Issue |
E3S Web Conf.
Volume 73, 2018
The 3rd International Conference on Energy, Environmental and Information System (ICENIS 2018)
|
|
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Article Number | 13022 | |
Number of page(s) | 5 | |
Section | System Information and Decision Support System | |
DOI | https://doi.org/10.1051/e3sconf/20187313022 | |
Published online | 21 December 2018 |
Using Sustainable Balanced Scorecard and Graph Theoretic Approach to Make Decision in Reverse Logistic
Industrial Engineering Department, Faculty of Engineering, Diponegoro University, Semarang - Indonesia
* Corresponding author: ariessuanty@gmail.com; aries.susanty@ft.unidp.ac.id
The aim of this study is to identify the decision should be made by the company to manage their reverse logistics activity. In this study, the company should decide whether the reverse logistic activity must be outsourced part of reverse logistics activity or all must be outsourced or nothing must be outsourced. The object of this study is PT. XYZ - a foreign-owned electronics company and there is four scenario for reverse logistic activity proposed by the company. This study uses a graph-theoretic approach as the method to consider interdependencies and maintaining the hierarchical relationship among attributes and sub-attributes which is important to determine the best scenario of reverse logistic. The attributes and sub-attributes were selected by combining four traditional balanced scorecard perspectives with two perspectives of sustainability, i.e. environmental and social. This research used primary data collected by distributing closed questionnaires to the management of the company. The data processing with a graph-theoretic approach generates the permanent function which is known as the outsourcing index for each scenario. The outsourcing index for first until the fourth scenario is 52.71, 70.97, 89.86, and 81.27 respectively.
Key words: PT.XYZ / graph theoretic approach / reverse logistic / sustainable balanced scorecard / outsourcing index
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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