Integrated ML Models for Dynamic Demand Optimization
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Solution Overview
Problem
Traditional systems for enterprise resource planning are not architected to handle high-volume, change, and speed scenarios with complex relationships, leading to inefficiencies in demand anticipation and collective optimization of parameters across different departments in organizations.
Innovation Solution
A method integrating one or more machine-learning models that impact different factor groups to generate dynamic recommendations for collectively optimizing a parameter, involving processing operational data, training ML models, integrating them, determining product demand, and updating recommendations in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional enterprise resource planning systems are used to manage operations, then system simplicity and ease of implementation are maintained, but the system cannot handle high-volume, high-speed scenarios with complex relationships across multiple departments
Solution Approach 1:
The patent segments the organization's operations into multiple independent machine learning models, each handling a specific factor group (e.g., pricing, inventory, promotions). This allows each model to be optimized independently for high-volume, high-speed processing while the overall system maintains manageability through modular architecture.
Solution Approach 2:
The patent merges multiple specialized machine learning models into an integrated system that collectively optimizes organizational parameters. The models are integrated through a common data platform and coordination mechanism, enabling them to handle complex cross-departmental relationships while maintaining the adaptability of individual models.
2Productivity
If separate teams optimize for different factor groups independently, then each team can focus on its specific objectives, but the organization cannot collectively optimize organizational parameters
Solution Approach 1:
The patent creates a universal machine learning platform that serves multiple factor groups simultaneously. Each machine learning model can be applied to different factor groups (pricing, inventory, promotions) while following the same architectural patterns and integration protocols, enabling collective optimization without proportionally increasing integration complexity.
Solution Approach 2:
The patent implements feedback loops where the output of one machine learning model becomes input for another, creating a coordinated optimization system. For example, pricing model outputs feed into demand forecasting models, which in turn inform inventory optimization models, enabling collective optimization through interdependent decision-making.
3Measurement precision
If traditional siloed approaches are used for sales, distribution, promotion and inventory optimization, then implementation simplicity is maintained, but demand anticipation accuracy and revenue optimization are limited
Solution Approach 1:
The patent enables each machine learning model to automatically learn and optimize its specific factor group without manual intervention. The models self-adjust parameters based on incoming data and feedback from other models, achieving high demand anticipation accuracy through automated, data-driven decision-making across all departments.
Data Source
AI summary
A method for integrating a machine learning (ML) model that impacts different factor groups for generating a dynamic recommendation to collectively optimize a parameter is provided. The method includes (i) processing a specification information and operational data associated with a demand management service obtained from client devices (116A-N), (ii) training the ML models with processed specification information and the operational data to obtain a trained ML model that includes an anticipation ML model that optimizes demand parameter or recommendation ML model that generates recommendation for optimizing a factor group, (iii) integrating the trained ML model with the ML models by setting an output of a first ML model as a feature of a second ML model and (iv) determining a demand of a product using the trained ML models and quantifying probabilistic values that signify prediction of the demand.