Decision and Information Synergy for Improving Product Recovery Performance

An approach for demonstrating the impact of product information and decision synergy on the effectiveness of product recovery decisions is vital. The evaluation approach presented in this paper suggests various actors that can play a part in a product’s lifecycle to quantify the benefit of making relevant product information available for decisions to the product recoverer. The expected benefit of information is positive if and only if at least one of the outcomes of the observation has the ability to change (flexible) the recovery decision maker’s alternative of the recovery option. Therefore, it was observed that availability of information need not always yield a positive benefit in terms of improved decision leading to improved recovery system performance. This led us to identify the conditions of synergy for improving decision by collecting more or appropriate level of information. This paper also proposes a semi or partially flexible decision model that facilitates flexible decision and information interoperability functions from the perspective of an enterprise engaged in or to be engaged in product recovery.

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

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

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

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

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

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

[7]  Rommert Dekker,et al.  A stochastic approach to a case study for product recovery network design , 2005, Eur. J. Oper. Res..

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

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

[10]  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).

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

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

[13]  Adrien Presley,et al.  The theory and practice of Reverse Logistics , 2007 .

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

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

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

[17]  Elif Akçali,et al.  Benders decomposition with alternative multiple cuts for a multi‐product closed‐loop supply chain network design model , 2007 .

[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 .

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

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