Relational Blending Engine and Planning

Planning refers to the process required to plan domain specific segments of a broad financial planning trial balance. For example, planning for the fixed asset range of accounts is very different from planning for staffing changes. These Planning areas involve different groups of users, process timing, calculations, and reporting needs. 

Traditional Planning Approaches and Problems

All Planning tasks share a common challenge. Creating an analytic model to represent a planning process is very difficult without knowing how many items will be planned.  The nature of analytic planning requires building a Cube and defining its Dimensions with the appropriate Dimension members (Products, Employees, Assets, etc.). This is where the problems begin.  How many assets should be added to the Asset Dimension in the Capital Planning Cube? How many new hire employees should be added to the Employee Dimension in the Workforce Planning Cube? These questions are impossible to answer, and nobody can predict how many items someone intends to add to a plan. Consequently, it is impossible to build Dimensions in advance of the planning process which leads to intensive Cube and Dimension maintenance activities. In addition, adding each asset or employee to an analytic planning model can create Dimensions with many members which can lead to processing performance problems.

OneStream Planning with Data Blending

The OneStream Planning Engine blends the relational data and analytic model capabilities of the OneStream Platform.  This creates a completely different way to approach the unknown nature of the Planning process.  The Planning solution uses a relational table (Register) to collect the items intended for planning (e.g. People, Assets, Projects, etc.) or compliance (e.g. Contracts).  It then applies calculations to the register items, which results in an output table that consists of an accounting distribution (like an Accounts Payable or Receivable Sub-System Distribution).  The resulting distribution is then mapped and automatically loaded into the analytic model at a summary level.  By loading summary data into the analytic model, the metadata maintenance and performance burden associated with the traditional analytic-only approach is virtually eliminated.

Using the relational blending approach eliminates having to know in advance how many items a user will plan.  A user can add as many register items as he/she needs without the system administrator having to change the planning model’s metadata.  The reason for this is that a relational table is used to hold the register and derive the calculations. The analytic model is only loaded with summary data, which in turn keeps calculation performance optimal.  Even better, the relational detail register information is still accessible from the analytic model via OneStream's built in drill-down capabilities. 

The Planning Engine allows more efficient planning models to be built and rapidly deployed.  The following sections provide an overview of the People Planning implementation which is a part of the Planning Engine.  The same concepts defined for People Planning span all applications in the Planning solutions.  The only differences between the applications are the fields defined in the Register table (i.e. what is being planned) and the types of prebuilt calculations required to support the application (e.g. Double Declining Balance Depreciation, Interest Expense, etc.).