SAP BPC solution provided accurate forecasting based on Week over Week Sales Trends utilizing Comparable/Non-Comparable and Identical/Non-Identical store analysis.
Location: Austin, TX
Industry: Retail Grocer
Products and Services: Natural and organic products
Solution: SAP BPC 7.5 MS
Implementation Partner: Akili
Situation and Challenges
- The desire to load, perform analysis, and report against Actuals data at a Daily level, and leverage this data to build a Sales Forecast at a Weekly level presented process and technical challenges, especially in an environment in which data volumes and subsequent system performance are an ongoing consideration. imported into the Weekly Sales Forecasting Tool.
- The various client’s regions each have a unique weekly forecasting process/model, so care had to be taken in the design of a solution that allowed flexibility while minimizing ongoing maintenance.
- Create a Weekly Sales Forecasting Tool that leverages the current BPC architecture, modeling, and processes while maintaining acceptable performance.
- Allow for direct forecasting of revenue elements across product and geographical groupings.
- Modify existing Comparable and Identical store sales analysis process to be performed across the new Weekly Sales model.
- The resulting solution provided accurate forecasting based on Week over Week Sales Trends utilizing Comparable/Non-Comparable and Identical/Non-Identical store analysis.
- This weekly sales forecast is seamlessly summarized into a Monthly aggregated view for Finance and Planning applications.
- The solution was optimized in a way that allows for consistent system performance.
- With strong technical and functional capabilities, including deep experience in the Retail industry, Akili has the tools to implement the ideal BPC planning model with an awareness of the potential pitfalls.
- Akili also brings expertise in building highly dynamic and data-intensive models across numerous other industries, and was able to bring a richer background to the table in order to implement a model that addressed the client’s potential performance concerns.