Yazar "Kazancoglu, Yigit" seçeneğine göre listele

Listeleniyor 1 - 4 / 4
  • Küçük Resim
    Öğe
    Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations
    (Springer, 2022) Sariyer, Gorkem; Ataman, Mustafa Gökalp; Mangla, Sachin Kumar; Kazancoglu, Yigit; Dora, Manoj
    Grounded in dynamic capabilities, this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs, it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients, the average daily length of stay (LOS), and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19, and includes data from 238,152 patients. Comparing statistics on daily patient volumes, average LOS, and resource usage, both before and during the COVID-19 pandemic, we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period, these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model, we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158,347, it decreased to 79,805 during-COVID-19. On the other hand, while the average daily LOS was 117.53 min before-COVID-19, this value was calculated to be 165,03 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies, it empirically investigates the impact of different policies on ED operations.
  • Küçük Resim
    Öğe
    Building sustainable resilient supply chain in retail sector under disruption
    (Elsevier Sci Ltd, 2024) Ekinci, Esra; Sezer, Muruvvet Deniz; Mangla, Sachin Kumar; Kazancoglu, Yigit
    Blockchain technologies have played a crucial role in transforming the retail industry, leading to remarkable advancements in recent times. Its pivotal role in managing risky environments by offering preventive and proactive measures cannot be overstated. The research contribution lies in introducing a set of criteria for assessing the adoption of Blockchain technology, specifically designed to evaluate the resilience of the retail sector. This study aims to ensure the establishment of a sustainable, resilient supply chain across diverse retail categories, particularly in the face of uncertain circumstances. A hybrid decision-making approach that combines the BestWorst Method (BWM) and Fuzzy TODIM has been employed to achieve these objectives. This study encompasses various types of retail companies to assess and compare their resilience levels by adopting Blockchain technology. The results of this study robustly suggest that speciality retailers with well-established, long-term partnerships are more predisposed to embrace and leverage the capabilities of Blockchain technologies. Conversely, discount retailers in Turkey face various challenges that impede their effective integration of Blockchain technologies. These challenges encompass collaborating with suppliers on short-term agreements and the unavailability of product tracking, among other factors. As a result, the outcomes of this study offer valuable insights for retailers in the sector, suggesting that they should consider modifying their operational strategies to better align with the adoption and integration of Blockchain technologies in the future.
  • Küçük Resim
    Öğe
    Data-driven decision making for modelling covid-19 and its implications: A cross-country study
    (Elsevier Science Inc, 2023) Sariyer, Gorkem; Mangla, Sachin Kumar; Kazancoglu, Yigit; Jain, Vranda; Ataman, Mustafa Gokalp
    Grounded in big data analytics capabilities, this study aims to model the COVID-19 spread globally by considering various factors such as demographic, cultural, health system, economic, technological, and policy-based. Classified values on each country's case, death, and recovery numbers (per 1000,000 population) were used to represent COVID-19 spread. Data sets also included 29 input variables for the corresponding six factors, containing data from 159 countries. The proposed model used a Multilayer Perceptron algorithm. The results show that each of the pre-mentioned factors significantly affects disease spread. Urban population, median age, life expectancy, numbers of medical doctors and nursing personnel, current health expenditure as a % of GDP, international health regulations capacity score, continent, literacy rate, governmental response stringency index, testing policy, internet usage %, human development index and GDP per capita were identified as significant. Taking early measures and adopting open public testing policies were recommended to policymakers in fighting pandemic diseases since the created scenarios on policy-based factors revealed their importance.
  • Küçük Resim Yok
    Öğe
    Smart technologies for collection and classification of electronic waste
    (Emerald Group Publishing Ltd, 2023) Ada, Erhan; Ilter, Halil Kemal; Sagnak, Muhittin; Kazancoglu, Yigit
    PurposeThe main aim of this study is to understand the role of smart technologies and show the rankings of various smart technologies in collection and classification of electronic waste (e-waste).Design/methodology/approachThis study presents a framework integrating the concepts of collection and classification mechanisms and smart technologies. The criteria set includes three main, which are economic, social and environmental criteria, including a total of 15 subcriteria. Smart technologies identified in this study were robotics, multiagent systems, autonomous tools, smart vehicles, data-driven technologies, Internet of things (IOT), cloud computing and big data analytics. The weights of all criteria were found using fuzzy analytic network process (ANP), and the scores of smart technologies which were useful for collection and classification of e-waste were calculated using fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR).FindingsThe most important criterion was found as collection cost, followed by pollution prevention and control, storage/holding cost and greenhouse gas emissions in collection and classification of e-waste. Autonomous tools were found as the best smart technology for collection and classification of e-waste, followed by robotics and smart vehicles.Originality/valueThe originality of the study is to propose a framework, which integrates the collection and classification of e-waste and smart technologies.