Demand Forecasting Models for Dynamic Pricing Optimization
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Solution Overview
Problem
Current machine learning technologies face challenges in predicting demand and optimizing prices for products across multiple classes over future time periods, as they often require known decision variables and struggle with unobservable demand factors, limiting their ability to maximize revenue effectively.
Innovation Solution
The development of machine learning models, such as Autoregressive Distributed Lag (ARDL) and Multinomial Logit (MNL) models, that can forecast demand growth and optimize prices by learning from historical data, including unobservable competitive prices and dynamic factors, allowing for recursive optimization and dynamic pricing across multiple time periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are trained to predict demand and optimize prices, then revenue maximization capability is improved, but the ability to handle unobservable demand factors and known decision variables remains limited
Solution Approach 1:
The patent segments the demand prediction problem into multiple components: observable demand factors, unobservable demand factors, and decision variables. It uses separate models to handle each component - a demand model for observable factors, a competitive model for unobservable factors, and integrates them through a revenue optimization framework. This segmentation allows the system to address the limitation of handling unobservable factors by explicitly modeling them as separate components.
Solution Approach 2:
The patent introduces an intermediary optimization layer that mediates between demand predictions and pricing decisions. This intermediary component (the optimization model) takes demand forecasts and competitive models as inputs and generates optimal pricing strategies, effectively bridging the gap between prediction accuracy and revenue maximization while handling both observable and unobservable factors.
2Productivity
If traditional machine learning models are used for demand prediction, then computational efficiency is maintained, but the ability to perform recursive optimization across multiple time periods is limited
Solution Approach 1:
The patent implements a dynamic optimization framework that operates recursively across multiple time periods. The system dynamically adjusts pricing strategies based on predicted demand and competitive responses at each time step, rather than using static models. This dynamic approach enables the system to adapt to changing conditions over time while maintaining computational efficiency through structured optimization procedures.
Solution Approach 2:
The patent employs preliminary actions by forecasting demand and competitive responses for future time periods before making actual pricing decisions. The system performs predictive actions in advance using trained models, then uses these forecasts to guide optimization decisions, enabling recursive optimization across multiple periods while maintaining efficiency through pre-computed predictions.
3Measurement precision
If models account for both observable and unobservable demand changes, then revenue forecasting accuracy is improved, but model complexity increases
Solution Approach 1:
The patent segments the demand modeling task into distinct components: observable demand factors modeled separately, unobservable demand factors modeled separately, and their interaction handled through the optimization framework. This segmentation allows the system to achieve high forecasting accuracy by addressing each component appropriately while managing overall model complexity through modular architecture.
Data Source
AI summary
A total demand model can be trained, by machine learning and using historical data. The total demand model can be configured to process current data and output first data indicating a predicted future total demand for a product. A target demand model can be trained. The target demand model can be configured to process the current data and, based on processing the current data, output a plurality of class demand models. Each class demand model can be configured to predict demand, for each of a plurality of future time periods, for a plurality of classes of the product. The class demand models configured to optimize, for each of the plurality of future time periods, a respective set of optimal prices for the respective classes of the product that maximizes total expected revenue for the product over the plurality of classes of the product.