Predictive Analytics within the Service Supply Chain

Gebraeel, Nagi

Title:

Predictive Analytics within the Service Supply Chain

dc.contributor.author	Gebraeel, Nagi
dc.contributor.corporatename	Georgia Institute of Technology. Supply Chain and Logistics Institute	en_US
dc.contributor.corporatename	Georgia Institute of Technology. School of Industrial and Systems Engineering	en_US
dc.date.accessioned	2017-04-17T16:21:53Z
dc.date.available	2017-04-17T16:21:53Z
dc.date.issued	2017-03-29
dc.description	Presented on March 29, 2017 at 12:00 p.m. in the Georgia Tech Callaway Manufacturing Building, Auditorium.	en_US
dc.description	Nagi Gebraeel is a Georgia Power Associate Professor in the Stewart School of Industrial & Systems Engineering at Georgia Tech. Dr. Gebraeel's research interests lie at the intersection of sensor-based Predictive Analytics, Machine Learning, and Asset Management. His key focus is on developing next-generation machine learning tools specifically tailored for real-time equipment diagnostics and prognostics that enable subsequent operational and logistical decision-making.	en_US
dc.description	Runtime: 52:21 minutes	en_US
dc.description.abstract	The Stewart School of Industrial and Systems Engineering at Georgia Tech established the Center for Predictive Analytics and Real-Time Optimization. (PARO). The center focuses on two main thrust areas. The first thrust area focuses on developing Predictive Analytic tools capable of synthesizing and extracting information from multi-stream sensor signals to predict future performance of complex engineering systems. The second thrust area deals with the development of real-time enhanced optimization models that compute optimal decision by leveraging the information embedded in the data. The development of modern methodologies allow for efficient updating when information changes as well as automatic model calibration using techniques from machine learning, information theory, and statistics. Housed in the Supply Chain and Logistics Institute, the Center for Predictive Analytics and Real-Time Optimization brings together experts from various disciplines. Drs. Gebraeel, Kvam, Paynabar, Pokutta, Ramudhin and Shi provide expertise in Data Mining and Statistical Analysis, Optimization, Diagnostics and Prognostics, Supply Chain, and Reliability, with domain expertise in the following industrial sectors; Automotive, Energy, Logistics, Airlines, Steel, Nanomanufacturing, Wind Power, and others. We provide various types of industries with a vehicle for addressing their problems through a single point of contact using a problem-driven approach.	en_US
dc.format.extent	52:21 minutes
dc.identifier.uri	http://hdl.handle.net/1853/56644
dc.language.iso	en_US	en_US
dc.relation.ispartofseries	Supply Chain and Logistics IRC Seminar Series	en_US
dc.subject	Predictive analytics	en_US
dc.subject	Service logistics	en_US
dc.title	Predictive Analytics within the Service Supply Chain	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.author	Gebraeel, Nagi
local.contributor.corporatename	Supply Chain and Logistics Institute
local.relation.ispartofseries	Supply Chain and Logistics Institute Seminar Series
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