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The article considers two problems: differentiation of retail trade turnover in the regions of the Russian Federation and building a supply chain to realize the potential. The first section is devoted to determining the retail potential of Russian regions. Based on the correlation analysis, the key structural factors influencing the retail trade turnover per capita have been identified. At the next stage, two factors were selected – the number of consumer kits per capita and the proportion of the urban population – as signs of regional clustering. Clustering is performed by the k-means algorithm using the Python scikit-learn library. The resulting clusters were then used for "smart" benchmarking of the subjects of the Russian Federation, which made it possible to identify the retail potential. The second section describes the process of building a network of distribution centers to meet the previously identified potential. The author's methodology for converting monetary units into tonnage was applied based on data from open sources. The simulation was carried out using special HORIZON software, which takes into account the real road network and regional tariffs. The calculation of the main parameters of the digital model was also carried out: storage density, cost of equipment, construction costs of 1 square meter, fixed costs (management costs, WMS, security) and inventory turnover. As a result of the study, the retail potential of the regions in the amount of 2.3 trillion rubles was revealed, 68 priority cities and 43 optimal warehouse locations were identified. The results serve as a methodological strategic planning tool for retailers and other organizations operating nationwide.
supply chain modeling, retail, clustering, correlation analysis, statistics, logistics, regional differentiation, regional analysis, cost optimization, supply chain management
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