Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2013

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

Multi-hierarchical modular design, Real-time risk monitoring, Supply chain networks, Supply Chain Risk Management (SCRM), Supply Chain Visualizer (SCV), Visualization

Discipline

Numerical Analysis and Scientific Computing | Operations and Supply Chain Management | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

2013 IEEE International Conference on Automation Science and Engineering (CASE): August 17-20, Madison, WI: Proceedings

First Page

207

Last Page

212

ISBN

9781479915156

Identifier

10.1109/CoASE.2013.6653910

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/CoASE.2013.6653910

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