Diagnostics for Trusted vs Historical Resource Value Differences
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Solution Overview
Problem
Current Enterprise Resource Planning (ERP) software applications face challenges in providing timely and efficient diagnostics for differences between trusted and historical resource values, especially when multiple digital rules are applied to the same dataset across various domains, leading to inefficiencies and inaccuracies in resource management.
Innovation Solution
A system that compares trusted resource values with historical resource values, detects differences, and outputs diagnostic comments to explain these differences, thereby improving the accuracy and efficiency of resource management processes by reducing processing, storage, and data transmission resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple digital rules are applied to the same dataset across various domains, then the comprehensiveness of resource management is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the resource management process into distinct phases: historical value computation, trusted value computation, difference detection, and diagnostic comment generation. Each phase handles specific rules and data subsets independently, allowing parallel processing and reducing overall processing time while maintaining comprehensive rule application.
Solution Approach 2:
The system performs preliminary computation of historical resource values using historical rules before the actual resource management decision is needed. This advance computation stores results that can be quickly compared against trusted values later, eliminating the need to reapply multiple rules during time-critical operations.
2Measurement precision
If comprehensive diagnostics are provided for differences between trusted and historical resource values, then the accuracy of resource management is improved, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary diagnostic engine that acts as a mediator between the complex rule application system and the user. This engine automatically generates human-readable diagnostic comments that explain differences between trusted and historical values, simplifying the user interface while maintaining comprehensive diagnostic capabilities through automated analysis of rule applications, data transformations, and computation steps.
3Reliability
If historical resource values are computed using multiple digital rules, then the completeness of resource valuation is improved, but the processing resources required increase
Solution Approach 1:
The system creates copies of historical datasets and applies different rule sets to these copies rather than repeatedly processing the original data. This allows comprehensive valuation using multiple rules while reducing processing resource consumption by avoiding redundant data loading and transformation operations on the same source data.
Data Source
AI summary
In embodiments, diagnostics are electronically determined and provided as to why historical resource values differ from resource values determined according to more-trusted ways. Historical relationship instance data regarding a plurality of historical relationship instances between a primary entity and a plurality of secondary entities are received along with a plurality of historical resource values, in which each historical relationship instance of the plurality of historical relationship instances is associated with a respective historical resource value of the plurality of historical resource values. The system produces a respective trusted resource values based on the respective historical relationship instance. Based on differences between the respective trusted resource value and the respective historical resource value associated with the dataset, the system outputs one or more diagnostic comments associated with the detected difference.