2 results
Search Results
Now showing 1 - 2 of 2
Article Citation - WoS: 89Citation - Scopus: 105Industry 4.0 Technologies, Sustainable Operations Practices and Their Impacts on Sustainable Performance(Elsevier Sci Ltd, 2023) Yavuz, Oguzhan; Uner, M. Mithat; Okumus, Fevzi; Karatepe, Osman M.Using a natural resource-based view and technology-organization-environment framework as the theoretical focus, this paper develops and tests a research model in which sustainable operations practices mediate the impact of industry 4.0 technologies on sustainable performance. The model also tests sustainable operations practices as a moderator of the effect of industry 4.0 technologies on sustainable performance. Data obtained from 302 participants in Turkey's technology development regions were utilized to gauge the aforesaid linkages via partial least squares structural equation modeling. As predicted, sustainable operations practices mediate the influence of industry 4.0 technologies on sustainable performance. Contrary to the study prediction, sustainable operations practices do not significantly moderate the impact of industry 4.0 technologies on sustainable performance. Theoretical and practical implications are discussed and future research directions are offered.Article Citation - WoS: 2Citation - Scopus: 3Real-Time Anomaly Detection System Within the Scope of Smart Factories(Springer, 2023) Bayraktar, Cihan; Karakaya, Ziya; Gokcen, HadiAnomaly detection is the process of identifying patterns that move differently from normal in a certain order. This process is considered one of the necessary measures for the safety of intelligent production systems. This study proposes a real-time anomaly detection system capable of using and analyzing data in smart production systems consisting of interconnected devices. Synthetic data were preferred in the study because it has difficulties such as high cost and a long time to obtain real anomaly data naturally for learning and testing processes. In order to obtain the necessary synthetic data, a simulation was developed by taking the popcorn production systems as an example. Multi-class anomalies were defined in the obtained data set, and the analysis performances were tested by creating learning models with AutoML libraries. In the field of production systems, while studies on anomaly detection generally focus on whether there is an anomaly in the system, it is aimed to determine which type of anomaly occurs in which device, together with the detection of anomaly by using multi-class tags in the data of this study. As a result of the tests, the Auto-Sklearn library presented the learning models with the highest performance on all data sets. As a result of the study, a real-time anomaly detection system was developed on dynamic data by using the obtained learning models.
