Archives 2020

Real-time Data Analytics Edge Computing Application for Industry 4.0: The Mahalanobis-Taguchi Approach

Industry 4.0 and its innovative technologies (e.g., Internet of Things, Cyber-Physical Systems, Cloud Computing, Big Data and Artificial Intelligence) represent great promise. Still, companies experience hardship when transforming from reactive to predictive manufacturing systems. The latter, driven by data science development, use predictive models to detect and solve production and maintenance issues before they happen. 

Read More

Industry 4.0 Implementation Challenges and Opportunities: A Managerial Perspective

Industry 4.0 is a concept aimed at achieving the integration of physical parts of the manufacturing process (i.e., complex machinery, various devices, and sensors) and cyber parts (i.e., advanced software) via networks and driven by Industry 4.0 technology categories used for prediction, control, maintenance, and integration of manufacturing processes. Industry 4.0, which is expected to have a great impact on manufacturing systems in the future, is attracting attention in both industry and academia. Although academic research on Industry 4.0 is growing exponentially, evidence of Industry 4.0 implementation challenges are still main topic. 

Read More