Track: Modeling and Simulation
Abstract
The major goal of this project was to provide an opportunity for minority undergraduate and graduate students to attend and participate in the Ninth North American International Conference on Industrial Engineering and Operations Management (IEOM). Students were exposed to Industry 4.0 and artificial intelligence with focusing on IoT, data analytics, iCloud, cybersecurity, product lifecycle management, industry solutions, simulation and digital twin. Pre and post-surveys were conducted to assess the learning experiences of conference attendees. Twenty-two students participated in the pre-survey and 12 students participated in the post-survey. Students were asked about their understanding of Industry 4.0 / cyber-physical systems (CPS) before and after the conference. It was an increase from 40.9% to 66.7%. Students were asked where their exposure was to companies that operate internationally. It increases from 40.9% to 58.3%. In the pre-survey, 30% - 40% of students were very knowledgeable in Industry 4.0 and in the post-survey, 50% - 60% of students had a very good knowledge in the same areas. Understanding and learning improvement of the percentage of awardees in other areas of Industry 4.0 who mentioned some knowledge and very knowledge between pre-conference and post-conference survey responses are listed below:
- Internet of Things (IoT): 68% to 58%
- Artificial Intelligence and Machine Learning (AI-ML): 86% to 83%
- Big Data and Data Analytics: 72% to 83%
- iCloud and Cybersecurity: 72% to 75%
- Simulation and Visualization: 68% to 66%
- Innovation and Entrepreneurship: 68% to 83%
- Industrial Automation: 45% to 66%
- Integrated Systems: 27% to 83%
- Industry Best Practices: 40% to 66%
- Logistics and Supply Chain: 54% to 66%
- Operational Excellence and Productivity Improvement: 54% to 74%
- Lean Six Sigma: 41% to 66%
- Digital Twin: 32% to 58%
- Digital Manufacturing: 40% to 75%
Students improved most of the areas except a few. Only a few had a decrease. It could be due to the sample size variation between pre-survey (22 responses) and post survey (12 responses).