Semantic Supply Chain Intelligence for Natural Language Alerts
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Solution Overview
Problem
Business intelligence systems struggle to respond to natural language questions and require manual navigation, leading to delays in addressing metric thresholds, hindering business performance.
Innovation Solution
A multi-layered intelligence system integrated with natural language processing and planning and execution systems, utilizing a semantic supply chain model to provide user- or machine-actionable insights, generate natural language alerts, and offer context-dependent recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional BI systems are used to monitor metrics and send alerts, then metric monitoring capability is provided, but the system cannot directly respond to natural language questions and requires manual navigation, leading to delays in addressing metric thresholds
Solution Approach 1:
The patent introduces a natural language processing intermediary layer between the user and the supply chain data system. This NLP intermediary translates natural language questions into structured queries, eliminating the need for manual dashboard navigation and enabling direct, automated responses to user inquiries about supply chain metrics and anomalies.
Solution Approach 2:
The system implements automated anomaly detection and resolution recommendations that operate without manual intervention. When metric thresholds are breached, the system automatically detects the anomaly, analyzes root causes, and generates corrective action recommendations, eliminating the need for manual monitoring and response processes.
2Loss of information
If existing BI dashboards are used, then sales information and metrics are provided, but the dashboards are difficult to navigate and require manual searching
Solution Approach 1:
The patent replaces the mechanical interaction model of traditional dashboards (manual clicking, scrolling, and filtering) with a natural language processing system. Users can query supply chain information using conversational language, and the system automatically retrieves and presents relevant data, eliminating the need for manual dashboard navigation while improving information accessibility.
3Reliability
If manual monitoring of metric thresholds is performed, then metric alerts are sent, but delays occur in correcting metrics and business performance is hindered
Solution Approach 1:
The patent implements a closed-loop feedback system where metric anomalies are automatically detected, analyzed, and resolved. When supply chain metrics breach defined thresholds, the system immediately detects the anomaly, analyzes root causes using AI/ML models, generates corrective action recommendations, and tracks implementation of resolutions. This automated feedback loop eliminates delays in metric correction and improves overall business performance by ensuring rapid response to supply chain issues.
Data Source
AI summary
A system and method are disclosed for providing supply chain intelligence based on a semantic supply chain model. The method includes building a semantic model of a supply chain, building goals and measures to construct measure graphs to represent supply chain scenarios, storing access and computation information for the measures, relating the measures to the supply chain goals, monitoring the measures associated with the supply chain goals; tuning the measures using machine learning models by tracking outcomes and user actions associated with the measures and goals to update the machine learning models based on the tracked outcomes and user actions, monitoring for abnormal patterns of the measures, triggering, based on a detection of an abnormal pattern, an alert and a resolution, and rendering an alert or a resolution in machine form to supply chain execution systems.