Abstract Ron S. Kenett
The elements of analytics in the context of big data include: i) Sensors and other data acquisition devices, ii) Cloud infrastructure to store raw data, iii) Data bus to receive data from the cloud, iv) Data storage and management system, to save relevant data, v) Data analytics engine and vi) Data visualization and rendering systems. The role of analytics in the context of advanced manufacturing (manufacturing 4.0) is to dramatically intensify applications of smart manufacturing using advanced data analytics, modeling and simulations, to produce a fundamental transformation to new product-based economics, flexible factories and demand-driven supply chain service enterprises.
Keywords: Manufacturing 4.0, information quality, manufacturing analytics, big data
The talk will review and evaluate the various elements of data analytics in advanced manufacturing analytics. These methods are used to coordinate enterprise responses throughout the entire manufacturing supply chain, deploy predictive and preventive maintenance, and provide an integrated environment for engineering and multivariate process control. Additional objectives of analytics include minimizing energy and material usage and maximizing environmental sustainability, health and safety and economic competitiveness. The evaluation we present is based on the information quality (InfoQ) framework proposed by Kenett and Shmueli (JRSS, 2014). For a high level overview of InfoQ dimensions, see