Optimization of Customer Clustering for Goods Transportation: A Case Study of a Spice and Condiment Manufacturer
DOI:
https://doi.org/10.53848/jlsco.v12i2.301529Keywords:
Customer clustering, K-Means clustering, Saving Algorithm, Vehicle Routing Problem, Logistics costAbstract
This research aims to optimize customer clustering and route planning in the Customer Service and Logistics Department of a case-study spice and condiment manufacturer. The company currently outsources transportation services using a one-vehicle-per-customer delivery model, resulting in unnecessarily high transportation distances and costs. The study utilized domestic delivery data from 13 customers during January–March 2024. A Fishbone Diagram was used to analyze root causes, followed by K-Means Clustering to group geographically proximate customers, and the Clarke–Wright Saving Algorithm to determine optimal routes within each cluster. The results showed that customers could be grouped into 3 clusters, reducing delivery routes from 13 to 4. Total transportation distance decreased from 1,696.9 km to 662.60 km (a 60.96% reduction), and transportation costs decreased from THB 55,730.71 to THB 19,550.89 (a 64.92% reduction). These findings demonstrate that the combination of K-Means Clustering and the Saving Algorithm is an effective approach for reducing logistics costs and can be widely applied in food and logistics industries.
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