{"title":"Objective and Subjective Integration in Distribution Center Location Selection: A Case Study of Battery- electric Motorcycle Sales","authors":"Sofia Fitri Ramadani, G. W. Bhawika, I. Baihaqi","doi":"10.2991/AEBMR.K.210510.045","DOIUrl":null,"url":null,"abstract":"Determining the distribution center location is a strategic decision for the company to maximize sales and minimize cost. Location decisions are long-term investment decisions. When the demand for products on the market increases but the distribution unbalanced, it can cause high transportation costs. Therefore, it requires the company to make adjustments, one of them by opening the location facility. Product characteristics in the market determine the location selection. The decision can not resolve by one criterion but many criteria, including subjective and objective measures. By using a case study of the distribution center of a battery-electric motorcycle, this paper offers a model for determining distribution location by integrating objective and subjective factors using the Brown-Gibson model. The objective factor weight is processed using a transportation model to obtain an alternative site with the lowest transportation cost. Furthermore, the number of unit allocation from the factory to the market achieved. The subjective factor weight processed using the Analytical Hierarchy Process (AHP) through identifications, verification of criteria, and questionnaires. Besides, the sensitivity analysis carried out to determine how much to influence the two factors to have on determining the distribution center location selection. The model results show that compared to the initial location, the proposed model decreased by 29.4%. The results of this study could be very beneficial for electric vehicle companies to determine the strategy and location decision criteria in the future to enhance effectiveness and efficiency in distribution performance.","PeriodicalId":287694,"journal":{"name":"Proceedings of the 2nd International Conference on Business and Management of Technology (ICONBMT 2020)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2nd International Conference on Business and Management of Technology (ICONBMT 2020)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2991/AEBMR.K.210510.045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Determining the distribution center location is a strategic decision for the company to maximize sales and minimize cost. Location decisions are long-term investment decisions. When the demand for products on the market increases but the distribution unbalanced, it can cause high transportation costs. Therefore, it requires the company to make adjustments, one of them by opening the location facility. Product characteristics in the market determine the location selection. The decision can not resolve by one criterion but many criteria, including subjective and objective measures. By using a case study of the distribution center of a battery-electric motorcycle, this paper offers a model for determining distribution location by integrating objective and subjective factors using the Brown-Gibson model. The objective factor weight is processed using a transportation model to obtain an alternative site with the lowest transportation cost. Furthermore, the number of unit allocation from the factory to the market achieved. The subjective factor weight processed using the Analytical Hierarchy Process (AHP) through identifications, verification of criteria, and questionnaires. Besides, the sensitivity analysis carried out to determine how much to influence the two factors to have on determining the distribution center location selection. The model results show that compared to the initial location, the proposed model decreased by 29.4%. The results of this study could be very beneficial for electric vehicle companies to determine the strategy and location decision criteria in the future to enhance effectiveness and efficiency in distribution performance.