AI Foundation Model for Demand Forecasting and Operation Control
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Solution Overview
Problem
Current methods for organization operation demand forecasting and operation planning rely heavily on human estimation and simple arithmetic, leading to unreliable and ineffective processes.
Innovation Solution
An artificial intelligence method utilizing a real-time self-trained private-public foundation model for generating demand forecasts, operation planning, operation monitoring, and operation control, which integrates multiple AI components and data sources for optimized decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators use simple arithmetic and experience for demand forecasting, then the process is simple and easy to operate, but the reliability and effectiveness of the forecast is poor
Solution Approach 1:
The patent replaces manual human operators performing simple arithmetic with an automated AI-based system that processes data through machine learning models. This substitution transforms the mechanical manual calculation process into an automated intelligent system, significantly improving forecast reliability while managing complexity through automation.
Solution Approach 2:
The system incorporates self-training capabilities where the AI model automatically learns from historical data and operational feedback without requiring manual reprogramming. The model continuously improves its forecasting accuracy by processing real-world data patterns, enabling the system to serve itself in terms of model optimization and adaptation.
2Reliability
If human operators manually monitor and adjust operation plans, then the system is easy to operate, but the process is not reliably repeatable and ineffective
Solution Approach 1:
The system implements continuous feedback loops where operational data is collected, analyzed by the AI model, and used to automatically adjust operation plans. This feedback mechanism ensures consistent, repeatable decision-making based on actual performance data rather than subjective human judgment, improving reliability while maintaining operational simplicity through automation.
Solution Approach 2:
The AI system pre-calculates multiple scenario-based operation plans in advance, considering various potential outcomes and constraints. This preliminary action allows the system to have ready-to-execute plans that are optimized beforehand, ensuring repeatable and reliable operations without requiring complex real-time manual adjustments.
3Productivity
If simple arithmetic methods are used for resource scheduling, then the operation is simple, but the effectiveness of resource allocation is poor
Solution Approach 1:
The system dynamically adjusts resource allocation based on real-time operational data and changing conditions. Rather than using static simple arithmetic, the AI model continuously optimizes resource distribution to match actual demand patterns, improving productivity while managing complexity through adaptive automation that learns from operational feedback.
Data Source
AI summary
An artificial intelligence platform for utilizing real-time self-trained private-public foundation models in generating demand forecast, operation planning, monitoring, and control. This platform helps users to optimize their operations in delivery of products or services. The platform generates forecast, requirements, schedules, delivery, payment, and support data for carrying out the operations. It generates control data to control field systems to carry out the operations, and monitor real-time field operation resulting data that are fed back from the field systems to the platform. This platform combines different types of input data, generates training data, utilizes a group of public and private foundation models and a number of AI algorithms. The Platform utilizes foundation models to encode data into N-dimension feature spaces, identifies closest types of operations, defines predictive functions, associate specific data to specific predictive functions, and use security measures to protect proprietary information.