Automated Supplier Matching via Data Standardization
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Solution Overview
Problem
Heavy industries face inefficiencies and complexities in procurement, manufacturing, and distribution due to disparate data communication standards and the reliance on manual, rules-based logic, leading to time-consuming and costly processes for matching manufacturing supplier capabilities with requirements.
Innovation Solution
A data-driven system that utilizes machine learning algorithms and a platform architecture to analyze and match procurement, manufacturing, and distribution requirements with supplier capabilities, employing APIs for secure data transfer and encryption, and data repositories for efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual searching and evaluation methods are used to match manufacturing suppliers with requirements, then personnel can exercise judgment and adaptability, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces manual mechanical searching and evaluation processes with an automated data-driven system that uses machine learning algorithms, natural language processing, and automated data extraction to match supplier capabilities with procurement requirements, thereby reducing time loss while maintaining or improving matching accuracy
Solution Approach 2:
The patent introduces an intermediary automated platform that acts as a mediator between procurement requirements and supplier capabilities, using data repositories, algorithms, and processing systems to facilitate efficient matching without requiring direct manual intervention, thus resolving the contradiction between accuracy and time consumption
2Ease of operation
If rules-based logic and form-driven workflows are employed for supplier matching, then processes are structured and controllable, but the system becomes inflexible and inaccurate
Solution Approach 1:
The patent implements dynamic algorithms that can adapt to different procurement scenarios and supplier profiles, moving away from static rules-based logic to flexible data-driven decision-making while maintaining structured process control through the automated platform's workflow management capabilities
Solution Approach 2:
The patent changes the fundamental parameters of the matching system from fixed rules and forms to dynamic data-driven parameters that can be adjusted based on the specific requirements and supplier capabilities, enabling both structured control and flexibility through algorithmic parameter optimization
3Stability of the object's composition
If disparate data communication standards and protocols are used in heavy industries, then existing systems can maintain their current operations, but data interconnectivity problems arise
Solution Approach 1:
The patent creates a universal data repository and standardized data structures that can accommodate multiple data communication standards and protocols, enabling the system to interface with diverse existing systems while maintaining data interconnectivity through a common standardized interface layer
Solution Approach 2:
The patent introduces an intermediary data standardization layer that translates between disparate data communication standards and protocols used in heavy industries, allowing existing systems to maintain their operations while enabling seamless data interconnectivity through the standardized intermediary platform
4Ease of manufacture
If conventional applications are used in heavy industries, then implementation is straightforward with existing tools, but compatibility between interconnected systems is lacking
Solution Approach 1:
The patent segments the system into modular components including data repositories, processing algorithms, and interface layers that can be independently implemented and integrated, maintaining ease of manufacture through modular deployment while ensuring system compatibility through standardized interfaces between segments
Data Source
AI summary
Techniques for data-driven requirements analysis and matching are described, including receiving an input from a notification service over a data network, the input including data indicating a requirement used to manufacture, procure, or distribute an item, the requirement being generated by an application configured to identify an attribute of the item, querying an endpoint in response to the input, the input indicating a machine capable of manufacturing the item, transforming the input, the data, and a result to a data format using a logic module of the platform to generate match data identifying a supplier capable of manufacturing at least a portion of the item, ranking the match data, and changing the match data from the data format to another format used to render a display of resultant data from the match data presented on a display.