Person:
AKBURAK, DİLEK

Profile Picture

Email Address

Birth Date

Research Projects

Organizational Units

Job Title

Arş.Gör.

Last Name

AKBURAK

First Name

DİLEK

Name

Filters

2018 - 201942020 - 20222
Reset filters

Settings

Search Results

Now showing 1 - 6 of 6
  • No Thumbnail Available
    Publication
    Anticipation for Customer Analytics
    (2019-11) Şenvar, Özlem; Yel, Necla; AKBURAK, DİLEK; 275470; 193864
    In the digitalization era, enterprises are required to proactive customer relationships via analytical techniques to execute customer treatment strategies based on predictive analytics drawing on a wealth of data about customer behaviors. Nowadays, predictive analytics with automatic decision making concepts are integrated for better understanding preferences of customers, segmenting customers, promoting right products to right customers, improving customer experience, and driving revenue. Customer value anticipation becomes inevitable for understanding customer requirements and expectations. Anticipation can provide capacities in shaping customer perceptions and expectations to make sense of novelty by enlarging and enhancing the analytical and operational approaches to incorporate decision making. In this regard, customer analytics is handled within the perspective of the process by which data from customer behavior utilized to make key business decisions through market segmentation and predictive analytics in order to maximize organizational throughput and maintain long lasting relationships with customers. From this standpoint, this study aims to provide an overview for customer analytics considering anticipation, customer experience and big data analytics involving strategic decision making processes for customer loyalty and relationships. Moreover, customer experience quality is discussed for anticipating customer expectation.
  • No Thumbnail Available
    Publication
    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.
  • No Thumbnail Available
    Publication
    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.
  • No Thumbnail Available
    Publication
    Implementation of Lean Six Sigma for Airline Ground Handling Process
    (2018-06) Şenvar, Özlem; AKBURAK, DİLEK; 275470
    Lean six sigma methodology offers a broad range of assessments and im­plementation services to meet the demands and challenges organizations face in to­day’s global marketplace, where improved processes and unobstructed flow are es­sential elements for reducing costs and maintaining a competitive advantage. So that organizations can maximize profits and increase business value by providing knowledge and guidance on implementing continuous improvement, culture change, methodologies and tools. From this standpoint, the purpose of this study is to analyze and enhance improvements for the non-value adding processes in airline ground handling operations via lean six sigma methodology and utilization of the appropriate tools.
  • No Thumbnail Available
    Publication
    Havaalanı Yer Hizmetlerinde Kalite ve Süreç İyileştirme Çalışmaları
    (2018-06) Şenvar, Özlem; AKBURAK, DİLEK; 275470; 193864
    Günümüzde hava yolu taşımacılığı ve hava yolunu tercih eden yolcuların sayısı her geçen gün artış göstermektedir. Bu talebi karşılamak için müşteri odaklı olmanın çok önemli olduğu havacılık sektöründe, müşteri beklentilerini en iyi şekilde karşılanması için gelişen teknoloji ve değişen düzenlemeleri göz önünde bulunduran servis süreçlerinin kalitesinin ele alınması gerekmektedir. Hizmet kalitesi farklı yollarla tanımlanmıştır ancak en yaygın kullanılan tanımlardan biri, hizmetin müşteri ihtiyaçlarını ve/veya beklentilerini karşılama derecesidir. Çok sayıda çalışma, hava yolu endüstrisinde hizmet kalitesi sorunlarını ele almaktadır. Bu çalışmada, hizmet kalitesini ölçmek için kullanılan en yaygın metotların detaylı incelenmesi ve karşılaştırılması yapılmaktadır. Bu kapsamda, hava yolu yer hizmetleri süreçlerinin performansını etkileyen faktörlerin incelenmesi ve hizmet kalitesinin ölçülmesinde literatürdeki çalışmaların derlenmesi ve konuyla ilgili yöntemlerin karşılaştırılmalı olarak irdelenmesi ve temel bulguların ve de önerilerin çerçevesinin sunulması çalışmanın temel amaçlarıdır.
  • No Thumbnail Available
    Publication
    An Unsupervised Data Mining Approach for Clustering Customers of Abrasive Manufacturer
    (2019-07) AKBURAK, DİLEK; 275470
    Customer segmentation is the process of dividing customers into groups based on common similar characteristics such as value, location, demography etc. Companies can communicate with each group effectively and appropriately by considering these common properties. Data mining algorithms are the most utilized techniques which lead direct marketers to develop their marketing strategies tailored to particular segments and/or individuals. Clustering is one of the unsupervised data mining methods used for grouping set of objects such a way that objects in the same group have maximum similarity while between group similarities are low. K-means clustering is a commonly used non-hierarchical clustering method for performing non-parametrical learning tasks. This study aims to identify customer types according to their profitability, value and risk in order to take appropriate action for each group via clustering. In this study, data items are grouped according to coded customer profile with respect to the consumers’ total expenditures. Customers are segmented as VIP, Platinum, Gold, and Bronze into 4 groups according to their values within 2 years.