• Arthur E. Bastanov, Ufa University of Science and Technology (Ufa, Russia)

The article presents the methodological outcome of the first stage of a research program aimed at improving inventory and working capital management for sellers operating on electronic marketplaces. The relevance of the study stems from the fact that practical tasks of allocating limited working capital across numerous product items are solved under conditions of stochastic demand, pronounced seasonality, heterogeneous volatility and constrained computational resources, whereas traditional inventory management methods either remain static or require a priori specification of demand and cost parameters. The paper proposes a comprehensive methodology for demand analysis and working capital allocation that comprises three related components: intelligent demand analysis based on seasonal-trend decomposition and machine learning models for time series (DLinear, NLinear, XGBoost); extended analysis of demand volatility using two variants of the coefficient of variation and adaptive clustering of the assortment matrix; and a working capital allocation algorithm built on an integral «profitability index» and supplemented by seasonality- and risk-aware adjustments. A key element of the methodology is the separation of predictable seasonal variation from residual volatility, which enables more accurate assessment of the risk associated with individual product items and the use of these risk estimates in working capital allocation decisions. The methodology yields a structured feature vector that includes forecast, statistical and economic characteristics of product items and is suitable both for one-step decision-support systems and as a state-space representation for subsequent stochastic and dynamic inventory control models. The scientific contribution of the work lies in integrating known tools for forecasting, volatility assessment and working capital allocation into a single mathematically consistent methodological model that accounts for the specifics of electronic marketplaces and the particular constraints faced by sellers. The article substantiates the proposed methodology, describes its architecture and presents the design of computational experiments intended for subsequent empirical evaluation of its effectiveness.

marketplace, seller, demand forecasting, seasonality, coefficient of variation, profitability index, inventory management, working capital management, machine learning methods

2026-09-03