Efficient knowledge integration to support a complex supply network management

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.