Raw Material Demand Forecasting With Autonomous Futures Procurement
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Solution Overview
Problem
The proliferation of data from IoT sensors and wearable technologies overwhelms the ability to transmit and process data effectively in value chain networks, leading to complexity and missed opportunities for timely decision-making.
Innovation Solution
A method for processing queries in a distributed database using edge devices, involving dynamic ledgers and probability distribution models to generate approximate responses, and optimizing data management through predictive models and digital twins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from IoT sensors and wearable technologies is transmitted and processed in value chain networks, then data availability and insight opportunities increase, but network complexity and processing overhead increase
Solution Approach 1:
The patent segments the centralized data processing architecture into distributed edge computing nodes. Each edge device independently processes local data from IoT sensors and wearables, dividing the overwhelming data stream into manageable segments that can be processed locally rather than transmitted through complex centralized networks.
Solution Approach 2:
The patent extracts critical processing functions from the centralized cloud infrastructure and places them at the edge devices. By taking out data processing capabilities from the complex centralized system and embedding them in distributed edge nodes, the patent reduces network overhead while maintaining data availability.
2Measurement precision
If all IoT sensor data is transmitted through the network for processing, then comprehensive data analysis is achieved, but network bandwidth consumption and processing time increase
Solution Approach 1:
The patent implements preliminary data processing and filtering at the edge devices before data is transmitted or stored. Edge nodes perform initial analysis, aggregate data, and prepare processed results in advance, reducing the need to transmit raw data and enabling faster response times while maintaining analytical comprehensiveness.
Solution Approach 2:
The patent creates local copies of data processing capabilities at edge devices through digital twins and local models. Instead of transmitting all raw data to centralized systems for analysis, edge devices maintain local copies and processing models that can independently analyze data, reducing network bandwidth consumption and processing delays.
3Productivity
If centralized systems process all value chain data, then centralized control is maintained, but system scalability and response time deteriorate
Solution Approach 1:
The patent divides the centralized control system into autonomous edge computing segments distributed across the value chain. Each edge device operates independently with local decision-making capabilities, allowing the system to scale horizontally by adding more edge nodes without increasing centralized complexity, while dramatically improving response times.
Data Source
AI summary
A raw material system includes a product manufacturing demand estimation system programmed to calculate an expected demand for a product at a future point in time. An environment detection system identifies at least one of an environmental condition or an environmental event. A raw material production system estimates a raw material availability at the future point in time based on the expected demand and the environmental condition/event. A raw material requirement system calculates a required raw material amount to manufacture the product at the future point in time based on the expected demand and the environmental condition/event. A raw material procurement system autonomously configures 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.