TY - JOUR
T1 - Big Data for supply chain management in the service and manufacturing sectors
T2 - Challenges, opportunities, and future perspectives
AU - Zhong, Ray Y.
AU - Newman, Stephen T.
AU - Huang, George Q.
AU - Lan, Shulin
PY - 2016/11
Y1 - 2016/11
N2 - Data from service and manufacturing sectors is increasing sharply and lifts up a growing enthusiasm for the notion of Big Data. This paper investigates representative Big Data applications from typical services like finance & economics, healthcare, Supply Chain Management (SCM), and manufacturing sector. Current technologies from key aspects of storage technology, data processing technology, data visualization technique, Big Data analytics, as well as models and algorithms are reviewed. This paper then provides a discussion from analyzing current movements on the Big Data for SCM in service and manufacturing world-wide including North America, Europe, and Asia Pacific region. Current challenges, opportunities, and future perspectives such as data collection methods, data transmission, data storage, processing technologies for Big Data, Big Data-enabled decision-making models, as well as Big Data interpretation and application are highlighted. Observations and insights from this paper could be referred by academia and practitioners when implementing Big Data analytics in the service and manufacturing sectors.
AB - Data from service and manufacturing sectors is increasing sharply and lifts up a growing enthusiasm for the notion of Big Data. This paper investigates representative Big Data applications from typical services like finance & economics, healthcare, Supply Chain Management (SCM), and manufacturing sector. Current technologies from key aspects of storage technology, data processing technology, data visualization technique, Big Data analytics, as well as models and algorithms are reviewed. This paper then provides a discussion from analyzing current movements on the Big Data for SCM in service and manufacturing world-wide including North America, Europe, and Asia Pacific region. Current challenges, opportunities, and future perspectives such as data collection methods, data transmission, data storage, processing technologies for Big Data, Big Data-enabled decision-making models, as well as Big Data interpretation and application are highlighted. Observations and insights from this paper could be referred by academia and practitioners when implementing Big Data analytics in the service and manufacturing sectors.
KW - Big Data
KW - Manufacturing sector
KW - Service applications
KW - Supply Chain Management (SCM)
UR - http://www.scopus.com/inward/record.url?scp=84978977044&partnerID=8YFLogxK
UR - http://dx.doi.org/10.1016/j.cie.2016.07.013
U2 - 10.1016/j.cie.2016.07.013
DO - 10.1016/j.cie.2016.07.013
M3 - Article
AN - SCOPUS:84978977044
SN - 0360-8352
VL - 101
SP - 572
EP - 591
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
ER -