G.K. Strugach1

1 Financial University under Russian Government (Moscow, Russia)
1 gkstrugach@fa.ru

In the context of distributed logistics on the Ozon marketplace, the sales activity of auto parts sellers often takes place under conditions of informational sparsity and uncertainty, without a holistic understanding of the relationships between demand, logistics costs, and profitability across geographical delivery clusters, which leads to an unstructured sales process and intuitive inventory allocation decisions.

Objective – the objective of this study is to develop a prototype of an analytical model using system analysis methods in order to transform an unstructured sales process of auto parts into a systematized supply optimization model, in which decisions on inventory allocation across delivery clusters are justified by a combination of data on demand, cost price, and logistics expenses.

The paper develops a prototype analytical model for supply optimization that, through system analysis of heterogeneous data sources, transforms a chaotic sales process into an ordered decision-making system where each inventory allocation decision is linked to forecasted economic outcomes. The model uses order history for March–August 2025, cost data from the “MoySklad” system, and cross-docking tariffs from the central warehouse, which makes it possible to jointly analyze demand, cost structure, and profitability indicators. A procedure is proposed for selecting the top 20 best-selling items, calculating operating profit by delivery clusters, and analyzing the profitability of individual SKUs by clusters, complemented by a profit-per-volumetric-unit indicator for item–cluster combinations.

The proposed model can be used by auto parts sellers on marketplaces to move from intuitive sales management to systematic, data-driven inventory allocation, to identify priority item–cluster combinations, to increase the efficiency of using limited logistics capacity, and to justify changes in supply strategies based on quantitative profitability indicators.

Strugach G.K. Prototype of an analytical model for supply optimization based on sales, product data and cross-docking with profit per volumetric unit. Nonlinear World. 2026. V. 24. № 2. P. 50–57. DOI: https:// doi.org/10.18127/ j20700970-202602-06 (In Russian)

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