This bachelor’s thesis addresses production optimization with a focus on workplace inefficiencies and bottlenecks in the manufacturing process. The aim is to establish a data-driven analytical approach to support decision-making. Data is collected from production systems and analyzed to understand the database structure. SQL queries are then used to prepare and transform the data into views optimized for the Power BI environment. In Power BI, reports and analyses are developed to provide insights into production efficiency, downtime, and key performance indicators (KPIs). Based on the results, main inefficiencies are identified and measures for improving production processes are proposed. The findings highlight the need for production standards, improved production planning, and optimization of low-efficiency workstations.
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