Facility Recommendation Engine Using Knowledge Graph Service Cases
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Solution Overview
Problem
Current facility management systems rely on worker domain knowledge and pre-defined instructions, leading to unoptimized asset management and inefficient utilization of resources, as well as challenges in predicting maintenance needs and providing real-time, intelligent recommendations for asset management.
Innovation Solution
A facility management system utilizing an IoT platform with real-time models and visual analytics to generate actionable recommendations for sustained peak performance, including the use of knowledge graphs to update and derive recommendations based on heuristic knowledge and tagged information for resolving service cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If worker domain knowledge and pre-defined instructions are used to manage assets, then asset management can be performed with simple systems, but the management becomes unoptimized and resource utilization is inefficient
Solution Approach 1:
The system implements feedback loops where asset performance data is continuously collected, analyzed, and used to generate actionable recommendations. The feedback mechanism compares actual asset performance against optimal parameters and automatically adjusts management decisions, transforming static pre-defined instructions into dynamic, data-driven recommendations that improve asset management efficiency without requiring complex manual intervention
Solution Approach 2:
The system enables assets to self-monitor and self-report their status through embedded sensors and IoT devices. Assets automatically generate service cases when anomalies are detected, eliminating the need for continuous human monitoring and allowing the system to self-optimize based on real-time data, thereby improving efficiency while maintaining simple operational interfaces
2Extent of automation
If worker domain knowledge is relied upon for asset management, then human expertise can be utilized, but the system cannot provide real-time intelligent recommendations and maintenance prediction becomes challenging
Solution Approach 1:
The system introduces an AI/ML-based recommendation engine as an intermediary between raw asset data and human decision-makers. This intermediary processes vast amounts of sensor data, applies domain knowledge through trained models, and generates actionable recommendations, thereby enabling real-time intelligent insights without losing the value of domain expertise which is encoded in the system's algorithms and knowledge graphs
Solution Approach 2:
The system transforms qualitative domain knowledge into quantitative parameters that can be processed by computational models. Domain expertise is converted into adjustable parameters, thresholds, and weights within the recommendation engine, allowing the system to dynamically adapt recommendations based on changing asset conditions while preserving the essence of human expertise in a machine-executable format
3Adaptability or versatility
If pre-defined instructions are used for asset management, then implementation is straightforward, but the system cannot adapt to changing conditions and predict maintenance needs
Solution Approach 1:
The system replaces static pre-defined instructions with dynamic recommendation models that continuously adapt to changing asset conditions. The recommendation engine adjusts its outputs in real-time based on incoming sensor data, asset history, and environmental factors, enabling the system to respond flexibly to new situations without requiring complex manual reconfiguration or rule updates
Data Source
AI summary
Various embodiments described herein relate to systems and methods for providing actionable recommendations in a facility. In this regard, a first service case is generated in response to identification of a first event associated with a first asset in a facility. The first service case comprises at least one of: a first recommendation to resolve the first event and a first root cause to diagnose the first event. Further, the first service case is based on heuristic recommendations and heuristic knowledge for resolution of a plurality of service cases associated with at least one asset in the facility. A first input indicative of a resolution of the first service case is received. The first input has tagged information indicative of a modification to at least one of: the first recommendation and the first root cause. In response to receipt of the first input, a knowledge graph is updated based on the tagged information. The knowledge graph corresponds to a data model constructed for resolution of the plurality of service cases associated with the at least one asset in the facility. An occurrence of a second event associated with a second asset in the facility is identified. In this regard, the second event is related to the first event. A second service case is generated in response to identification of the second event. The second service case is generated based on the modification to at least one of: the first recommendation and the first root cause derived from the knowledge graph.