We partnered with our client to elevate their financial planning process by building a dynamic Quote Planning solution using SAP Analytics Cloud (SAC). We connected SAC with their existing SAP Configure, Price, Quote (CPQ) system, and established seamless data consumption and write-back capabilities. The solution helps their teams to plan, forecast, and manage quotes with greater precision and agility
It was a challenging task to establish a reliable two-way data flow between SAP CPQ and SAP Analytics. It required the careful handling of data models to ensure the consistency during reading and write back operations. Moreover, maintaining the real time accuracy, and timing to avoid discrepancies was also a big challenge.
The quotes in the CPQ system often involved the multi-level structures, which were highly dependent across the product lines and pricing models. So, we required to design a planning model in SAC that can accommodate the complexity without breaking the overall structure
There was a big performance challenge for the number of users and data volumes accessing the planning environment. It was highly important to build a system with the capability of handling large-scale planning scenarios without the issues of lags, data loss, and delays.
Our experts implemented custom integration logic using SAC's planning capabilities and pre-configured connectors that ensured the smooth two-way data flow into the system. Scheduled data jobs helped in pulling the data from SAP CPQ into SAC at defined intervals and ensuring that the planned data was sent back efficiently to CPQ.
Our team curated a flexible and scalable planning model in SAC that carried the capability of handling the complex, multi-level quote structures from CPQ. At the same time, the custom hierarchies and dimension mappings were curated that focused to preserve dependencies and relationships within quotes. This mechanism allowed the users to drill down into granular quote data while ensuring a clear, aggregated view for planning and forecasting.
We fostered the data partitioning strategies within SAC that distributed large data volumes into manageable bundles. Load balancing techniques were facilitated that helped to optimize processing during peak usage hours. At the same time, planning functions and scripts were streamlined to ensure a fast and responsive user experience and reduce resource consumption even with high data loads.
We achieved significant results by the end of the project
