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An Agent-based Approach to Enhance Supply Chain Agility in a Heterogeneous Environment

  • Chun-Che Huang
  • Tzu-Liang Bill Tseng
  • Horng-Fu Chuang
  • Yu-Neng Fan
Chapter
  • 2.4k Downloads
Part of the Springer Series in Advanced Manufacturing book series (SSAM)

Abstract

Businesses are undergoing a major paradigm shift, moving from traditional management into a world of agile organizations and processes. An agile enterprise should be able to respond rapidly to market changes. For this reason, enterprises have been seeking to develop numerous information technology (IT) systems to assist with their business processes. However, by their very nature, information/knowledge in the systems is disparate and heterogeneous and can be represented in various ways (text, pdf, html, etc.) and can be either structured or unstructured. It is, therefore, difficult to acquire, organize, or distribute information/knowledge using only traditional information technology methods such as e-mail or file servers. Because of the autonomous and collaborative aspects inherent in agent-based technology, this may be a possible solution to the problem of heterogeneity. Agent technology radically alters not only the way in which computers interact, but also the way complex processes, e.g., supply chains are conceptualized and built. This chapter proposes an agent-based system and formulates agile interaction for the collaboration of supply chain entities in a heterogeneous environment. The agent-based system enhances supply chain agility through the approach in which the agents autonomously plan and pursue their objectives and subgoals to cooperate, coordinate, and negotiate with others and to respond flexibly and intelligently to dynamic and unpredictable situations. Annotation and articulation mechanisms are developed to address and solve the heterogeneity problem of information/knowledge resources.

Keywords

Supply Chain Supply Chain Management MultiAgent System Incoming Message Semantic Interoperability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • Chun-Che Huang
    • 1
  • Tzu-Liang Bill Tseng
    • 2
  • Horng-Fu Chuang
    • 3
  • Yu-Neng Fan
    • 1
  1. 1.Department of Information ManagementNational Chi-Nan UniversityTaiwan
  2. 2.Department of Mechanical and Industrial EngineeringThe University of Texas at El PasoUSA
  3. 3.Department of Accounting InformationDa-Yeh UniversityTaiwan

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