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In the global manufacturing business, more sustainable solutions are needed, and in parallel manufacturing companies also need a capability to react quickly to unpredictable changes in a highly competitive marketplace. This has become more visible at the start of the Covid-19 pandemic when many companies experienced manufacturing constraints such as a shortage of raw materials as well as a need for transformation of manufacturing facilities to produce vital items. In parallel, many manufacturing companies also are in the transition towards more digitalization as well as automation in their facilities, which can contribute to the capability to manage unpredictable changes, but it might also contribute to challenges. Therefore, the digital transformation of production processes with innovative and emerging technologies, and the contribution and challenges of these technologies are of importance to understand. Specifically, in which way these emerging technologies contribute to a resilient and sustainable production process from a technological perspective, and how these production processes might contribute to achieving sustainability goals. Based on findings from literature an analysis is performed aiming to explore the emerging technologies perspective in relation to capabilities as well as challenges regarding implementing solutions that contributes to a resilient and sustainable production process. The overall findings indicated the importance of having flexible production facilities to be able to respond to customer expectations. However, the digital transformation of production facilities demonstrated other challenges such as energy consumption, lack of skilled personal, low level of standardization, financial sources, and security problems. To conclude, the relationship between the content of resilient and sustainable production systems that are influenced by emerging technologies are suggested, with the ambition to contribute to a more competitive manufacturing company that increase its capability to manage customer expectations. © 2022 The authors and IOS Press.
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Objective: Coronavirus disease 2019 (COVID-19) had an unprecedented effect on both nations and health systems. Time series modeling using Auto-Regressive Integrated Moving Averages (ARIMA) models have been used to forecast variables extensively instatistics and econometrics. We aimed to predict the total number of cases for COVID19 using ARIMA models of time-series analysis in Turkey.
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Introduction: In this study, we aimed to better understand the role of chest CT as an initial workup tool among all COVID-19 patients admitted to a tertiary hospital. Materials and Methods: We retrospectively evaluated the data of patients that were suspected for COVID-19. All patients who had both noncontrast chest CT scan with RT-PCR test results included in the study. Symptomatic patients were divided into four groups according to time between the onset of symptoms and chest CT;4 days, 5-8 days, 9-13 days, and 14 days. Chest CT findings according to symptom status, duration, and RT-PCR positivity were evaluated. Results: Data for a total of 791 patients were evaluated. The mean patient age was 51.7 19.7 years. 459 (58%) patients were male, and 332 (42%) were female. 55.1% of patients had positive and 44.9% negative RT-PCR tests. Typical, indeterminate, atypical and negative chest CT findings were seen in 241 (30.5%), 131 (16.6%), 154 (19.5%), and 265 (33.5%) patients, respectively. Among 355 patients with negative RT-PCR results, 152 (42.8%) had typical or indeterminate chest CT findings. Asymptomatic patients had a 91.9% of RT-PCR positivity. Only 123 (61.5%) patients had typical or indeterminate CT findings among symptomatic and RT-PCR positive cohort. Conclusion: The greatest value of our study is in demonstrating the value of chest CT in both patients that had symptoms but had negative RT-PCR test results and insignificance of chest CT in asymptomatic but had suspected contact with COVID-19 patients.