Dynamic Forecasting System for Pharmaceutical Demand
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Solution Overview
Problem
Existing methods for predicting market demand for pharmaceutical and healthcare products are inadequate for rapidly shifting market conditions, as they rely on historical data and assume uniform activity across sampled and unsampled outlets, leading to biased estimates.
Innovation Solution
The development of forecasting systems and methods that analyze sample data at product levels, continuously evaluate and recalibrate prediction models, and dynamically update forecasts using incoming market data from reporting outlets to estimate demand for both sampled and non-sample outlets, incorporating adjustment factors for unusual events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If historical data and uniform activity assumptions are used for prediction, then the prediction method is simple and easy to implement, but the prediction accuracy deteriorates under rapidly shifting market conditions
Solution Approach 1:
The patent applies dynamics by continuously updating prediction models with incoming market data and recalibrating parameters in real-time. The system transitions from static historical assumptions to dynamic adaptive modeling that responds to changing market conditions, thereby improving prediction accuracy without significantly increasing implementation complexity
Solution Approach 2:
The patent implements feedback mechanisms by comparing actual sales data from sampled outlets against predictions, then using this feedback to recalibrate prediction models and adjust parameters. This closed-loop approach continuously improves prediction accuracy while maintaining system simplicity through automated feedback processing
2Quantity of substance
If sample data from limited outlets is used to estimate demand for all outlets, then data collection cost is reduced, but estimation bias increases due to non-uniform activity across outlets
Solution Approach 1:
The patent applies local quality by recognizing that different outlets have different activity characteristics and applying outlet-specific calibration factors and parameters. Instead of assuming uniform activity across all outlets, the system tailors predictions to local outlet characteristics while still using data from a limited sample, thereby reducing estimation bias without requiring comprehensive data collection
Solution Approach 2:
The patent uses sampled outlets as intermediaries to infer demand characteristics for unsampled outlets. By collecting data from a representative sample and using statistical calibration, the system indirectly estimates demand for the broader population of outlets, reducing direct data collection costs while maintaining estimation accuracy through proper sampling and calibration techniques
3Device complexity
If static prediction models are used, then model complexity is minimized, but adaptability to rapidly shifting market conditions deteriorates
Solution Approach 1:
The patent transforms static prediction models into dynamic adaptive models that automatically adjust to changing market conditions. The system continuously recalibrates parameters and updates predictions based on incoming data, providing market responsiveness while managing complexity through automated processes and efficient data structures
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting model parameters based on incoming market data and observed patterns. The system modifies prediction parameters in response to changing conditions, enabling adaptability without requiring complete model restructuring, thus balancing complexity and responsiveness
Data Source
AI summary
A method and system of predicting market information includes the steps of receiving first data, forecasting further data based on the first data, receiving second data and comparing the further data with the second data, and creating an adjustment factor to account for any difference between the further data and the second data.