In manufacturing, an enormous amount of data, both real and simulation data, is being continuously generated. The appropriate information, if extracted from big data, could provide insights on increasing sustainability, productivity, flexibility, and competitive advantages and eventually contribute to achieving the objectives of smart manufacturing on agility, asset utilization, and sustainability. The challenge is to reduce information overload for manufacturing and filter useful information to get the same detail level of manufacturing insights. The adoption of manufacturing data analytics in a timely manner can facilitate moving traditional manufacturing to agile, and eventually, smart manufacturing. This paper addresses how to apply a standardized predictive modeling technique onto manufacturing data analytics applications.
2014 Simulation Interoperability Workshop (SIW)
September 7-12, 2014
Data analytics, manufacturing, predictive model, PMML