Adaptive Software Configuration for ERP Task Failure Resolution
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Solution Overview
Problem
Conventional methods for resolving failed software tasks in ERP and APS systems are time-consuming and resource-intensive, especially when there are multiple potential causes and configurations, leading to performance degradation in time-critical tasks.
Innovation Solution
A system and method for adaptive software configuration based on current and historical data, where configurations are ranked by weights reflecting past success rates, allowing for efficient selection and implementation of the most likely solution to resolve task failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional diagnosis and configuration search methods are used to resolve failed tasks, then the root cause can be identified and configurations can be found, but the process takes a lot of time and severely affects the performance of time-critical tasks
Solution Approach 1:
The system performs preliminary actions by proactively monitoring task execution and detecting failures early, before they propagate through the system. The adaptive configuration system pre-processes failure data and maintains a repository of known configurations, enabling rapid response when failures occur without requiring lengthy post-failure diagnosis
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring task execution status and using historical failure data to improve future resolution. The adaptive configuration system learns from past failures and successes, adjusting its configuration recommendations based on feedback from previous resolution attempts, thereby reducing resolution time while maintaining accuracy
2Reliability
If a large number of potential configurations are considered to resolve task failures, then the likelihood of finding a correct solution increases, but the time and resources required to search through all configurations increase significantly
Solution Approach 1:
The system changes parameters by dynamically adjusting configuration parameters based on the specific failure context and historical data. Instead of exhaustively searching through all possible configurations, the adaptive system modifies key parameters according to learned patterns from similar failures, significantly reducing the search space while maintaining high solution accuracy
Solution Approach 2:
The system performs self-service by automatically selecting and applying appropriate configurations based on monitored failure patterns, without requiring manual intervention or exhaustive searching. The adaptive configuration system autonomously learns from historical data and makes intelligent decisions about which configurations to apply, reducing both time and computational resources required
3Productivity
If limited time and resources are provided to resolve task failures, then the system can focus on a smaller set of potential resolutions, but this typically requires lengthy diagnosis to identify the correct subset
Solution Approach 1:
The system applies segmentation by dividing the complex failure resolution process into distinct modules: failure detection, historical data retrieval, configuration selection, and application. This modular approach allows the system to handle limited resources efficiently by processing only relevant segments based on the specific failure type, reducing both time and diagnostic complexity
Data Source
AI summary
This disclosure relates to systems and methods for adaptive configuration of software based on current and historical data. In one embodiment, a method is disclosed, which comprises receiving first data that reflects a first status of an execution of a software task. The method further comprises determining, based on the first data, a first set of configurations to be provided for the execution of the software task, wherein each configuration of the first set of configurations is associated with a weight that reflects a statistic measurement of a prior status of an execution of the software task when the configuration is provided, and wherein the first set of configurations are ranked based on the weights. The method also comprises providing, based on the ranking, at least one of the first set of configurations for the execution of the software task.