Management of the budgeting process
This page provides the user with tools to design, execute, and maintain the entire budgeting cycle, from setting objectives to data collection, through approval and continuous monitoring.
Definition of objectives¶
In a planning process, data collection can occur according to two approaches:
- Top-down: objectives set by management and cascaded to operational units.
- Bottom-up: objectives proposed by operational units and then aggregated.
A hybrid approach (top-down and bottom-up) can also be applied to balance strategy and operational realism.
High-level Process¶
Simulation¶
In the context of the planning process, it is essential to have features that allow both the analysis of alternative scenarios and the structured management of different data versions.
For this purpose, the following features are used:
- What-if: simulations to assess the impact of alternative scenarios on budget and forecast
- Versioning: management of multiple data versions (budget, forecast, scenarios) for comparison and traceability
Phases¶
The planning process typically includes the following phases.
| Negotiation | Negotiation and adjustments ensure alignment of operational unit data with strategic objectives and budget quality. |
| Approval | Final phase of the planning process in which management reviews and validates the planned data, confirming consistency with the corporate strategy and authorizing the use of approved versions. |
| Communication | The communication of planning data is the process by which planned information — such as budgets, forecasts, and scenarios — is distributed and shared among the various corporate levels and the functions involved. |
| Control and monitoring | In this phase, planned data is verified, variances against objectives are analyzed, and corrective actions are initiated, ensuring transparency and traceability of information. |
Features by process phase¶
This section lists the functionalities used in the budgeting process, divided by process phase.
Data Collection and Processing¶
Data collection and processing can be carried out through the following methods:
| Method | Description |
|---|---|
| Data Entry, see Data Entry Methods | manual data entry by users in budget forms. |
| Driver Based, see Allocations and closing - Overview | automatic calculation based on drivers (e.g., volumes × prices, FTE × average cost). |
| MDX Calculations, see Multi-Dimensional Script Account Calculation Logic | formulas written in MDX language for calculations on dimensions/measures. |
| Forecasting Algorithms, see Forecast Models | integrated statistical methods for projections (trend, regression, seasonality). |
| ML Algorithms, see Advanced Analytics | machine learning models for advanced forecasts based on historical data and complex patterns. |
Setup and Maintenance¶
The parameterization of the budgeting process is based on various activities listed below.
Data modeling
Below are the types of modeling
| Activity | Description |
|---|---|
| Data model design, see Multidimensional Data model | Activities for initial definition and configuration of accounts, dimensions, and hierarchies aimed at ensuring a consistent data structure, correct operation of CPM processes, and controlled access to information. |
| Process Data Model (PDM), see Introduction to the Process Data Model | Defines which accounts and categories can be modified (editable) in each phase of a workflow process (e.g., budgeting, forecasting, reporting). The administrator configures the PDM by assigning specific combinations of accounts and categories to the different steps of the operational flow, also differentiating them by company entity. |
Advanced data entry features¶
Below are some of the main features that allow guided and traceable data entry and calculation:
| Feature | Description |
|---|---|
| Spreading , see Method of spreading in data entry) Break Back, see The Break Back function | distribution of a total across periods or centers reverse calculation from aggregated values |
| Monthly/custom splitting, see Custom Period Splitting | division of values according to predefined rules |
Formulas and Calculations
Below are the tools and methods for processing planning and reporting data.
| Field | Description |
|---|---|
| MDX, see Multi-Dimensional Script Account Calculation Logic | multidimensional formulas for complex calculations on data cubes |
| Data Transfer Process (DTP), see Data Transformation Package (DTP) | automatic transfer and population of data between entities or models |
| Forecasting Algorithms, see Forecast Models | forecasts based on statistical and historical methods |
| ML Algorithms,see Advanced Analytics | machine learning models for advanced predictive analysis and complex patterns |
Versioning
Below are the features for saving, replicating, and protecting system data.
| Field | Description |
|---|---|
| Snapshot, for details see Snapshot | capturing the state of data at a given point in time for reference or comparison |
| Backup, see Archiving process data | data protection to ensure recovery in case of errors or losses |