Assessing the economic readiness of Russian regions for robotics: identifying barriers and clustering territories on the Kohonen self-organizing maps novos

This article presents a comprehensive analysis of barriers to industrial robot adoption in Russian regions using Kohonen's self-organizing map methodology. The study identifies typological groups of regions based on their level of robotic readiness. This study presents statistical data on organizations that indicated reasons for not using industrial robots and those planning to use them across the Russian Federation's constituent entities in 2024. Each row in the original sample represents a geographic unit (regions and some associations), with one key column, "Organizations planning to use industrial robots," and several columns, "Barriers/Reasons" (the number of organizations indicating each reason). The analysis focused on cleaning the table structure, checking data quality, ranking regions for deeper analysis, and segmenting regions using a self-organizing map (SOM) and cluster compression. The article presents the main findings and supporting data (tables generated using Python code), along with brief explanations and recommendations for further action. The text alternates conclusions with specific results that support them.