Title: Efficient knowledge integration to support a complex supply network management

Authors: Xiangyang Li, Charu Chandra

Addresses: Industrial and Manufacturing Systems Engineering, University of Michigan – Dearborn, 4901 Evergreen Road, Dearborn MI 48128, USA. ' Industrial and Manufacturing Systems Engineering, University of Michigan – Dearborn, 4901 Evergreen Road, Dearborn MI 48128, USA

Abstract: Modern manufacturing and logistics witness new enterprise paradigms that consist of heterogeneous supply, production and service networks distributed across a large geographical region. With the aid of emerging techniques such as sensor networks and RFID tagging, information integration is a key to addressing the challenge in efficiently managing such complex Supply Networks (SNs). An adaptive knowledge fusion framework is proposed in this paper that consists of dependency modelling, active configuration planning and scheduling and quality assurance of knowledge integration. We use cases of supply chain risk management and knowledge network in customer service to elaborate the problem and then describe our framework. Some initial results for the proposed Bayesian approach are presented thereafter. We conclude by describing the implication and future research issues of this proposition.

Keywords: knowledge integration; sensor networks; complex systems; RFID; radio frequency identification; supply networks; information integration; knowledge fusion; dependency modelling; configuration planning; scheduling; quality assurance; supply chain management; risk management; SCM; customer service.

DOI: 10.1504/IJMTM.2007.011398

International Journal of Manufacturing Technology and Management, 2007 Vol.10 No.1, pp.1 - 18

Published online: 30 Nov 2006 *

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