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Artificial Intelligence Techniques for Networked Manufacturing Enterprises ManagementMulti-agent Simulation-based Decision Support System and Application in Networked Manufacturing Enterprises

Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management:... [The recent financial crisis has had a major negative impact on the global economy, and particularly had a significant impact on the global manufacturing industry due to the ever-decreasing customer demand. For manufacturing enterprises, especially those who run businesses in multiple countries, it is now a good time to operate in a smarter way and lead the new era that is taking shape underneath the present crisis. We introduce a multi-agent-based simulation tool in this chapter, with a description of the overall architecture, modelling elements, operational policies, etc. The tool has been used in a commercial project with a leading high-tech manufacturer. The complex relationships between service levels, inventory cost, transportation cost, and forecasting accuracy were well studied. The project results show that networked enterprises can really get better insight from such a quantitative analysis and would be able to identify solid opportunities for cost saving and performance improvement.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Artificial Intelligence Techniques for Networked Manufacturing Enterprises ManagementMulti-agent Simulation-based Decision Support System and Application in Networked Manufacturing Enterprises

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Publisher
Springer London
Copyright
© Springer-Verlag London 2010
ISBN
978-1-84996-118-9
Pages
87 –105
DOI
10.1007/978-1-84996-119-6_3
Publisher site
See Chapter on Publisher Site

Abstract

[The recent financial crisis has had a major negative impact on the global economy, and particularly had a significant impact on the global manufacturing industry due to the ever-decreasing customer demand. For manufacturing enterprises, especially those who run businesses in multiple countries, it is now a good time to operate in a smarter way and lead the new era that is taking shape underneath the present crisis. We introduce a multi-agent-based simulation tool in this chapter, with a description of the overall architecture, modelling elements, operational policies, etc. The tool has been used in a commercial project with a leading high-tech manufacturer. The complex relationships between service levels, inventory cost, transportation cost, and forecasting accuracy were well studied. The project results show that networked enterprises can really get better insight from such a quantitative analysis and would be able to identify solid opportunities for cost saving and performance improvement.]

Published: Jan 1, 2010

Keywords: Supply Chain; Supply Chain Management; Forecast Accuracy; Fill Rate; Bullwhip Effect

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