Title :
Data analytics using simulation for smart manufacturing
Author :
Guodong Shao ; Seung-Jun Shin ; Jain, Sonal
Author_Institution :
Syst. Integration Div., Nat. Inst. of Stand. & Technol., Gaithersburg, MD, USA
Abstract :
Manufacturing organizations are able to accumulate large amounts of plant floor production and environmental data due to advances in data collection, communications technology, and use of standards. The challenge has shifted from collecting a sufficient amount of data to analyzing and making decisions based on the huge amount of data available. Data analytics (DA) can help understand and gain insights from the big data and in turn help advance towards the vision of smart manufacturing. Modeling and simulation have been used by manufacturers to analyze their operations and support decision making. This paper proposes multiple methods in which simulation can serve as a DA application or support other DA applications in manufacturing environment to address big data issues. An example case is discussed to demonstrate one use of simulation. In the presented case, a virtual representation of machining operations is used to generate the data required to evaluate manufacturing data analytics applications.
Keywords :
Big Data; data analysis; decision making; digital simulation; industrial plants; machining; manufacturing industries; Big Data; DA; communications technology; data collection; decision making; environmental data; machining operations; manufacturing data analytics applications; manufacturing organizations; plant floor production; simulation; smart manufacturing; virtual representation; Analytical models; Data analysis; Data models; Manufacturing; Predictive models; Production facilities; Virtual manufacturing;
Conference_Titel :
Simulation Conference (WSC), 2014 Winter
Conference_Location :
Savanah, GA
Print_ISBN :
978-1-4799-7484-9
DOI :
10.1109/WSC.2014.7020063