Analytics-driven asset management

Arun Hampapur*, Heng Cao, Andrew Davenport, Weishan S. Dong, Don Fenhagen, Rogerio S. Feris, Germán Goldszmidt, Zhongbo B. Jiang, Jayant Kalagnanam, Tarun Kumar, Hongfei Li, Xuan Liu, Shilpa Mahatma, Sharath Pankanti, Dan Pelleg, Wei Sun, Mary Taylor, Chun Hua Tian, Segev Wasserkrug, Lexing XieMujib Lodhi, Charles Kiely, Kevin Butturff, Louis Desjardins

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
JournalIBM Journal of Research and Development
Volume55
Issue number1-2
DOIs
Publication statusPublished - Jan 2011
Externally publishedYes

Fingerprint

Dive into the research topics of 'Analytics-driven asset management'. Together they form a unique fingerprint.

Cite this