Watchlist Engine for ERP Data Monitoring
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Solution Overview
Problem
Users of Enterprise Resource Planning (ERP) Software face inefficiencies in identifying and monitoring data items that require user actions, as conventional methods are time-consuming and prone to overlooking necessary actions.
Innovation Solution
Implementing a watchlist mechanism that proactively identifies and alerts users to data items requiring action through watchlist items and a watchlist engine, which defines parameters and criteria for monitoring and notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually check database records to identify data items requiring user actions, then users can identify specific data items, but the process becomes time-consuming and risk of overlooking items increases
Solution Approach 1:
The system performs self-monitoring by automatically evaluating data items against watchlist criteria without requiring user intervention. The watchlist engine continuously checks database records and identifies items meeting specified criteria, eliminating the need for manual user checking while ensuring comprehensive coverage.
Solution Approach 2:
Users pre-configure watchlist item definitions with specific criteria and parameters before monitoring begins. This preliminary setup enables the system to automatically identify relevant data items without requiring users to manually check each record, as the monitoring framework is already prepared to detect matching items.
2Reliability
If multiple sets of data items are monitored manually, then comprehensive coverage is achieved, but the time required increases significantly
Solution Approach 1:
Multiple watchlist item definitions are merged into a unified monitoring framework. The watchlist engine consolidates monitoring of multiple data item sets by evaluating them against their respective criteria simultaneously, allowing comprehensive coverage of multiple datasets without requiring separate manual checking processes for each.
Solution Approach 2:
The watchlist monitoring framework is designed to be universal, capable of monitoring multiple types of data items with different criteria through a single system. The engine can evaluate diverse data sets against various watchlist definitions, providing multi-functional monitoring capability that improves productivity while maintaining comprehensive coverage.
3Measurement precision
If users actively identify and determine user actions for each data item, then accurate action determination is achieved, but the process becomes complex and time-consuming
Solution Approach 1:
The system provides automated feedback by evaluating data items against watchlist criteria and generating notifications when items meet specified conditions. This feedback mechanism accurately determines when user actions are required without requiring users to manually analyze each data item, maintaining precision while reducing process complexity.
Solution Approach 2:
The watchlist engine acts as an intermediary between the database and the user. It automatically evaluates data items against monitoring criteria and translates complex evaluation logic into simple notifications, maintaining accurate action determination while shielding users from the underlying complexity of the monitoring process.
Data Source
AI summary
Embodiments of the present invention provide a mechanism for monitoring data using a watchlist item and a watchlist item definition that includes a set of parameters for identifying a set of data items for user action and criteria for recommending or requiring user action for the set of data items to be identified.