PubMed İndeksli Yayınlar Koleksiyonu

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Browsing PubMed İndeksli Yayınlar Koleksiyonu by Journal "Annals of Operations Research"

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    Article
    Citation - WoS: 10
    Citation - Scopus: 13
    Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations
    (SPRINGER, 2023) Gorkem Sariyer; Mustafa Gokalp Ataman; Sachin Kumar Mangla; Yigit Kazancoglu; Manoj Dora; Ataman, Mustafa Gokalp; Dora, Manoj; Sariyer, Gorkem; Mangla, Sachin Kumar; Kazancoglu, Yigit
    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 238152 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 158347 it decreased to 79805 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 16503 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.
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    Article
    Citation - WoS: 33
    Citation - Scopus: 36
    The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods
    (SPRINGER, 2024) Sule Birim; Ipek Kazancoglu; Sachin Kumar Mangla; Aysun Kahraman; Yigit Kazancoglu; Birim, Sule; Kazancoglu, Ipek; Mangla, Sachin Kumar; Kahraman, Aysun; Kazancoglu, Yigit
    In recent years machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques-Support Vector Regression (SVR) Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques-Artificial Neural Network (ANN) Long Short Term Memory (LSTM)-to deal with demand forecasting based on advertising expenses. Deep learning is a powerful technique that can solve marketing problems based on both classification and regression algorithms. Accordingly a television manufacturer's real market dataset consisting of advertising expenditures sales and demand forecasting via chosen machine learning methods was analyzed and compared in terms of the accuracy of demand forecasting. As a result Long Short Term Memory has been found to be superior to other models in providing highly accurate prediction results for demand forecasting based on advertising expenses.
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    Article
    Citation - WoS: 67
    Citation - Scopus: 88
    Using emerging technologies to improve the sustainability and resilience of supply chains in a fuzzy environment in the context of COVID-19
    (Springer, 2023) Ipek Kazançoǧlu; Melisa Ozbiltekin-Pala; Sachin Kumar Kumar Mangla; Ajay Kumar; Yigit Kazancoglu; Ozbiltekin-Pala, Melisa; Kumar, Ajay; Kazancoglu, Ipek; Mangla, Sachin Kumar; Kazancoglu, Yigit
    In rapidly changing business conditions it has become extremely important to ensure the sustainability of supply chains and further improve the resiliency to those events such as COVID-19 that can cause unexpected disruptions in the value supply chain. Although globalized supply chains have already been criticized for lack of control over sustainability and resilience of supply chain operations these issues have become more prevalent in the uncertain environment driven by COVID-19. The use of emerging technologies such as blockchain Industry 4.0 analytics model and artificial intelligence driven methods are aimed at increasing the sustainability and resilience of supply chains especially in an uncertain environment. In this context this research aims to identify the problematic areas encountered in building a resilient and sustainable supply chain in the pre-COVID-19 era and during COVID-19 and to offer solutions to those problematic areas tackled by an appropriate emerging technology. This research has been contextualized in the automotive industry, this industry has a complex supply chain structure and is one of the sectors most affected by COVID-19. Based on the findings the most important problematic areas encountered in SSCM pre-COVID-19 are determined as supply chain traceability demand planning and production management as well as purchasing process planning based on cause and effect groups. The most important issues to be addressed during COVID-19 are top management support purchasing process planning and supply chain traceability respectively. © 2023 Elsevier B.V. All rights reserved.