DocumentCode :
2059886
Title :
RiskVis: Supply chain visualization with risk management and real-time monitoring
Author :
Goh, Rick Siow Mong ; Zhaoxia Wang ; Xiaofeng Yin ; Xiuju Fu ; Ponnambalam, Loganathan ; Sifei Lu ; Xiaorong Li
Author_Institution :
Dept. of Comput. Sci., Inst. of High Performance Comput., Singapore, Singapore
fYear :
2013
fDate :
17-20 Aug. 2013
Firstpage :
207
Lastpage :
212
Abstract :
With increased complexity, supply chain networks (SCNs) of modern era face higher risks and lower efficiency due to limited visibility. Hence, there is an immediate need to provide end-to-end supply chain visibility for efficient management of complex supply chains. This paper proposes a visualization scheme based on multi-hierarchical modular design and develops a supply chain visualization platform with risk management and real-time monitoring, named RiskVis, for realizing better Supply Chain Risk Management (SCRM). A Supply Chain Visualizer (SCV) with a graphical visualization platform is mounted as a part of a SCRM management decision-making dashboard and it provides senior management a clearer view of supply chain operations in a local/regional/global setting. The platform not only displays spatio-temporal connectivity patterns of entities in a supply chain; it also accommodates real-time risk-related data collection and risk monitoring. The proposed platform offers the flexibility to be customized based on the user´s requirements - to process and store the supply chain data in the server, visualize the supply chain data, network map, risk alert, and other information needed for SCRM. Supply chain decision makers can deploy it on the desktop or embed it into the company´s enterprise applications in a front office environment for better managing risks of their supply chains.
Keywords :
data visualisation; decision support systems; risk management; supply chain management; RiskVis platform; SCN; SCRM; decision-making dashboard; graphical visualization platform; multihierarchical modular design; realtime risk-related data collection; risk monitoring; spatio-temporal connectivity patterns; supply chain entities; supply chain management; supply chain networks; supply chain risk management; supply chain visualization; supply chain visualizer; Companies; Complexity theory; Data visualization; Monitoring; Real-time systems; Risk management; Supply chains; Multi-hierarchical modular design; Real-time risk monitoring; Supply Chain Risk Management (SCRM); Supply Chain Visualizer (SCV); Supply chain networks; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automation Science and Engineering (CASE), 2013 IEEE International Conference on
Conference_Location :
Madison, WI
ISSN :
2161-8070
Type :
conf
DOI :
10.1109/CoASE.2013.6653910
Filename :
6653910
Link To Document :
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