%0 Journal Article %1 LadeGhoshSrinivasan17intelligent %A Lade, Prasanth %A Ghosh, Rumi %A Srinivasan, Soundar %D 2017 %J IEEE Intelligent Systems %K 01801 ieee paper embedded ai factory device data analysis processing pattern recognition learn zzz.i40 %N 3 %P 74--79 %R 10.1109/MIS.2017.49 %T Manufacturing Analytics and Industrial Internet of Things %V 32 %X Over the last two decades, manufacturing across the globe has evolved to be more intelligent and data driven. In the age of industrial Internet of Things, a smart production unit can be perceived as a large connected industrial system of materials, parts, machines, tools, inventory, and logistics that can relay data and communicate with each other. While, traditionally, the focus has been on machine health and predictive maintenance, the manufacturing industry has also started focusing on analyzing data from the entire production line. These applications bring a new set of analytics challenges. Unlike traditional data mining analysis, which consists of lean datasets (that is, datasets with few features), manufacturing has fat datasets. In addition, previous approaches to manufacturing analytics restricted themselves to small time periods of data. The latest advances in big data analytics allows researchers to do a deep dive into years of data. Bosch collects and utilizes all available information about its products to increase its understanding of complex linear and nonlinear relationships between parts, machines, and assembly lines. This helps in use cases such as the discovery of the root cause of internal defects. This article presents a case study and provides detail about challenges and approaches in data extraction, modeling, and visualization.

@article{LadeGhoshSrinivasan17intelligent, abstract = {Over the last two decades, manufacturing across the globe has evolved to be more intelligent and data driven. In the age of industrial Internet of Things, a smart production unit can be perceived as a large connected industrial system of materials, parts, machines, tools, inventory, and logistics that can relay data and communicate with each other. While, traditionally, the focus has been on machine health and predictive maintenance, the manufacturing industry has also started focusing on analyzing data from the entire production line. These applications bring a new set of analytics challenges. Unlike traditional data mining analysis, which consists of lean datasets (that is, datasets with few features), manufacturing has fat datasets. In addition, previous approaches to manufacturing analytics restricted themselves to small time periods of data. The latest advances in big data analytics allows researchers to do a deep dive into years of data. Bosch collects and utilizes all available information about its products to increase its understanding of complex linear and nonlinear relationships between parts, machines, and assembly lines. This helps in use cases such as the discovery of the root cause of internal defects. This article presents a case study and provides detail about challenges and approaches in data extraction, modeling, and visualization.}, added-at = {2017-10-30T17:43:23.000+0100}, author = {Lade, Prasanth and Ghosh, Rumi and Srinivasan, Soundar}, biburl = {https://www.bibsonomy.org/bibtex/23813417716dc5144e3fed74d5202cb80/flint63}, doi = {10.1109/MIS.2017.49}, file = {IEEE Digital Library:2017/LadeGhoshSrinivasan17intelligent.pdf:PDF}, groups = {public}, interhash = {51d412a1ae7abc54ae6d28491e9d8f8b}, intrahash = {3813417716dc5144e3fed74d5202cb80}, issn = {1541-1672}, journal = {IEEE Intelligent Systems}, keywords = {01801 ieee paper embedded ai factory device data analysis processing pattern recognition learn zzz.i40}, number = 3, pages = {74--79}, timestamp = {2018-04-16T12:36:39.000+0200}, title = {Manufacturing Analytics and {Industrial Internet of Things}}, username = {flint63}, volume = 32, year = 2017 }