Browsing by Author "Dora, Manoj"

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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, 2022-09-15) 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.
Citation - WoS: 9
Citation - Scopus: 13
Smart Closed-Loop Food Supply Chain in Circular Economy
(Institute of Electrical and Electronics Engineers Inc., 2024) Yigit Kazancoglu; Esra Ekinci; Sachin Kumar Kumar Mangla; Muruvvet Deniz Sezer; Sonal Choudhary; Manoj Kumar Dora; Choudhary, Sonal; Dora, Manoj; Sezer, Muruvvet Deniz; Ekinci, Esra; Kazancoglu, Yigit; Mangla, Sachin Kumar
The aim of this study is to propose a triple bottom line (TBL) framework to determine the key metrics of environmental social and economic sustainability and predict the impacts of these metrics on sustainability in circular economy. First TBL working framework has been established to determine the key environmental social and economic sustainability metrics. Then the system dynamics (SD) model is developed to estimate the effects of TBL metrics within a smart closed-loop food chain. The proposed SD model reinforces the idea that each metric supports more than one dimension of sustainability in that food loss can have implications for sustainability dimensions. With the help of the incorporated metrics management can see the supply chain and examine various parts of it such as production distribution the impact of buying decisions etc. The results showed which stages of the food supply chain need attention because of the high value-added product losses and if the company targets are enough to reduce greenhouse gas emissions even though the production volume will increase in the coming years. This article contributes to the literature by determining the key metrics of TBL sustainability in the smart closed-loop supply chain in the food industry. © 2024 Elsevier B.V. All rights reserved.