Case Study
The client is a Fortune 100 company headquartered in the United States. They are a global leader in the Computers and Office Equipment industry.
Infosys as a strategic IT partner of the client is actively involved in their SAP digital transformation journey. The client partnered with Infosys to simplify their data platform utilizing the power of advanced data warehouse.
Key Challenges
- Supply chain planning in Integrated Business Planning (SAP IBP) was done in discrete mode preventing the business from getting a single view of data for decision-making
- Multiple sources systems and the lack of a single source of truth for data for planning was causing data redundancy and high operational expenses
- SAP Business Warehouse was on an RDBMS database due to which the run time of jobs was significantly higher
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Talk To ExpertsThe Solution
The client partnered with Infosys for SAP S/4HANA Embedded Business Warehouse related projects and enhancements.
The project involved migration and consolidation of master and transactional data from legacy systems to a single SAP S/4HANA Embedded Business Warehouse (7.5 on HANA) with minimal transformation and harmonization. The dataset was then sent downstream to IBP for supply chain and demand planning.
SAP Business Warehouse 7.5 on HANA Greenfield Implementation Program
Simplified most of the existing data flow in SAP Business Warehouse by leveraging advanced layered scalable architecture principles which reduced the data footprint and redundancy
Implemented a leaner and more flexible architecture using enhanced modelling capability leading to reduced development effort
Used Core Data Services (CDS) views to extract data by integrating SAP S/4HANA and SAP BW 7.5 on HANA
Simplified the orchestration and automation of the overall process post implementation using scheduling tools (SAP CPS Redwood)
Benefits
Co-ordinated supply chain planning helped reduce revenue loss due to outliers by up to 9%
Hosted Business Warehouse 7.5 on HANA on AWS Cloud, reducing total cost of ownership by up to 12%
Enabled agile data warehouse development and better real-time insights
Provided greater business agility
Optimized data models leading to overall savings of about 15% on the data footprint
Improved average data load times by up to 22%
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