In this paper, we describe a framework of analytics solution library for solving variety of industrial problems in resource and operations management. This Big Data enabled analytics solutions library is targeted for manufacturing industries including Electronics, Semiconductor, Automotive, Chemical & Petroleum, Oil & Gas industries, Energy & Utility, and Mining & Metals industries. The analytic library contains families of state of art analytics including predictive asset management, predictive failure analysis of process and equipment, process and equipment monitoring, analysis and optimization, predictive environmental analytics system and spatio-temporal analytics for safety and operational effectiveness. The analytics is powered by variety of advanced algorithms, is cataloged based on a comprehensive ontology, and guides common users without specialized training in mathematics with application specific workflows and a pre-engineered application programming interface (API), data structures and Graphical User Interface (GUI) widgets for visualizing input data and output solution. The analytics solution library can ingest remote sensing data from the Internet of Things (IoT) 3.0 foundations and is designed to consume variety and vast amount (i.e., Big Data) of structured and unstructured data including video, text and real time sensor data. The analytics services library bridges the gap between analytics algorithms and real business problems, and promotes the convergence of Information Technology (IT) and Operations Technology (OT) and promotes a vision of Industry 4.0 through effective and efficient utilization of advanced analytics provisioned through an increasingly sensored physical world.
By: Jayant Kalagnanam, Young Min Lee, Tsuyoshi Ide
Published in: RC25563 in 2015
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