WAREHOUSE LOCATION OPTIMIZATION WITH CLUSTERING ANALYSIS TO MINIMIZE SHIPPING COST FOR INTER-ISLAND TRANSACTIONS IN E-COMMERCE

This research investigates the optimization of warehouse locations to minimize shipping costs in e-commerce for PT Startup Haluan Dunia, an Indonesian social commerce company. The study explores warehouse optimization, supply chain management, logistics, third-party logistics (3PL), and clusterin...

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Bibliographic Details
Main Author: Trianto Atmojo, Alvin
Format: Theses
Language:Indonesia
Subjects:
Online Access:https://digilib.itb.ac.id/gdl/view/75610
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Institution: Institut Teknologi Bandung
Language: Indonesia
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Summary:This research investigates the optimization of warehouse locations to minimize shipping costs in e-commerce for PT Startup Haluan Dunia, an Indonesian social commerce company. The study explores warehouse optimization, supply chain management, logistics, third-party logistics (3PL), and clustering analysis to determine the most suitable warehouse locations. The conceptual framework outlines the research design, data sources, and attributes, including the minimum regional wage, population, and geographic coordinates of cities. Clustering analysis, utilizing k-means and weighted k-means clustering, is implemented to group cities based on similarity and identify optimal warehouse locations. Using quantitative research techniques, this study addresses the challenge of high logistics shipping fees leading to economic inequality and limited product accessibility outside of Java, Indonesia. Data collection methods encompass surveys, secondary data analysis, web scraping, and retrieval, ensuring comprehensive coverage of relevant attributes. Distance and order probability are chosen as the primary features for clustering analysis. Distance is computed using the Euclidean method based on latitude and longitude coordinates, while order probability is derived from the minimum regional wage and population of each city. Simulation scenarios estimate demand and evaluate different warehouse location options. Transaction simulations calculate shipping costs based on the nearest warehouse for each scenario. The results demonstrate that clustering analysis optimizes warehouse locations, resulting in significant shipping cost reductions. Based on the research findings, the implementation plan proposes up to five optimal warehouse locations: Tapanuli Tengah, Musi Banyuasin, Katingan, Gorontalo Utara, and Wajo. These locations offer proximity to demand areas and potential cost savings. This research provides insights for PT Startup Haluan Dunia to enhance logistics efficiency, reduce shipping costs, and improve customer accessibility and affordability outside of Java Island. By optimizing warehouse locations through clustering analysis, the company can foster growth and customer satisfaction in the competitive e-commerce landscape.