Resource Allocation Discrepancy Detection Using Template-Based Models
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Solution Overview
Problem
Large enterprises face difficulties in developing and comparing resource allocation models due to complexity, making it hard to identify errors or inefficiencies across different enterprises, and to compare efficiencies or allocate resources effectively.
Innovation Solution
A system that uses graph-based data models with nodes representing resources and edges showing allocation, along with discrepancy models and an analysis engine to identify discrepancies by comparing resource allocation values with benchmark models, and allowing for user feedback to modify rules and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sophisticated computer programs and computers are used to assist in generating resource models for larger enterprises, then the ability to analyze resource allocations is improved, but the device complexity and difficulty of model development increase
Solution Approach 1:
The patent segments the complex resource allocation model into standardized template components (resource templates, allocation templates, project templates) that can be independently configured and combined. This segmentation allows large enterprises to build sophisticated analysis capabilities without managing monolithic complex models, as each template serves a specific function and can be reused across multiple contexts.
Solution Approach 2:
The system enables parameter changes by allowing organizations to customize template parameters (such as resource types, allocation rules, cost rates) without changing the underlying model structure. This lets enterprises adapt standardized templates to their specific needs while maintaining the simplicity of template-based modeling rather than developing custom complex models.
2Adaptability or versatility
If the number of tracked activities and elements increases to cover more enterprise operations, then the comprehensiveness of resource modeling is improved, but the complexity of the underlying data models increases
Solution Approach 1:
The patent implements universality through standardized templates that can serve multiple functions across different enterprise operations. A single resource template can be used for various resource types (personnel, equipment, materials), and allocation templates can handle different allocation scenarios (project-based, department-based, cost-center-based). This multi-functionality allows comprehensive coverage of enterprise operations without creating separate complex data models for each scenario.
Solution Approach 2:
The system employs nesting by organizing resource models in hierarchical layers where templates contain parameters, which contain values, and templates can be nested within other templates. This nested structure allows comprehensive modeling of complex enterprise operations while maintaining simplicity at each layer, as each level handles only its specific aspect rather than the entire complexity.
3Loss of information
If complex models with many items and entities are used for enterprise scale resource modeling, then the detail and scope of resource tracking is improved, but the difficulty of development and error identification increases
Solution Approach 1:
The patent uses copying by creating standardized template instances that can be replicated across multiple contexts. Instead of manually defining each resource and allocation relationship in detail, the system copies proven template configurations and adapts them to specific needs. This reduces development difficulty and errors, as templates are pre-validated and can be consistently reused, while still capturing detailed resource tracking information through template parameters and instances.
Data Source
AI summary
Embodiments are directed to identifying allocation discrepancies. Data models and Benchmark models may be provided to an analysis engine. Discrepancy models may be provided to the analysis engine, such that each discrepancy model may be arranged to include one or more rules. The analysis engine may be employed to search for discrepancies in the data models based on the discrepancy models and the benchmark models. If discrepancies may be identified by the analysis engine, one or more notifications may be provided to one or more users. Also, the discrepancy models may be modified based on subsequent feedback provided by the one or more users. Correlations in the resource allocation values may be identified based on machine learning that includes one or more of linear regression, deep learning neural networks, or the like. And, additional discrepancy models may be provided based on the identified correlations.