Product Revenue Optimization with Integrated Scenario Modeling
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Solution Overview
Problem
Manufacturers face challenges in optimizing product revenue by managing dynamic demand mix and complexity, lacking data-driven tools for decision-making, and existing solutions address only parts of the problem in a disconnected manner, taking too long to run.
Innovation Solution
A processor-implemented method and system for optimizing product revenue, utilizing hardware processors to analyze revenue drivers, predict best-selling configurations, create scenarios, and optimize using an autoregressive neural network model to achieve a target value, with iterative refinement to ensure tolerance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional tools and approaches are used to address product revenue optimization, then particular parts of the problem can be addressed, but the solutions are disconnected and take too long to run
Solution Approach 1:
The patent combines multiple disconnected tools and approaches into a single integrated revenue optimization system. The configurator, scenario analysis, and optimization modules work together as a unified system rather than separate tools, enabling end-to-end revenue optimization from product configuration through scenario planning to final optimization execution.
Solution Approach 2:
The optimization system is designed to handle multiple functions within a single platform: it performs product configuration, scenario analysis, revenue optimization, and provides data-driven decision-making capabilities. This multi-functional approach eliminates the need for multiple separate tools and significantly improves execution speed while maintaining comprehensive problem-solving capability.
2Measurement precision
If data-driven tools are implemented for decision-making, then revenue optimization accuracy improves, but the system complexity increases
Solution Approach 1:
The system automatically performs data extraction, scenario creation, and optimization without requiring manual intervention for each step. The configurator automatically generates configurations from product models and feature variants, and the optimization module automatically selects the best scenarios based on revenue targets, reducing the need for complex manual data processing while maintaining high accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where historical sales data is used to train forecasting models, which then provide predictions that feed into the optimization process. The optimization results are fed back to adjust future configurations and scenarios, creating a continuous improvement loop that enhances prediction accuracy while managing system complexity through automated feedback loops.
3Reliability
If comprehensive scenario analysis is performed, then decision-making quality improves, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary actions by pre-creating multiple scenarios and configurations before the actual optimization decision is needed. The configurator pre-generates product configurations from feature variants, and the scenario analysis module pre-explores multiple revenue scenarios, so that when the final optimization is required, the groundwork is already laid and can be executed quickly and reliably.
Data Source
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AI summary
Growing revenue and market to meet dynamic demand mix while managing complexity are major challenges to manufacturers. Building a right product mix that effectively manages supply and production cost without losing demand is therefore very imperative for offering to customers. Conventionally available tools and approaches only address particular parts of the whole problem in disconnected fashion, and these take too long to run. Embodiments of the present disclosure provide systems and methods for optimizing product revenue by setting up problems, model revenue drivers, configuring relevant scenarios including decision inputs like relevant data, constraints, or business rules to establish a total target revenue and analyze resulting outputs with iterative improvements towards the target value for product models.