Flexibility Focused Decision and Information Sharing Model for Product Recovery System

As a result of rapid progress in technology and the shrinking product lifecycles faster than ever before, has led creeping realization of additional profits by performing the effective and efficient product recovery operations at a world-class level quintessential. Realization of these motives is complex due to the multidimensional relationships associated with the quality, variety, timeliness, demand changes, and logical processing of product returns and inherent complexity of recovery process. Therefore an Enterprise System (ES) perspective will give us a scope to develop a profit oriented recovery process as a flexible system that can handle products with various options and greater return volume and structural variability. This paper proposes generic model to enterprises engaged in or to be engaged in product recovery processes. A semi or partially flexible decision process model that facilitates flexible decision and information sharing (DIS) functions in product returns. This DIS model leads us to conceptualize the evolution of information associated with a product returns and how it might be encapsulated by Reverse Enterprise System (RES) to improve its profit and system performance.

[1]  Erwin van der Laan,et al.  Quantitative models for reverse logistics: A review , 1997 .

[2]  Ronald S. Tibben-Lembke,et al.  AN EXAMINATION OF REVERSE LOGISTICS PRACTICES , 2001 .

[3]  V. Guide Production planning and control for remanufacturing: industry practice and research needs , 2000 .

[4]  Charles R. McLean,et al.  Disassembly of products , 1996 .

[5]  Subhash Wadhwa,et al.  Flexible Process Planning Approaches for Sustainable Decisions in Reverse Logistics System , 2007 .

[6]  A. Campbell,et al.  Corporate strategy: The quest for parenting advantage , 1995 .

[7]  P. C. Schuur,et al.  Business case Océ: Reverse logistic network re-design for copiers , 1999 .

[8]  L. Wein,et al.  Inventory Management of Remanufacturable Products , 2000 .

[9]  Subhash Wadhwa,et al.  A Genetic Algorithm Based Scheduling for a Flexible System , 2007 .

[10]  D. Pujari,et al.  Green and competitive: Influences on environmental new product development performance , 2003 .

[11]  D. Guide,et al.  Business Aspects of Closed-Loop Supply Chains , 2003 .

[12]  Valerie M. Thomas,et al.  Information technology and product lifecycle management , 1999, Proceedings of the 1999 IEEE International Symposium on Electronics and the Environment (Cat. No.99CH36357).

[13]  Michael J. Magazine,et al.  Quantitative Models for Supply Chain Management , 1998 .

[14]  Jacqueline M. Bloemhof-Ruwaard,et al.  THE IMPACT OF PRODUCT RECOVERY ON LOGISTICS NETWORK DESIGN , 2001 .

[15]  Gilvan C. Souza,et al.  Reverse Supply Chains for Commercial Returns , 2004 .

[16]  S. Wadhwa,et al.  Modeling FMS with decision Petri nets , 1989 .

[17]  Shad Dowlatshahi,et al.  Developing a Theory of Reverse Logistics , 2000, Interfaces.

[18]  Keely L. Croxton,et al.  THE RETURNS MANAGEMENT PROCESS , 2002 .

[19]  P. Daugherty,et al.  INFORMATION SUPPORT FOR REVERSE LOGISTICS: THE INFLUENCE OF RELATIONSHIP COMMITMENT , 2002 .