Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/190641 
Year of Publication: 
2012
Citation: 
[Journal:] International Journal of Business Science & Applied Management (IJBSAM) [ISSN:] 1753-0296 [Volume:] 7 [Issue:] 3 [Publisher:] International Journal of Business Science & Applied Management [Place:] s.l. [Year:] 2012 [Pages:] 12-22
Publisher: 
International Journal of Business Science & Applied Management, s.l.
Abstract: 
One of the critical parameters in reverse supply chain management is the increased variability of the quality condition of used, returned products. The volatile nature of returns' quality often dictates the establishment of quality assessment procedures and the development of technologies that facilitate the fast, accurate and inexpensive classification of returns. The appropriate degree in which a firm has to allocate resources for acquiring information on the quality of returned units, naturally, depends on the anticipated improvement of recovery activities' profitability. Therefore, the quantification of the savings associated with confronting or resolving quality uncertainty is a necessary input during the determination of the proper recovery procedures' configuration. In the current paper, we study a remanufacturing system in a multi-period setting in which returns' quality information is exploited during remanufacturing planning. However, in the decision-making process, certain aspects of the problem examined, such as the quantification of shortage cost, are overlooked or simplified. The objective is to examine the advisability of acquiring advanced quality information in order to be used during sub-optimal decision-making processes, in comparison with alternative policies which do not take explicitly into account returns' quality information. Moreover, through an extensive numerical analysis we examine the implications of alternative considerations regarding returned units' quality on remanufacturing planning, lead-time and service-levels and evaluate their impact on the overall system operational cost.
Subjects: 
logistics policies
quality of returns
inventory management
stochastic lead-time
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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