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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Main Author: Ario Prabowo
Other Authors: Shaligram Pokharel
Format: Final Year Project
Language:English
Published: 2009
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Online Access:http://hdl.handle.net/10356/18433
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-184332023-03-04T18:16:37Z Simulation model of reverse logistic in computer industry Ario Prabowo Shaligram Pokharel School of Mechanical and Aerospace Engineering DRNTU::Engineering::Industrial engineering::Engineering logistics 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). Bachelor of Engineering 2009-06-29T02:22:10Z 2009-06-29T02:22:10Z 2009 2009 Final Year Project (FYP) http://hdl.handle.net/10356/18433 en Nanyang Technological University 92 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Industrial engineering::Engineering logistics
spellingShingle DRNTU::Engineering::Industrial engineering::Engineering logistics
Ario Prabowo
Simulation model of reverse logistic in computer industry
description 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).
author2 Shaligram Pokharel
author_facet Shaligram Pokharel
Ario Prabowo
format Final Year Project
author Ario Prabowo
author_sort Ario Prabowo
title Simulation model of reverse logistic in computer industry
title_short Simulation model of reverse logistic in computer industry
title_full Simulation model of reverse logistic in computer industry
title_fullStr Simulation model of reverse logistic in computer industry
title_full_unstemmed Simulation model of reverse logistic in computer industry
title_sort simulation model of reverse logistic in computer industry
publishDate 2009
url http://hdl.handle.net/10356/18433
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