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Supply chain network risk analysis - a privacy preserving approach

 
: Fridgen, Gilbert; Zare Garizy, Tirazheh

:

Association for Information Systems -AIS-:
23rd European Conference on Information Systems, ECIS 2015. Online resource : Completed Research Papers, 26 - 29 May 2015, Münster
AIS Electronic Library (AISeL), 2015
http://aisel.aisnet.org/ecis2015_cr/
ISBN: 978-3-00-050284-2
Paper 49
European Conference on Information Systems (ECIS) <23, 2015, Münster>
Englisch
Konferenzbeitrag
Fraunhofer FIT ()
algorithms; multiparty computation; privacy; risk management; supply chain network

Abstract
Globalization and outsourcing are two main factors which are leading to higher complexity of supply chain networks. Today’s complex distributed supply chain networks are vulnerable to various kinds of risks. Due to the strategic importance of having a sustainable network it is necessary to have an enhanced supply chain network risk management. The first step in the risk management is risk identification. In a supply chain network many firms depend directly or indirectly on a specific supplier. Any failure in a supplier’s production can risk the whole network’s robustness. In this regard, unknown risks of network’s structure can endanger the whole network’s robustness. In spite of the importance of risk identification of supply chain network, companies are not willing to exchange the structural information of their network. Firms are concerned about risking their strategic positioning or established connections in the network. Combining the secure multiparty computation cryptography methods with risk identification algorithms driven from social network analysis, is the solution of this paper for this challenge. With this combination we enable structural risk identification of supply chain networks without endangering companies’ competitive advantage.

: http://publica.fraunhofer.de/dokumente/N-391986.html