Facility Data Model Runtime Updates for Asset KPI Maintenance
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Solution Overview
Problem
Existing facility management systems face challenges in efficiently updating and integrating Key Performance Indicators (KPIs) and managing asset data in real-time due to complex processes, which can lead to downtime and increased costs, especially in organizations with multiple facilities at different locations.
Innovation Solution
An Asset Management System (AMS) that allows facility managers to make custom requests for modifying or adding asset data and KPIs in real-time, generating query lists, and executing data models dynamically for optimized facility maintenance operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data models are updated to include new assets or modify KPIs, then the system becomes more adaptable to changing facility needs, but the complexity of the update process increases
Solution Approach 1:
The system enables dynamic modification of data models at runtime, allowing facility managers to add new assets and modify KPIs without requiring system reconfiguration or downtime. The data model evolves adaptively as facility needs change, with updates being applied immediately rather than requiring static pre-planning
Solution Approach 2:
The system provides self-service capabilities through automated query list generation and validation. When a facility manager requests to add an asset or modify a KPI, the system automatically generates the necessary queries, validates the input data, and applies the changes without requiring complex manual intervention or expert system reconfiguration
2Productivity
If real-time modification of data models is enabled, then operational efficiency improves, but the risk of system errors increases
Solution Approach 1:
The system incorporates multiple feedback mechanisms including validation of custom inputs against the data model schema, automated generation of query lists to verify data integrity, and confirmation steps before applying changes. This feedback loop ensures that real-time modifications maintain system reliability by detecting and preventing errors before they propagate
Solution Approach 2:
Before applying real-time modifications, the system performs preliminary actions including validating the custom input data, generating query lists to verify data consistency, and checking for potential conflicts with existing data model constraints. These preliminary checks prevent erroneous changes from being applied, thereby maintaining system reliability while enabling operational efficiency
3Manufacturing precision
If custom input validation is performed, then data quality improves, but the time required for model modification increases
Solution Approach 1:
The system performs validation of custom inputs as a preliminary action before the actual model modification occurs. By validating data quality upfront through automated checks against the data model schema and generating query lists to verify consistency, the system ensures high data quality while keeping the overall modification time minimal through automation
Data Source
AI summary
Techniques for runtime optimizations to data models for facility maintenance operations are described. In operation, a custom request from a user to modify a data model of a facility, where the data model is indicative of information corresponding to assets monitored in the facility, and where the data model of the facility includes asset data corresponding to each asset of the facility and key performance indicators (KPIs) corresponding to each asset of the facility. A category of the custom request is then identified to generate a query list corresponding to the category of custom request for modifying asset data in the data model, where the custom request includes at least one of updating asset data corresponding to an asset of the facility and introducing new asset data in the data model. A custom input from the user is then received in response to the query list generated and modifying the data model in correspondence to the custom input. The data model is subsequently executed with modified asset data in runtime for dynamic monitoring of the facility.