Variable Resolution Resource Allocation Data Model
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Solution Overview
Problem
The complexity of financial allocation models in large enterprises makes it difficult to accurately determine the total cost of ownership for products and services, and generates challenges in generating reporting information, especially with the increasing number of tracked activities and elements.
Innovation Solution
A data model with continuously variable resolution of resource allocation is implemented, allowing for the generation of grouped objects and assignment ratio tables based on key features, which simplifies the allocation of resources and reduces computational complexity by collapsing line items into grouped objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of tracked activities and elements increases to improve financial modeling accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the financial allocation model into hierarchical levels (enterprise level, business unit level, department level, etc.) and by categorizing resources into distinct types (human resources, capital resources, material resources). This segmentation allows the system to handle complexity through structured breakdown while maintaining accurate tracking of numerous activities and elements at appropriate granularities.
Solution Approach 2:
The patent introduces multiple dimensions for organizing financial data including time dimensions (budget periods, actuals periods), organizational dimensions (hierarchy levels), and resource type dimensions. This multi-dimensional approach allows the system to manage complex financial models by adding structural layers rather than increasing linear complexity, enabling efficient querying and reporting across different granularities.
2Measurement precision
If the number of tracked activities and elements increases to improve financial modeling accuracy, then measurement precision improves, but loss of time increases
Solution Approach 1:
The patent implements preliminary action through pre-defined allocation rules, templates, and hierarchical structures that are established before financial data collection begins. These pre-configured elements enable the system to quickly process and allocate costs without requiring complex real-time calculations, significantly reducing reporting generation time while maintaining accuracy.
Solution Approach 2:
The patent applies dynamics by enabling flexible adjustment of model granularity and allocation rules based on reporting needs. The system can dynamically switch between high-level summary views and detailed line-item levels, allowing users to obtain both strategic overview and operational detail efficiently without being constrained by a fixed model structure.
3Measurement precision
If the number of tracked activities and elements increases to improve financial modeling accuracy, then measurement precision improves, but productivity decreases
Solution Approach 1:
The patent uses copying by creating template-based structures that can be replicated across different organizational units and time periods. Once allocation rules and hierarchical structures are defined at the enterprise level, they can be copied and adapted to business units and departments, dramatically reducing the time and effort required to build comprehensive financial models while maintaining consistency and accuracy.
Solution Approach 2:
The patent implements universality through a unified financial allocation model that handles multiple resource types (human, capital, material) and multiple organizational levels within a single integrated system. This universal framework eliminates the need for separate modeling approaches for different resource categories, improving productivity by providing consistent processing across diverse financial data while maintaining measurement precision.
Data Source
AI summary
Embodiments are directed towards allocating resources in a business system. A data model that includes a plurality data objects may be generated, such that each data object includes a plurality of data object line items. Allocation rules that allocate resources between the data objects may be generated. The allocation rules may be employed to identify key features of data objects, such that the key features are used by the allocation rules to allocate resources.If key features are identified, grouped objects that separately correspond to the data objects that include key features may be generated. Also, grouped object line items may be generated for each of the grouped objects based on each distinct value of the key features, such that the data object line items are collapsed into grouped object line items based on the distinct values of the key features.