Strategic Product Placement in Warehouses Using Turnover-Based Clustering - Arif, Fahmi; Agustian, Galih; Ramadhan, Fadillah; Rizkiani, Firda Nur; Sudarawerti, Galuh (International Journal of Industrial Engineering: Theory, Applications and Practice, 2026)
This study aims to optimize product locations within a warehouse using turnover rates as an indicator of sales performance. By positioning items based on sales frequency, the approach enhances warehouse layout efficiency and accessibility. Principal Component Analysis (PCA) and k-means clustering were employed to organize products based on their sales patterns. Twenty clusters were formed and used to inform strategic product placement. The results demonstrate the effectiveness of this clustering-based method in improving warehouse operations by reducing expected travel distance as a proxy for order-picking times and enhancing operational efficiency. The proposed approach is data-intensive, which may limit its applicability to organizations without sufficient sales data. This study introduces the application of PCA and k-means clustering in optimizing storage location assignments. It highlights the value of using data-driven techniques to solve real-world warehouse management challenges. Future studies could investigate incorporating factors such as demand fluctuations to enhance the model's flexibility.