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AKBURAK, DİLEK

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Arş.Gör.
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AKBURAK
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DİLEK
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    Customer Oriented Intelligent DSS Based on Two-Phased Clustering and Integrated Interval Type-2 fuzzy AHP and Hesitant Fuzzy TOPSIS
    (IOS Press BV, 2020) Şenvar, Özlem; AKBURAK, DİLEK; Yel, Necla
    Firms need to integrate multiple business functions in order to acquire, analyze, model, and evaluate information necessary for better understanding customer behaviors and making data-driven decisions to enhance the customer experience journey. This study proposes a customer oriented intelligent decision support system (IDSS) to ultimately improve the customer experience journey. Besides, a real application study is handled for a multinational company located in Turkey, considering its abrasives product sales for years of 2017 and 2018. For the data utilized in application study, the proposed methodology is constructed for customer segmentation to develop appropriate data-driven marketing strategies for customers with similar values, preferences and other factors for creating customer-centric organizations. In this regard; firstly two-phased clustering process, which involves the hierarchical multivariate average linkage clustering algorithm and partitional k-means clustering algorithm, is used to present the number of clusters on the basis of three variables (expenditure, transaction and unit cost) and then to assign the customers to the related clusters (VIP, Platinum, Gold and Bronze), respectively. Secondly, the performances of company's departments are ranked according to the preferences of customers from each segment considering 4Ps marketing mix concept via integrated methodology of interval type-2 Fuzzy AHP and hesitant fuzzy TOPSIS.
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    Review of Fuzzy Multi-Criteria Decision Making Methods for Intelligent Supplier Selection
    (Springer International Publishing, 2022) AKBURAK, DİLEK
    Supplier selection is a focal process that affects the quality and cost performance of the company. Therefore, it is one of the well-known decision making problems that researchers and practitioners are most interested in. In order to choose the most strategic supplier, determining and prioritizing the selection criteria and choosing the appropriate method(s) directly affect the supply chain performance. In this study, the publications focused on multi-criteria decision making (MCDM) on the supplier selection problems in the uncertain environment are reviewed from 2017 to the present (Feb. 2022). Due to the effect of uncertainty and vagueness, most recent approaches developed for supplier selection, are constructed by integrating MCDM approach(es) with fuzzy set theory. The most widely applied and integrated fuzzy MCDM methods are stated as AHP, ANP, TOPSIS, and VIKOR. These studies are categorized into single or multiple/integrated MCDM approach(es) with different fuzzy sets in various application industries. This study contributes to the literature to examine the most frequently applied and recently developed fuzzy MCDM approaches for intelligent supplier selection by considering various assessment criteria under imprecise environments. Also, newly improved ideas can be proposed with the help of the analysis of studies about intelligent supplier selection up to the present.