Initially, daily store-level point-of-sale (POS) and inventory data were used in a highly tactical manner, as CPG manufacturers sought to solve mainly retail execution issues. However, suppliers now want to extract and integrate inventory and POS data with other data sources to serve as the foundation for various business and analytics applications across the organization.
RSi’s automated extraction management process provides CPG suppliers with seamless data integration, incorporating high levels of precision and adhering to protocols that provide quality controlled results.
RSi knows superior data management is key to efficiently and effectively running your business on POS data. That’s why we believe that aligning hierarchies, SKUs versus UPCs, restatements and other factors to maintain data integrity, must be carefully considered and solved, and the process must then be automated.
Cleansed and harmonized POS and inventory data is securely extracted via RSi, and can be immediately integrated with your internal solutions and other BI tools through an SFTP extraction. These extractions can then be scheduled to allow for an entirely automated process of retrieving relevant data on a regular basis, powering reporting and analysis across your business in areas such as:
- »On-Shelf Availability/Out-of-Stocks
- »Promotion Planning
- »New Item Launch
- »Category Management
- »End-to-End Forecasting
Leveraging the investment in POS and inventory data in multiple applications across the enterprise delivers higher levels of precision using automation, and with very low incremental costs. Under this new operation, CPG manufacturers can derive significantly increased value from the systems being fed, with the entire organization operating from the most up-to-date data and single version of the truth.
The RSi Advantage:
* Standard and advanced measures including core, computed and calculated measures.
* Systems are always populated with the latest changes in retailer data.
* Align internal system requirements with retailer data update timeline.