Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31195
Title: Design and development of logistics workflow systems for demand management with RFID
Authors: Lee, CKM
Ho, W
Ho, GTS
Lau, HCW
Keywords: Artificial neural network
Backpropagation algorithm
Demand chain management
Logistics workflow
RFID
Supply chain management
Issue Date: 2011
Publisher: Pergamon Press
Source: Expert systems with applications, 2011, v. 38, no. 5, p. 5428-5437 How to cite?
Journal: Expert systems with applications 
Abstract: This paper discusses demand and supply chain management and examines how artificial intelligence techniques and RFID technology can enhance the responsiveness of the logistics workflow. This proposed system is expected to have a significant impact on the performance of logistics networks by virtue of its capabilities to adapt unexpected supply and demand changes in the volatile marketplace with the unique feature of responsiveness with the advanced technology, Radio Frequency Identification (RFID). Recent studies have found that RFID and artificial intelligence techniques drive the development of total solution in logistics industry. Apart from tracking the movement of the goods, RFID is able to play an important role to reflect the inventory level of various distribution areas. In today's globalized industrial environment, the physical logistics operations and the associated flow of information are the essential elements for companies to realize an efficient logistics workflow scenario. Basically, a flexible logistics workflow, which is characterized by its fast responsiveness in dealing with customer requirements through the integration of various value chain activities, is fundamental to leverage business performance of enterprises. The significance of this research is the demonstration of the synergy of using a combination of advanced technologies to form an integrated system that helps achieve lean and agile logistics workflow.
URI: http://hdl.handle.net/10397/31195
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2010.10.012
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