Abstract—Reverse logistics (RL) stands for all the operations related to the reuse of used products, excess inventory of products and materials including collection, disassembly and processing of used products, parts, and/or materials. Over the past few years, RL has received much attention because many companies are using it as a strategic tool to serve their customers and can generate good revenue. An efficient reverse distribution structure may lead to a significant return on investment as well as a significantly increased competitiveness in the market. Therefore, analysis of barriers hindering the successful implementation of RL is a crucial issue. These barriers not only affect RL but influence each other also. In existing models, the holistic view in understanding the interrelation between the barriers is not accounted for but is diagnosed independently. This paper utilizes the Interpretive Structural Modeling (ISM) methodology to understand the mutual influences among the barriers so that barriers that are at the root of some more barriers (called driving barriers) and those which are most influenced by the others (called driven barriers) are identified.
Index Terms—Barriers, Interpretive Structural Modeling (ISM) , Reverse logistics(RL), transitivity.
Santosh Kumar Sharma is with the National Institute of Technology, Rourkela India (e-mail: santoshsharma@csitdurg.in)
Biranchi Narayan Panda is with the National Institute of Technology, Rourkela India (e-mail : Biranchi.panda3@gmail.com)
Siba Sankar Mahapatra is with the National Institute of Technology, Rourkela 769008 India (phone: 91-661-2462512; fax: 91-661-2472926 e-mail: mahapatrass2003@yahoo.com).
Sadanand Sahu is with the Indian Institute of Technology, Kharagpur, India (e-mail: sahus@mechiitkgp.ernet.in)
Cite: S. K. Sharma, B. N. Panda, S. S. Mahapatra, and S. Sahu, "Analysis of Barriers for Reverse Logistics: An Indian Perspective," International Journal of Modeling and Optimization vol. 1, no. 2, pp. 101-106, 2011.
Copyright © 2008-2024. International Journal of Modeling and Optimization. All rights reserved.
E-mail: ijmo@iacsitp.com