Analytics as an Enabler of Advanced Manufacturing: A Critical Review of Tools and Applications

Abstract

Knowledge and information is a critical asset of a manufacturing enterprise. It enables a business to differentiate itself from competitors and to compete efficiently and effectively to the best of its ability. Information technology, telecommunications, and manufacturing are merging as the means of production becomes increasingly autonomous. Advanced manufacturing or Industry 4.0 is based on three interconnected pillars: 1) Computerized Product Design and Smart Technology, 2) Smart Sensors, Internet of Things and Data Collectors and 3) Analytics, Control and Data Science. This report is a critical review of the third pillar of data analytics, and how it is interconnected to the other two pillars. The objective is to present a context for a range of analytic challenges in Industry 4.0. We first provide a general introduction to advanced manufacturing elements, presents trends in modern analytics tools and technology. We then list challenges of analytics supporting Industry 4.0. The information quality (InfoQ) framework is proposed as a backbone to evaluate the analytics needed in Industry 4.0. The eight InfoQ dimensions are: 1) Data Resolution, 2) Data Structure, 3) Data Integration, 4) Temporal Relevance, 5) Chronology of Data and Goal, 6) Generalizability, 7) Operationalization and 8) Communication. These dimensions provide a classification of advanced manufacturing analytics domains. This review is providing background and directions for future research and implementations of analytics in Industry 4.0. The objective is to motivate researchers, practitioners and industrialists to develop Industry 4.0 deployment and expansion. A set of challenges in Analytics supporting Industry 4.0 is listed in the discussion.