Survey on Data-Driven Decision Support Frameworks in Industrial IoT (IIoT)-Based Manufacturing

Abstract

 Industrial Internet of Things (IIoT) has become a revolution in the smart manufacturing paradigm as it provides pervasive connectivity, real-time data collection, and intelligent automation of industrial systems. In IIoT-enabled manufacturing, large volumes of heterogeneous data are constantly generated by sensors, machines, and enterprise systems, which must be used effectively to implement the data-driven decision support frameworks. This decision-making process relies on machine learning, artificial intelligence, edge-to-cloud computing, and optimization of processes, quality control, and real-time operational decisions. Using a manufacturing example, examine the following decision support system paradigms: data-driven, model-driven, knowledge-driven, document-driven, and communication-driven. Moreover, the key issues like data heterogeneity, complexity of integration, scalability, security, privacy, and interpretability of the model are also discussed, which can be used to consider creating robust, scalable, and intelligent IIoT-based data-driven decision support systems to be used in next-generation smart manufacturing systems