Development of Regional Finance Solution by creating Multi-Dimensional Cubes and Power BI reports. Solution will be available to user groups based on authorization principles.
Customer Profile
GEA is a global equipment provider and one of the largest suppliers of processing technology for food, beverage, pharmaceutical and a wide range of other industries. The international technology group focuses on process technology and components for sophisticated production processes in various end-user markets. Their portfolio includes machinery and plants as well as advanced process technology, components, and comprehensive services.
Customer Requirements
Regional Finance Reporting:
- Creation of Automated Reporting to leverage Central GEA BW (SAP PBW) data for Regional Meetings and provide support to Regional Finance access APR, CORE Data
Regional Business Support:
- Creation of Data Models and Visualizations using Local ERP (SAP) and BW Data
- Development of Analytics and KPI’s to drive business behavior
- Assistance to local finance teams to prepare monthly financials and create visualizations to communicate financial results
Congruent’s Solution
- Extracted data from SAP to SQL database and performed required transformations
- Developed a Business Intelligence solution by utilizing data sourced from an SQL database
- Business Intelligence solution – We provided SSAS cubes and Power BI visualizations
- SSAS cubes – Developed multidimensional cubes to analyze data and Ad hoc reports
- Power BI - Developed Visualizations and Data Models to create business solutions for commercial and operational teams
- Row level security – Provided access to the solution based on authorization principles
Benefits
- Streamlined the preparation process for regional business review meetings
- Accelerated the communication of regional performance on key indicators such as Sales, OI, GP, WC, and EBITDA
Congruent’s Key Challenge
- Performance issues resulting from creation of complex analytical measures in Power BI
- Solved the issue by implementing analytical logic at SQL level and then pass final table to Power BI
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