How can decision support aid smart manufacturing initiatives?

Abstract

Manufacturing in developed nations must incorporate more data capture and decision support to control costs and maintain product quality. Digital transformation of manufacturing means production must be transformed using technologies like robotics, Internet of Things (IoT), Intelligent systems, and real-time analytics. Smart manufacturing means all aspects of production are transformed so they are data, computing, and decision support intensive. Smart manufacturing has been defined by i-SCOOP.eu as the "fully-integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs." Various decision support and data-driven capabilities must be incorporated in smart manufacturing systems, including: Data-Driven DSS Big Data Management in the Cloud can improve data management and distribution for both "machine-generated data" and "human-generated data." Real-time automated fault detection, classification and root-cause detection should be optimized using data from sensors. Finally, data-driven DSS should integrate real-time and special study data analysis. New and expanded data sources can enhance predictive analytics and output can be quickly shared using visualization tools. Knowledge-Driven DSS In a smart manufacturing environment, sharing expert domain knowledge at the manager-operator and operator-machine interface level is very important. Recommender systems and opinion mining can support real-time data-based decision making. Machine/user relationship mining and clustering can increase the self-awareness, self-learning, and self-maintenance of production systems. Finally, Reciprocal Learning-Based DSS (RL-DSS) can make repetitive decisions and reduce the human decision making load. Routine decision tasks can be learned and programmed. Then decision makers update their knowledge and help create more intelligent decisions than previously possible for semi-structured decisions.