Demand Supply Modeling with Lognormal Price Sensitivity
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Solution Overview
Problem
Conventional demand and supply modeling methods in economics are inadequate for industries with sparse historical data, as they fail to accurately account for variable market size and price variability, leading to uncertain profitability predictions.
Innovation Solution
The development of systems, methods, and computer program products that model demand, supply, and profitability using lognormal distributions for price sensitivity and market potential, incorporating Monte Carlo simulations to forecast market conditions and account for uncertainty in price and quantity, enabling more accurate predictions in uncertain markets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional demand curves based on historical data and estimates are used, then the modeling approach is simple, but the accuracy of profitability predictions deteriorates when historical data is sparse
Solution Approach 1:
The patent transforms the demand curve from a deterministic function into a probabilistic model by introducing price sensitivity as a random variable following a lognormal distribution. This parameter change allows the model to incorporate uncertainty and variability in consumer price responses, improving prediction accuracy when historical data is limited while maintaining computational tractability through closed-form solutions.
Solution Approach 2:
The patent introduces price sensitivity distribution as an intermediary element between price and quantity demanded. This distribution acts as a mediator that captures the heterogeneous responses of different consumer segments to price changes, allowing the model to bridge the gap between simple historical averages and complex individual-level behavioral data.
2Ease of operation
If conventional demand curves are used, then the model is easy to implement, but it fails to account for variability in the relationship between price and quantity sold
Solution Approach 1:
The patent makes the demand model dynamic by allowing the price sensitivity parameter to vary across different consumer segments and price points rather than assuming a fixed relationship. The lognormal distribution of price sensitivity captures the dynamic and heterogeneous nature of consumer responses to price changes, enabling the model to adapt to different market conditions while maintaining a unified mathematical framework.
Solution Approach 2:
The patent performs preliminary characterization of price sensitivity distributions using available historical data or expert estimates before conducting profitability analysis. This preliminary action establishes the statistical properties of consumer responses in advance, allowing the model to handle variability without requiring extensive real-time data collection during the analysis phase.
3Device complexity
If conventional demand curves are used, then the modeling process is straightforward, but it inadequately integrates variable factors such as market size and prices paid by consumers
Solution Approach 1:
The patent creates a universal modeling framework that simultaneously handles multiple variable factors including market size, price sensitivity, cost structures, and profitability metrics within a single integrated model. The lognormal price sensitivity distribution serves as a universal representation that can capture diverse consumer behaviors across different products and markets, reducing the need for separate models for each factor.
Solution Approach 2:
The patent combines multiple elements (historical data, expert estimates, statistical distributions, and profitability calculations) into a composite modeling approach. This composite model integrates heterogeneous information sources and variable factors into a unified profitability prediction framework, where each component contributes to the overall reliability without requiring any single element to be perfect.
Data Source
AI summary
Systems, methods and computer program products for modeling demand, supply and associated profitability of a good. According to one method, a price sensitivity distribution is determined, and then a market potential distribution of a number of units of the good is determined. Next, a forecasted market is selected according to a Monte Carlo method based upon the market potential distribution, where the forecasted market includes a predefined number of units of the good. A demand and/or supply for the good in the forecasted market is then modeled based upon the price sensitivity distribution and the predefined number of units in the forecasted market. By so modeling demand and/or supply, the method can account for uncertainty in a market for the good, as defined by the number of units of the good purchased and the price at which those units are purchased and/or produced.