TY - JOUR
T1 - Analytics-driven asset management
AU - Hampapur, Arun
AU - Cao, Heng
AU - Davenport, Andrew
AU - Dong, Weishan S.
AU - Fenhagen, Don
AU - Feris, Rogerio S.
AU - Goldszmidt, Germán
AU - Jiang, Zhongbo B.
AU - Kalagnanam, Jayant
AU - Kumar, Tarun
AU - Li, Hongfei
AU - Liu, Xuan
AU - Mahatma, Shilpa
AU - Pankanti, Sharath
AU - Pelleg, Dan
AU - Sun, Wei
AU - Taylor, Mary
AU - Tian, Chun Hua
AU - Wasserkrug, Segev
AU - Xie, Lexing
AU - Lodhi, Mujib
AU - Kiely, Charles
AU - Butturff, Kevin
AU - Desjardins, Louis
PY - 2011/1
Y1 - 2011/1
N2 - Asset-intensive businesses across industries rely on physical assets to deliver services to their customers, and effective asset management is critical to the businesses. Today, businesses may make use of enterprise asset-management (EAM) solutions for many asset-related processes, ranging from the core asset-management functions to maintenance, inventory, contracts, warranties, procurement, and customer-service management. While EAM solutions have transformed the operational aspects of asset management through data capture and process automation, the decision-making process with respect to assets still heavily relies on institutional knowledge and anecdotal insights. Analytics-driven asset management is an approach that makes use of advanced analytics and optimization technologies to transform the vast amounts of data from asset management, metering, and sensor systems into actionable insight, foresight, and prescriptions that can guide decisions involving strategic and tactical assets, as well as customer and business models.
AB - Asset-intensive businesses across industries rely on physical assets to deliver services to their customers, and effective asset management is critical to the businesses. Today, businesses may make use of enterprise asset-management (EAM) solutions for many asset-related processes, ranging from the core asset-management functions to maintenance, inventory, contracts, warranties, procurement, and customer-service management. While EAM solutions have transformed the operational aspects of asset management through data capture and process automation, the decision-making process with respect to assets still heavily relies on institutional knowledge and anecdotal insights. Analytics-driven asset management is an approach that makes use of advanced analytics and optimization technologies to transform the vast amounts of data from asset management, metering, and sensor systems into actionable insight, foresight, and prescriptions that can guide decisions involving strategic and tactical assets, as well as customer and business models.
UR - http://www.scopus.com/inward/record.url?scp=81155135216&partnerID=8YFLogxK
U2 - 10.1147/JRD.2010.2092173
DO - 10.1147/JRD.2010.2092173
M3 - Article
SN - 0018-8646
VL - 55
JO - IBM Journal of Research and Development
JF - IBM Journal of Research and Development
IS - 1-2
ER -