METHODOLOGY OF BALANCED ASSIGNMENT OF STORAGE PLACES FOR GOODS BASED ON TIME CHARACTERISTICS OF DEMAND

Abstract

The article considers the current scientific and practical problem of storage place assignment (SLAP) in e-commerce warehouses. Classical approaches, such as ABC analysis, optimise order picking routes while ignoring the operational costs of placing goods. This leads to an imbalance, resulting in the reduction of picking distances by increasing the labor intensity of the product put-away process. The analysis of recent studies has shown that despite the proven benefits of individual time series forecasting methods, the need for comprehensive methods remains relevant. In particular, there is a lack of approaches that would integrate the analysis of demand dynamics directly into solving the SLAP problem and balance the associated costs between the processes of placing and picking orders. The Time-Oriented Assignment of Storage Locations (TOASL) methodology is proposed, the fundamental principle of which is to combine the processes of order picking and goods placement within a single optimization problem. Its architecture consists of four key phases. The first stage involves data processing, which includes aggregation, filtering, and normalisation of demand time series. The modelling stage includes clustering of time series to identify groups of SKUs with similar dynamics. The simulation phase (creation of a virtual warehouse model and testing policies) and evaluation (calculation of key performance indicators and statistical verification) complete the cycle. The study, which covers the first two stages of the methodology, used a publicly available dataset from the Kaggle platform on transactions of an online electronics store. After data filtering, which selected only purchase events for target product categories, and aggregation by session, 471 unique SKU demand time series were generated. These series were normalized using the Min-Max method to focus on the shape and dynamics of demand, rather than on absolute volumes. A comparative analysis of three clustering algorithms was conducted: Ward's Agglomerative Hierarchical Clustering (AHC), k-means, and Self-Organizing Maps (SOM). The optimal number of clusters, which is 10, was determined by the elbow method. The AHC-Ward method demonstrated the best quality of partitioning, achieving the highest silhouette score with a value of 0.39. The performed grouping allowed to identify 10 sets of positions with similar dynamics. Based on the analysis of the 5 largest clusters, logically justified warehouse zoning principles were proposed, which provide for compact placement of goods with similar demand in adjacent aisles. It is shown that clustering of demand time series is an effective basis for further adaptive warehouse zoning.