From Data to Insights: Uncovering Process Inefficiencies Through Business Intelligence and Data Visualization

Outlines the change in the Bubano site operated by Wienerberger Italia where around half of Italy's total output is also produced. The first part of the evidence demonstrates how the implementation of an Enterprise Business Intelligence solution enabled a move away from a fragmented, manual reporting ecosystem to establish a governed Single Source of Truth. This shift from a reactive, spreadsheet-driven approach to a proactive diagnostic model is accomplished through the establishment of an Azure Databricks Medallion Lakehouse architecture by defining data across Bronze, Silver, and Gold layers to allow for traceable, accurate, and analytically ready information. Collecting raw sensor and production signal data falls under the Bronze category, cleaning and standardising this data resides in the Silver section, and modelling the data into a Star Schema at the Gold layer supports business metrics pertaining to production, downtime, energy, and quality. By utilizing Microsoft Fabric and Power BI for automation end-to-end workflow, two sets of operational dashboards have been developed. One for real-time tracking of production and energy consumption; two using Pareto analysis to identify the most significant contributors to inefficiency, and three to provide insight into both quality and scrap to underpin sustainability initiatives and process reliability. The ultimate aim of the above solution is to assist in the transition from retrospective reporting to proactive, insight-driven decision-making while laying the framework for scalable Digital Factory initiatives to support future Industry 4.0 efforts such as predictive maintenance, anomaly detection, and prescriptive analytics.