Intelligent Bill of Materials Builder Using Knowledge Graphs
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Solution Overview
Problem
Manual generation and adaptation of Bills of Materials (BoMs) for equipment manufacturing and site construction are error-prone and unmanageable due to the complexity of tracking numerous component properties, such as prices, availability, and lead times, especially in repetitive manufacturing and construction projects with variations over time.
Innovation Solution
An automated intelligent BoM builder that utilizes a knowledge graph to identify correlations between sites and components, enabling the automatic generation of optimized BoMs that adapt to changing properties, balancing trade-offs between lead time, construction time, component reliability, and maintenance cost, and selecting components based on project objectives and site characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual generation and adaptation of Bills of Materials (BoMs) is used, then flexibility in handling project-specific requirements is maintained, but accuracy and efficiency deteriorate due to error-prone manual processes
Solution Approach 1:
The patent replaces manual mechanical processes with an intelligent automated system that uses machine learning models and knowledge graphs to generate and adapt BoMs. The system automatically queries historical project data, identifies relevant components, and generates BoMs without manual intervention, thereby improving accuracy while maintaining adaptability through algorithmic decision-making.
Solution Approach 2:
The system enables self-service by automatically generating and adapting BoMs based on historical data and project requirements without requiring manual input. The intelligent system autonomously queries the knowledge graph, selects appropriate components, and updates BoMs in real-time, freeing users from error-prone manual processes while maintaining flexibility.
2Reliability
If comprehensive tracking of component properties (prices, availability, lead times) is implemented, then procurement quality improves, but system complexity increases making management difficult
Solution Approach 1:
The patent creates a universal BoM management system that handles multiple component properties (prices, availability, lead times, specifications) through a single integrated platform. The system universally queries and processes all these properties simultaneously using knowledge graphs and machine learning, eliminating the need for separate tracking systems and reducing overall management complexity.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary layer between raw component data and BoM generation. This intermediary structure organizes and standardizes comprehensive component properties, making them easily queryable and manageable. The knowledge graph mediates between complex data sources and the BoM system, simplifying information retrieval and processing.
3Productivity
If automated BoM generation is implemented, then efficiency and accuracy improve, but adaptability to project-specific variations may deteriorate
Solution Approach 1:
The patent implements dynamic BoM generation where the system adapts its behavior based on project-specific requirements. The machine learning models dynamically adjust component selection criteria based on queried historical data, and the system continuously updates BoMs as project parameters change. This dynamic approach maintains high efficiency while ensuring adaptability to variations.
Solution Approach 2:
The system incorporates feedback mechanisms where generated BoMs are continuously refined based on project outcomes and historical data. The knowledge graph learns from past projects and feeds this information back into future BoM generation, improving both efficiency and adaptability over time. User modifications and corrections also feed back into the system to enhance future automated generation accuracy.
Data Source
AI summary
A method and system for intelligently generating Bill of Materials (BoM) and tracking components of a build site. The method and system include querying a knowledge graph for a collection of historical bill of materials (BoMs), automatically generating a digital twin of a built site based on a selected group of completed build site, and intelligently generating the BoM for the digital twin based on BoMs of the selected group of completed built site.