<![CDATA[Food distribution efficiency is an important factor in maintaining supply chain performance, particularly in controlling transportation costs and ensuring timely delivery. Inefficiencies in distribution may lead to increased operational expenses and delays in delivering food products to their destinations. This study aims to analyze food distribution efficiency by grouping distribution activities based on transportation costs and delivery time using the K-Means clustering algorithm. The research stages consist of data collection, data preprocessing, feature selection, data standardization, determination of the optimal number of clusters, implementation of the K-Means algorithm, and cluster evaluation. Transportation cost and delivery time are used as the main variables to identify distribution patterns and classify distribution activities into efficiency groups. The resulting clusters are intended to distinguish distribution activities with relatively efficient, moderate, and less efficient characteristics, providing a basis for identifying operational patterns that require improvement. Data visualization is also used to support the interpretation of clustering results and facilitate comparisons among groups. The experimental results, based on 330 simulated food distribution records, produced three clusters. Cluster 1 consisted of 110 records, with an average distribution cost of IDR 176,363 and an average delivery time of 2.83 hours. Cluster 2 contained 111 records, with an average distribution cost of IDR 338,472 and an average delivery time of 5.83 hours. Cluster 3 comprised 109 records, with an average distribution cost of IDR 535,296 and an average delivery time of 9.49 hours. The clustering evaluation yielded a Silhouette Score of 0.679 and a Davies–Bouldin Index of 0.444. This study is expected to provide an analytical framework for supporting data-driven decisions in food distribution management. The proposed approach can assist distribution managers in evaluating cost and delivery-time patterns and identifying opportunities to improve operational efficiency. The validity of the findings depends on the quality of the operational data and the evaluation of the resulting clusters.]]>
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