Clustering Analysis Approach to Enhance Inventory Management and Product Recommendations in the Ornamental Plant Industry

The ornamental plant industry has grown significantly as consumers seek to enhance living spaces with diverse plant species. This study aims to optimize inventory management and marketing strategies by applying K-Means clustering to categorize plants based on price, pot size, light requirements, care levels, and popularity. The method used is K-Means clustering, which groups plants into three clusters based on key characteristics. By analyzing these attributes, K-Means clustering identifies patterns and similarities among different plant species, allowing businesses to understand consumer preferences and inventory management better. The results identified three main clusters: Cluster 1 (moderate care, light, popularity) plants like Aglaonema require balanced stock and targeted promotion for medium-light environments. Cluster 2 (low care, light, popularity) plants such as Aglaonema Chiangmai need high stock levels and budget-friendly marketing. Cluster 3 (high care, light, popularity) plants like Alocasia demand elevated stock, premium quality, and care-focused promotion.