Simulation model of reverse logistic in computer industry

This project focuses on the reverse logistics network modeling by using Arena Simulation Software version 10.0 and also on the analysis of the result after simulation run. The objective as well as the scope of the project are defined in the introductory chapters. Data used in simulation and detailed...

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Bibliographic Details
Main Author: Ario Prabowo
Other Authors: Shaligram Pokharel
Format: Final Year Project
Language:English
Published: 2009
Subjects:
Online Access:http://hdl.handle.net/10356/18433
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Institution: Nanyang Technological University
Language: English
Description
Summary:This project focuses on the reverse logistics network modeling by using Arena Simulation Software version 10.0 and also on the analysis of the result after simulation run. The objective as well as the scope of the project are defined in the introductory chapters. Data used in simulation and detailed explanation of how to implement the RL network into Arena software is also provided and elaborated in the Appendix. In this project, the reverse logistics network is modeled as an independent reverse channel from forward logistics network. The flow of returned entities starts from costumer through collection points (retailers). From there, the returned entities will then be transferred to reprocessing centers and then redistribute back to the consumers through distribution centers. Recycling, disposal, refurbishment, storing and reselling of used modules from returned entities are considered in the simulation. The use of the model is shown through its application in numerical examples. At the end of the project, it can be concluded that the cost of procuring new modules for remanufacturing is the one that contribute the most in overall total network cost by 32.10% followed by acquisition cost (21.17%), transportation cost (13.23%), RPC cost (13.11%), disposal cost (11.69%) and other costs involved (8.70%). Therefore, suppresing new module cost and acquisition cost as low as possible might significantly benefit the system to be more cost effective. Furthermore, through sensitivity analysis, the total network cost is proven to be sensitive to variation in return quantity and recyclability percentage of returned products, and thus, companies might improve their total network cost by developing products with higher percentage of recyclable content. However, to reduce the complexities involved, it is assumed that there is only single reverse distribution channel in the network (directly from customer to retailers).