Raw Material Procurement Using Edge Demand and Availability Forecasting
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Solution Overview
Problem
The proliferation of data from IoT sensors and other sources in value chain networks overwhelms traditional centralized data collection methods, leading to complexity and inefficiencies in data transmission and automated decision-making.
Innovation Solution
A method for processing queries in a distributed database using edge devices, which store queries on a dynamic ledger, generate approximate responses based on summary data, and transmit these responses, leveraging technologies like blockchain and neural networks for efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If centralized data collection methods are used to gather data from IoT sensors and value chain networks, then comprehensive data coverage is achieved, but system complexity and transmission burden increase significantly
Solution Approach 1:
The patent divides the centralized data collection system into multiple edge devices distributed across the value chain network. Each edge device independently collects and processes data from local IoT sensors, eliminating the need for a single centralized collection point and reducing system complexity while maintaining comprehensive data coverage.
Solution Approach 2:
The patent introduces a new dimensional approach by implementing hierarchical data collection across multiple levels (edge devices, regional aggregators, central system). This multi-dimensional architecture allows data to be collected comprehensively at the edge level while distributing processing responsibilities, thereby reducing the transmission burden on any single point in the system.
2Loss of information
If all raw data from distributed IoT sensors is transmitted to centralized systems, then complete information availability is achieved, but data transmission burden and network load increase
Solution Approach 1:
The patent extracts and processes data locally at edge devices before transmission to centralized systems. By performing preliminary data processing, filtering, and aggregation at the edge level, the system maintains complete information availability while significantly reducing the volume of data that needs to be transmitted across the network, thereby lowering transmission burden and energy consumption.
Solution Approach 2:
The patent implements preliminary data processing actions at edge devices, including data validation, filtering, and local aggregation, before data is transmitted to centralized systems. This preliminary action ensures that only necessary and processed data is transmitted, maintaining information availability while reducing network load and transmission energy requirements.
3Extent of automation
If centralized systems process all data from distributed networks, then unified decision-making is achieved, but processing time and response delay increase
Solution Approach 1:
The patent segments decision-making authority across multiple edge devices distributed in the value chain network. Each edge device can independently make local decisions based on its processed data, eliminating the need for all data to be transmitted to a centralized system for processing. This maintains unified decision-making through coordinated edge devices while significantly reducing response time and delays.
4Stability of the object's composition
If traditional centralized data management is used in value chain networks, then data consistency is maintained, but scalability and adaptability to distributed architectures are limited
Solution Approach 1:
The patent implements a universal data management framework that operates effectively across both centralized and distributed architectures. The system uses standardized data schemas, protocols, and processing methods that maintain data consistency regardless of whether data is collected and processed centrally or distributed across multiple edge devices, thereby enabling scalability and adaptability to different architectural configurations.
Data Source
AI summary
A raw material system includes a product manufacturing demand estimation system programmed to calculate an expected demand for a product. The system includes an environment detection system configured to identify an environmental condition or event. The system includes a raw material production system programmed to estimate a raw material availability at the future point in time based on the expected demand and the environmental condition/event. The system includes a raw material requirement system programmed to calculate a required raw material amount to manufacture the product based on the expected demand and the environmental condition/event. The system includes a raw material procurement system programmed to autonomously configure a futures contract for procurement of at least a portion of the required raw material amount in response to the required raw material amount calculation exceeding the raw material availability estimation.