Railway freight systems are increasingly required to improve performance under conditions of growing demand and limited infrastructure expansion. In this context, railway operators face a practical managerial challenge: whether to increase capacity by expanding the wagon fleet or by optimizing existing assets. This paper addresses this issue through a KPI-based business analytics framework integrated into a multi-layered digital data architecture. The study develops a rule-based Decision Support System (DSS) designed to transform raw technical parameters into strategic results. The research utilizes a digital analytical engine to process wagon specifications and real-time infrastructure data, programmatically enforcing safety filters for dynamic interaction between rolling stock and track [1]. The results demonstrate that systems prioritizing higher axle loads - measured in ton-force per axle (tf) - achieve superior asset utilization and long-term performance compared to simple fleet expansion, which often leads to marginal gains and higher costs. The findings conclude that axle load optimization (25-27 tf) supported by a digital DSS, is the most sustainable strategy for capacity growth. The practical significance lies in providing a data-driven roadmap for managerial decision-making and the digital transformation of the railway sector in Uzbekistan.