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A multi-criteria decision making methodology for the selection of reverse logistics operating modes

Author

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  • P. Sasikumar
  • A. Noorul Haq

Abstract

Logistics plays an important role in business today. It is not possible to succeed in marketing, manufacturing or project execution without the logistics support. Customers expect logisticians to provide better transportation service, on time delivery, operational flexibility and reduced damage-in-transit. Manufacturing companies are under a great pressure to find the right solution to tackle these challenges. Reverse logistics (RL) is one type of logistics which plays an important part of supply chain management because of environmental legislation and increasing awareness among the customer. In order to concentrate on their core business operations and to achieve cost-efficiency and improve delivery performance, the companies have to choose appropriate operating modes for reverse logistics operations. There are three basic operating modes of reverse logistics for enterprise: self-support, joint venture and outsourcing. The objective of this paper is to efficiently assist the decision makers in determining the 'most appropriate' reverse logistics operating modes using multi-criteria decision making (MCDM) model. For this purpose analytic hierarchy process (AHP) is used as a MCDM methodology. Finally, an example has been illustrated to highlight the procedural implementation of the proposed model.

Suggested Citation

  • P. Sasikumar & A. Noorul Haq, 2010. "A multi-criteria decision making methodology for the selection of reverse logistics operating modes," International Journal of Enterprise Network Management, Inderscience Enterprises Ltd, vol. 4(1), pages 68-79.
    • Handle: RePEc:ids:ijenma:v:4:y:2010:i:1:p:68-79
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    Citations

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    Cited by:

    1. Marta Pérez-Pérez & Canan Kocabasoglu-Hillmer & Ana María Serrano-Bedia & María Concepción López-Fernández, 2019. "Manufacturing and Supply Chain Flexibility: Building an Integrative Conceptual Model Through Systematic Literature Review and Bibliometric Analysis," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(1), pages 1-23, December.
    2. C.G. Sreenivasa & S.R. Devadasan & N.M. Sivaram & S. Karthi, 2012. "Resource optimisation through artificial neural network for handling supply chain constraints," International Journal of Logistics Economics and Globalisation, Inderscience Enterprises Ltd, vol. 4(1/2), pages 5-19.

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