A Hybrid Big Data Analytics and Industry 4.0 Approach for Projecting The Cycle Time Range

Abstract To enhance the effectiveness of projecting the cycle time range of a job in a factory, a hybrid big data analytics and Industry 4.0 (BD-I4) approach is proposed in this study. As a joint application of big data analytics and Industry 4.0, the BD-I4 approach is distinct from existing methods in this field. In the BD-I4 approach, first, each expert constructs a fuzzy deep neural network (FDNN) to project the cycle time range of a job, which is an application of big data analytics (i.e., deep learning). Subsequently, fuzzy weighted intersection (FWI) is applied to aggregate the cycle time ranges projected by experts to consider their unequal authority levels, which is an application of Industry 4.0 (i.e., artificial intelligence). After applying the BD-I4 approach to a real case, the experimental results showed that the proposed methodology improved the projection precision by up to 72%. This result implied that instead of relying on a single expert, seeking the collaboration among multiple experts may be more effective and efficient.