Supply chain vulnerability assessment: A network based visualization and clustering analysis approach

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2017-11-03
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Blackhurst, Jennifer
Rungtusanatham, M. Johnny
Ambulkar, Saurabh
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Scheibe, Kevin
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Supply Chain Management
Supply chain management is an integrated program of study concerned with the efficient flow of materials, products, and information within and among organizations. It involves the integration of business processes across organizations, from material sources and suppliers through manufacturing, and processing to the final customer. The program provides you with the core knowledge related to a wide variety of supply chain activities, including demand planning, purchasing, transportation management, warehouse management, inventory control, material handling, product and service support, information technology, and strategic supply chain management.
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Supply chains are large, complex, and often unpredictable. Purchasing and supply managers and supply chain risk managers need methods and tools to enable them to quickly understand how unexpected disruptions in the supply chain start and grow and to what extent will they negatively impact the flow of goods and services. This paper introduces a methodological approach that can be used by both researchers and managers to quickly visualize a supply chain, map out the propagation path of disruptive events from the supply side to the end customer and understand potential weaknesses in the supply chain design; taking into account the structure, connectivity, and dependence within the supply chain. The approach incorporates a Petri net and Triangularization Clustering Algorithm to offer insights into a supply chain network's vulnerabilities and can be used to efficiently assess supply chain disruption mitigation strategies, especially in complex and difficulty to analyze supply chain systems.

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This article is published as Blackhurst, J., Rungtusanatham, M.J., Scheibe, K.P., Ambulkar, S., Supply chain vulnerability assessment: A network based visualization and clustering analysis approach. Journal of Purchasing and Supply Management, Nov 3 2017, 24(1); 21-30. DOI: 10.1016/j.pursup.2017.10.004. Posted with permission.

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Sun Jan 01 00:00:00 UTC 2017
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