Hybrid big data analytics and Industry 4.0 approach to projecting cycle time ranges

This study proposes a hybrid big data analytics and Industry 4.0 (BD-I4) approach to enhancing the effectiveness of cycle time range projections for factory jobs. 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 this approach, each expert first constructs a fuzzy deep neural network to project the cycle time range of a job, an application of big data analytics (i.e., deep learning). Subsequently, the fuzzy weighted intersection operator is applied to aggregate the projected cycle times such that unequal authority levels can be considered, an application of Industry 4.0 (i.e., artificial intelligence). Applying the BD-I4 approach to a real case that the proposed methodology improved the projection precision by up to 72%, suggesting that instead of relying on a single expert, collaboration among multiple experts may be more effective and efficient.

Automated summary

This study proposes a hybrid approach of big data analytics and Industry 4.0 to enhance the effectiveness of cycle time range projections for factory jobs. By utilizing fuzzy deep neural networks and fuzzy weighted intersection operator, this approach considers both deep learning and artificial intelligence and improves projection precision through collaboration among multiple experts.