Design and development of logistics workflow systems for demand management with RFID

Research highlights? In the responsive logistics information system, radio frequency identification can provide visibility of product flow and capture the real time inventory data. ? The captured data are analysed by online analytical process to identify the market segment. ? With advert of artificial neural network, the demand pattern is recognized and the corresponding replenishment strategy can be formulated. 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.

[1]  Jussi T. S. Heikkilä,et al.  From supply to demand chain management: efficiency and customer satisfaction , 2002 .

[2]  Walter Lang,et al.  Applying autonomous sensor systems in logistics—Combining sensor networks, RFIDs and software agents , 2006 .

[3]  Gérard P. Cachon,et al.  CAMPBELL SOUP'S CONTINUOUS REPLENISHMENT PROGRAM: EVALUATION AND ENHANCED INVENTORY DECISION RULES , 1997 .

[4]  Pingyu Jiang,et al.  RFID-based wireless manufacturing for walking-worker assembly islands with fixed-position layouts , 2007 .

[5]  Dirk Van Compernolle,et al.  Multilayer perceptrons as labelers for hidden Markov models , 1994, IEEE Trans. Speech Audio Process..

[6]  Wan-I Lee,et al.  The exploration of consumers' behavior in choosing hospital by the application of neural network , 2008, Expert Syst. Appl..

[7]  Sandip Lahiri,et al.  RFID Sourcebook , 2005 .

[8]  D. Thomason Strategic, tactical, operational [demand management] , 2004 .

[9]  F. Robert Jacobs,et al.  A Comparison of Reorder Point and Material Requirements Planning Inventory Control Logic , 1992 .

[10]  Carlos Cordón,et al.  Building Successful Customer—Supplier Alliances , 1998 .

[11]  Robert B. Johnston,et al.  Principles of digitally mediated replenishment of goods: electronic commerce and supply chain reform , 2000 .

[12]  Shensheng Zhang,et al.  A case study of an inter-enterprise workflow-supported supply chain management system , 2005, Inf. Manag..

[13]  Mahesh S. Raisinghani,et al.  Electronic commerce: opportunity and challenges , 2000 .

[14]  Henry C. W. Lau,et al.  A dynamic information schema for supporting product lifecycle management , 2006, Expert Syst. Appl..

[15]  Petri Hautaniemi,et al.  The choice of replenishment policies in an MRP environment , 1999 .

[16]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[17]  G. David Garson,et al.  Neural Networks: An Introductory Guide for Social Scientists , 1999 .

[18]  Garry White Strategic, Tactical, & Operational Management Security Model , 2009, J. Comput. Inf. Syst..

[19]  N. Karacapilidis,et al.  Enhanced supply chain management for e-business transactions , 2004 .

[20]  H. Lau,et al.  On a responsive supply chain information system , 2000 .

[21]  W. B. Lee,et al.  Design of a RFID case-based resource management system for warehouse operations , 2005, Expert Syst. Appl..

[22]  Hokey Min,et al.  Supply chain modeling: past, present and future , 2002 .

[23]  David C. Wyld,et al.  Where is my suitcase? RFID and airline customer service , 2005 .

[24]  K. Tan A framework of supply chain management literature , 2001 .

[25]  M. Frohlich,et al.  Demand chain management in manufacturing and services: web-based integration, drivers and performance , 2002 .

[26]  George E. Palmatier,et al.  Demand Management Best Practices: Process, Principles, and Collaboration , 2003 .

[27]  Anne Martensen,et al.  Analysing Customer Satisfaction Data: A Comparison of Regression and Artificial Neural Networks , 2005 .

[28]  James V. Rauff Data Mining: A Tutorial-Based Primer , 2005 .

[29]  D. Towill,et al.  Analysis and design of focused demand chains , 2002 .