Deep Learning Readiness for Smart Manufacturing: A Socio-Technical Approach | ||
| المجلة العلمية للدراسات والبØوث المالية والتجارية | ||
| Volume 5, Issue 2 - Serial Number 1, July 2024, Pages 181-213 PDF (1.15 M) | ||
| Document Type: المقالة الأصلية | ||
| DOI: 10.21608/cfdj.2023.254091.1865 | ||
| Author | ||
| أيمن Ù…Øسب* | ||
| قسم نظم معلومات الأعمال ، کلية تکنولوجيا الإدارة ونظم المعلومات، جامعة بورسعيد، بورسعيد، مصر | ||
| Abstract | ||
| The study's goal is to look into the current situation of Egyptian industrial organization's readiness to use deep learning techniques, as well as to identify both technical and social factors that influence their readiness to use advanced artificial intelligence technologies. The study employed a questionnaire to gather data from 162 factories across different industrial sectors in Egypt. The research hypothesis test was based on the structural equation modeling method, which uses the partial least squares method based on variance to analyze data through the "Smart-PLS" program. This method was chosen because it is the most appropriate for the study's characteristics, taking into account factors like sample size and data type. The results showed that enhancing the technical and social aspects of the manufacturing system has a positive impact on an organization's readiness to apply deep learning techniques in the Egyptian context, which in turn has an impact on this organization's capacity to apply smart manufacturing technologies. The study additionally found that Egyptian manufacturers are just partially ready to use deep learning techniques, with a medium readiness level, and that they continue to use manufacturing systems that are somewhat distinct from artificial intelligence technology. The study provides set guidelines to improve organization's deep learning readiness for smart manufacturing in the Egyptian industrial sector. | ||
| Keywords | ||
| Deep learning; Smart Manufacturing; Socio-technical Theory; Deep learning Readiness; Egyptian industrial organizations | ||
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