Dursun Delen

İş dünyasının giderek karmaşıklaşan ve dinamik hale gelen yapısı, farklı disiplinlerden gelen bireylerin aynı örgütsel çatı altında aynı amaçlar doğrultusunda etkin ve verimli çalışmalarını zorunlu hale getirmiştir. Bu sebeple de, işletmenin tüm işlevlerini bütüncül bir bakış açısı ile değerlendirebilecek ve bu hususları faaliyet gösterilen ekosistemin diğer dinamikleri ile uyumlu yönetebilecek bireylere duyulan ihtiyaç artmıştır. Ayrıca, teknoloji alanında yaşanan baş döndürücü gelişmeler rekabetin sahasını genişletmiş ve özellikle üretim, dağıtım, pazarlama ve finans alanlarında entegre bilgi birikimine sahip, yönetsel becerisi yüksek insan kaynağına önemli ölçüde bir talep doğurmuştur.

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Dursun Delen

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Sağlık Analitiği, Karar Destek Sistemleri, Sağlık Analitiği, İş Zekası, İş Analitiği

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Listeleniyor 1 - 10 / 18

A probabilistic data analytics methodology based on Bayesian Belief network for predicting and understanding breast cancer survival
(Elsevier, 2022) Dağ, Aslı Z.; Akcam, Zümrüt; Kibis, Eyyub; Şimşek, Serhat; Delen, Dursun; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
Understanding breast cancer survival has proven to be a challenging problem for practitioners and researchers. Identifying the factors affecting cancer progression, their interrelationships, and their influence on patients’ long-term survival helps make timely treatment decisions. The current study addresses this problem by proposing a Tree-Augmented Bayesian Belief Network (TAN)-based data analytics methodology comprising of four steps: data acquisition and preprocessing, variable selection via Genetic Algorithm (GA), data balancing with synthetic minority over-sampling and random undersampling methods, and finally the development of the TAN model to determine the probabilistic inter-conditional dependency structure among breast cancer-related variables along with the posterior survival probabilities The proposed model is compared to well-known machine learning models. A what-if analysis has also been conducted to verify the associations among the variables in the TAN model. The relative importance of each variable has been investigated via sensitivity analysis. Finally, a decision support tool is developed to further explore the conditional dependency structure among the cancer-related factors. The results produced by the proposed methodology, namely the patientspecific posterior survival probabilities and the conditional relationships among the variables, can be used by healthcare professionals and physicians to improve the decision-making process in planning and managing breast cancer treatments. Our generic methodology can also accommodate other types of cancer and be applied to manage various medical procedures.
An ANP and fuzzy TOPSIS-based SWOT analysis for Turkey's energy planning
(Elsevier, 2018) Ervural, Beyzanur Çayır; Zaim, Selim; Demirel, Ömer Fahrettin; Aydın, Zeynep; Delen, Dursun; Zaim, Selim; Demirel, Ömer Fahrettin; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
Energy planning involves a perpetual process of reevaluating alternative energy strategies. Authorities responsible for energy planning and management have to adjust their strategies according to new and improved alternative solutions based on the sustainability criteria. In this study, we propose an integrated hybrid methodology for the analysis of Turkey’s energy sector using Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, Analytic Network Process (ANP) process, and weighted fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) to formulate and holistically analyze the energy strategy alternatives and priorities. The methodology proposed in this study allowed identifying the relevant criteria and sub-criteria using a SWOT analysis. Then, ANP approach, which is one of the popular multi-criteria decision making (MCDM) methods, is employed to determine the weights of each SWOT factors and sub-factors. Finally, fuzzy TOPSIS methodology is conducted to prioritize alternative energy strategies. We discuss the obtained results for the development of long-range alternative energy strategies. The results showed that turning the country into an energy hub and an energy terminal by effectively using the geo-strategic position within the framework of the regional cooperation is the most important priority. On the other hand, using the nuclear energy technologies within the energy supply strategies found to be the least favored priority.
Big data analytics capabilities and firm performance: An integrated MCDM approach
(Elsevier, 2020) Yasmin, Mariam; Tatoğlu, Ekrem; Kılıç, Hüseyin Selçuk; Zaim, Selim; Delen, Dursun; Tatoğlu, Ekrem; Zaim, Selim; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
This study explores the interdependence of big data analytics (BDA) capabilities and the impact of these capabilities on firm performance using an integrated multicriteria decision-making (MCDM) methodology. Drawing on a rich data set obtained from selected case study firms in Pakistan, three MCDM tools, namely, intuitionistic fuzzy decision-making trial and evolution laboratory (IF-DEMATEL), analytic network process (ANP), and simple additive weighting (SAW), are employed to assess the relative importance of BDA capabilities and the relationship of these capabilities with the firm performance. The results show that BDA capabilities are interdependent, and infrastructure capabilities are the highest-ranked among all, followed by management and human resource capabilities, respectively. The SAW results indicate an association between BDA capabilities and firm performance. Moreover, BDA capabilities are more strongly related to operational performance than to market performance.
Business analytics adoption and technological intensity: An efficiency analysis
(Springer, 2023) Bayraktar, Erkan; Tatoğlu, Ekrem; Aydıner, Arafat Salih; Delen, Dursun; Tatoğlu, Ekrem; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
Despite the overwhelming popularity of business analytics (BA) as an evidence-based decision support mechanism, the impact of its adoption on organizational performance has received scant attention from the research community. This study aims to unfold the adoption efficiencies of BA and its applications by proposing a data envelopment analysis (DEA) methodology to holistically assess the underlying factors with respect to the level of achievement regarding organizational performance, operational performance, and financial performance. Furthermore, the study unveils the firm-level and sectoral-level discrepancies in BA adoption efficiency in different industry settings. Relying on survey data obtained from 204 executives in various industries, this study provides empirical support for the cross-industry differences in BA adoption efficiencies. The results show that the firms in low-tech industries seem to achieve the highest efficiency from adopting BA regarding its influence on firm performance.
The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review
(Elsevier, 2022) Yalçın, Ahmet Selçuk; Kılıç, Hüseyin Selçuk; Delen, Dursun; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
Business analytics (BA) systems are considered significant investments for enterprises because they have the potential to considerably improve firms’ performance. With the value offered by BA, companies are able to discover the hidden information in the data, improve decision-making processes, and support strategic planning. On the other hand, because there are multiple criteria and multiple alternatives involved in most decision- making situations, multi-criteria decision-making (MCDM) methods play an important role in BA practices. Providing inputs to the components of descriptive or predictive analytics or being used as a decision-making tool for evaluating the alternatives within prescriptive analytics exemplify the roles. Therefore, the use of hidden information discovered by business analytics and the need for utilizing the right MCDM method for optimal decision-making made these two concepts inseparable. In this paper, in order to review the use of MCDM methods in BA, the subject of BA is investigated from a taxonomical perspective (descriptive, predictive, and prescriptive), and its connection with MCDM techniques is revealed. Similarly, MCDM methods are studied using two main categories, multi-attribute decision making (MADM) and multi-objective decision making (MODM) methods. Furthermore, tabular and graphical analyses are also performed within the proposed review meth-odology. To the best of our knowledge, this review is the first attempt that holistically considers the use of MCDM methods in BA.
An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions
(Elsevier, 2022) Davazdahemami, Behrooz; Zolbanin, Hamed M.; Delen, Dursun; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
One of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory- predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies.
An interactive decision support system for real-time ambulance relocation with priority guidelines
(Elsevier, 2022) Hajiali, Mahdi; Teimoury, Ebrahim; Rabiee, Meysam; Delen, Dursun; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
Changes in demand patterns and unexpected events are the two primary sources of delays in healthcare emergency operations. To mitigate such delays, researchers proposed the movement of idle ambulances between emergency bases as one of the effective ways to improve the areal coverage of future demands. In this study, we have developed a model-driven decision support system that simultaneously seeks to maximize demand coverage while minimizing travel time by optimally relocating emergency response vehicles. The developed mathematical model partitions and prioritizes demand into four categories and continuously updates them over time. Furthermore, it dynamically calculates the number of coverages in different regions based on the current location of idle ambulances. Also, we developed a real-time risk assessment DSS for recommended relocations, which could be utilized as a reference by the EMS user while implementing suggested relocation decisions. A real case study is used to validate the proposed DSS, and its final output is compared to the existing operational policy. The findings show that the average workload added to each ambulance due to relocations has significantly improved the response time and coverage ratio. Compared to the existing operational policy, the developed decision support system decreased the time to respond to calls, which was deemed to be more than to offsets the increase in travel time due to relocation. Furthermore, the system also reduced the total working time of all ambulances by about 9% per shift.
Development of a sustainable corporate social responsibility index for performance evaluation of the energy industry: A hybrid decision-making methodology
(Elsevier, 2023) Dinçer, Hasan; Yüksel, Serhat; Hacıoğlu, Ümit; Yılmaz, Mustafa Kemal; Delen, Dursun; Hacıoğlu, Ümit; Yılmaz, Mustafa Kemal; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
The ever-increasing pressure from stakeholders and policymakers on energy companies to achieve Sustainable Development Goals (SDGs) and Corporate Social Responsibility (CSR) mission requires them to reinvent their policies and practices. This study aims to examine the performance of alternative business models for the oil and gas industry by employing a hybrid business analytics methodology under a fuzzy environment resulting in a generalizable model named “Sustainable Development Goals-oriented CSR Index.” The proposed methodology employs a hybrid framework that utilizes bipolar Q-rung Orthopair Fuzzy (q-ROF), Multi Stepwise Weight Assessment Ratio Analysis (M-SWARA), and Elimination and Choice Translating Reality (ELECTRE) methods. The findings show that (i) the proposed model is reliable and consistent throughout the similar fuzzy set value ranges, (ii) clean energy is the most important SDG-oriented CSR Index factor for the sustainable energy industry in emerging economies, (iii) drilling is the best alternative energy sourcing for the oil and gas industry, and (iv) clean energy projects have the highest priority for energy investors. The results also highlight that global warming can be managed with effective energy practices for long-term sustainability. Finally, the findings suggest that energy companies should have the essential technological infrastructure and capable workforce to increase investment efficiency.
Optimizing sustainable industry investment selection: A golden cut-enhanced quantum spherical fuzzy decision-making approach
(Elsevier, 2023) Hacıoğlu, Ümit; Dinçer, Hasan; Yılmaz, Mustafa Kemal; Yüksel, Serhat; Sonko, Mariama; Delen, Dursun; Hacıoğlu, Ümit; Yılmaz, Mustafa Kemal; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
This study aims to rank sustainable industry alternatives in emerging markets based on the directional impact relations of the environmental, social, and governance (ESG) index components for a socially and environmentally conscious investment strategy. To achieve this goal, we employ a golden cut-enhanced quantum spherical fuzzy decision-making approach. Specifically, we first use a quantum spherical fuzzy DEMATEL technique to identify the impact-relation directions and the weights of the ESG criteria set. Second, we employ the extended TOPSIS with the quantum spherical fuzzy sets to rank the industry alternatives concerning their directional ESG performances. The findings show that (i) H20 Emissions, Innovation, Community Investment, Gender Equity, Human Rights, and CSR Strategy are the main influencing factors based on their impact-relations directional scores, (ii) Resource Usage, Product Responsibility, and Shareholders’ Rights are the set of criteria under the influence of remaining ESG, (iii) Innovation is the strongest ESG performance criterion, whereas Human Rights is the weakest, (iv) technology and communication are the best-performing industries based on the directional ESG index performance scores, whereas real estate and basic materials industries are the worst performing. The study provides valuable and actionable insights for companies that aim to make socially responsible investments.
An integrated approach for lean production using simulation and data envelopment analysis
(Springer, 2021) Buyuksaatci Kiris, Sinem; Eryarsoy, Enes; Zaim, Selim; Delen, Dursun; Zaim, Selim; Delen, Dursun; Yönetim Bilimleri Fakültesi, İşletme Bölümü
According to the extant literature, improving the leanness of a production system boosts a company’s productivity and competitiveness. However, such an endeavor usually involves managing multiple, potentially conflicting objectives. This study proposes a framework that analyzes lean production methods using simulation and data envelopment analysis (DEA) to accommodate the underlying multi-objective decision-making problem. The proposed framework can help identify the most efficient solution alternative by (i) considering the most common lean production methods for assembly line balancing, such as single minute exchange of dies (SMED) and multi-machine set-up reduction (MMSUR), (ii) creating and simulating various alternative assembly line configuration options via discrete-event simulation modeling, and (iii) formulating and applying DEA to identify the best alternative assembly system configuration for the multi-objective decision making. In this study, we demonstrate the viability and superiority of the proposed framework with an application case on an automotive spare parts production system. The results show that the suggested framework substantially improves the existing system by increasing efficiency while concurrently decreasing work-in-process (WIP).

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