain is an important factor in its management.
📊
Key Findings & Data:Businesses that want to make strategic decisions consider supplier selection as an important link.
💡
Conclusion & Impact:The results were interpreted and recommendations were made.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Bulanık AHP · bulanık TOPSIS · tedarikçi seçimi

Abstract

Considering the supply chain as a whole and contributing to every link of the chain is an important factor in its management. Businesses that want to make strategic decisions consider supplier selection as an important link. The aim of the study is to increase the efficiency in the enterprise by ensuring that the most appropriate decision is made about the selection of suppliers by the retail companies carrying out service activities. However, it is possible to choose the most suitable supplier by offering the possibility of ranking among the alternatives. For this purpose, solutions were presented in the selection of suppliers by using Multi Criteria Decision Making (MCDM) methods in an enterprise that manages the warehouse activities in Tuzla. In this study, five main criteria determined in retail sector were evaluated by four decision makers (DM) according to fifteen sub-criteria. Verbal expressions evaluated by DMs were converted to fuzzy and trapezoid numbers. Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy TOPSIS methods were used as an integrated method. The results were interpreted and recommendations were made.

Özet

Tedarik zincirini bir bütün olarak düşünmek ve zincirin her halkasına katkı sağlamak yönetilmesinde önemli bir etkendir. Stratejik olarak karar almak isteyen işletmeler tedarikçi seçimlerini önemli bir halka olarak değerlendirir. Çalışmanın amacı, hizmet faaliyetleri yürüten perakende firmalarının tedarikçi seçmeleri konusunda en uygun kararı verilmesini sağlayarak işletme içinde verimliliği arttırmaktır. Bununla birlikte alternatifler arasında sıralama imkanı sunarak en uygun tedarikçiyi seçmektir. Bu amaca yönelik Tuzla’ da depo faaliyetlerini yöneten bir işletmede Çok Kriterli Karar Verme (ÇKKV) yöntemleri kullanılarak tedarikçi seçiminde çözümler sunulmuştur. Bu çalışmada, perakende sektöründe belirlenen beş ana kriter, on beş alt kritere göre dört karar verici(KV) tarafından değerlendirilmiştir. KV’ler tarafından değerlendirilen sözel ifadeler bulanık ve yamuk sayılara dönüştürülmüştür. ÇKKV yöntemlerinden Bulanık Analitik Hiyerarşi Prosesi (BAHP) ve Bulanık TOPSIS yöntemleri bütünleşik olarak kullanılmıştır. Elde edilen sonuçlar yorumlanarak, tavsiyelerde bulunulmuştur.

🔖

Cite This Article

ONAT, A., KAÇTIOĞLU, S. (2020). SUPPLIER SELECTİON BY FUZZY AHP AND FUZZY TOPSIS METHOD: AN APPLICATION IN RETAIL SECTOR. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, Cilt: 19 - Sayı: 37, 65–79. https://ajindex.com/articles/123894

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. Ballı, S., Korukoğlu,S., (2009), “Operating System Selection Using Fuzzy AHP and TOPSIS Methods”, Mathematical and Computational Applications, 14(2), 119-130.
  2. Ballı, S., Korukoğlu,S., (2014), “Development of a Fuzzy Decision Support Framework for Complex Multi‐ Attribute Decision Problems: A Case Study for the Selection of Skilful Basketball Players”, Expert Systems, 31(1), 56-69.
  3. Chamodrakas, D., Batis D., (2010),“Supplier Selection in electronic Marketplaces Using Satisficing and Fuzzy AHP”, Expert Systems with Applications. 37, 490–498, 2010.
  4. Chen, C.T., (2000), “Extensions of the TOPSIS for Group Decisionmaking under Fuzzy Environment”, Fuzzy Sets and Systems, 114(1), 1-9.
  5. Chen, C.T., Lin, C.T., Huang, S.F., (2006), “A Fuzzy Approach for Supplier Evaluation and Selection in Supply Chain Management”, Int. J. Production Economics, 102(2), 289-301.
  6. Dickson, G., (1966), “An Analysis of Vendor Selection Systems and Decisions”, Journal of Purchasing, 2, 28-41.
  7. Ebrahimnejad, S., Mousavi, S.M., Seyrafianpour, H., (2010), “Risk Identification and Assessment for Builde-Operate-Transfer Projects: A Fuzzy Multi Attribute Decision Making Model”.,Expert Syst. Appl., 37 (1), 575-586.
  8. Eraslan, İ., Bakan, İ., Kuyucu, A., (2008), “Türk Tekstil ve Hazır Giyim Sektörünün Uluslararası Rekabetçilik Düzeyinin Analizi”, İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 13, 265-300.
  9. Erdem, M., (2012), Türkiye’de Kombine Taşımacılık İçin Liman Yerinin Bulanık AHP İle Seçimi, İstanbul Üniversitesi,
  10. Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, 87, İstanbul.
  11. Hwang, C.L., Yoon, K., (1981), Multiple Attributes Decision Making Methods and Applications, Springer, Berlin. Kahraman, C., Ates, N.Y., Çevik, S., Gülbay, M., Erdogan, S.A., (2007), "Hierarchical Fuzzy TOPSIS Model for Selection among Logistics İnformation Technologies", Journal of Enterprise Information Management, 20(2), 143–168.
  12. Kannan, D., Jabbour, A.B.L.S., Jabbour, C.J.C., (2014), “Selecting Green Suppliers Based on GSCM Practices: Using Fuzzy TOPSIS Applied to A Brazilian Electronics Company”, European Journal of Operational Research. 233, 432–447.
  13. Kaptanoğlu, D., Özok, F., (2006), “Akademik Performans Değerlendirmesi için bir Bulanık Model”, İstanbul Teknik Üniversitesi Müh. Dergisi, 5, 193-204. Karakaşoğlu N., (2007), “Comparison of Fuzzy AHP and Fuzzy TOPSIS Methods for Facility Location Selection”, International Journal of Advanced Manufacturing Technology, 12(18), 37-45.
  14. Kurt Tekez, E., Bark, N., (2016), “Mob