This thesis develops an analytical framework to monitor, analyze, and improve the Weight Per Shipment (WPS) KPI within a global supply chain context. The objective is to transform a previously manual and fragmented calculation process into a structured, scalable, and automated analytical system capable of supporting decision-making across descriptive, diagnostic, predictive, and prescriptive levels. The solution integrates data from multiple sources, including SAP transactional data, container transportation systems, demand forecasts, and regulatory weight limits, within the company’s existing cloud data platform. Using Power BI, a semantic model was developed to ensure consistent KPI definitions and interactive reporting. The framework supports different levels of analysis: descriptive dashboards to monitor performance, diagnostic tools to understand deviations, predictive estimations based on demand forecasts, and prescriptive insights to identify container loading optimization opportunities.