Pharmaceutical Demand Forecasting via Dynamic Sample Imputation
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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 assumptions that lead to biased estimates, failing to provide accurate and timely forecasts for individual pharmacies.
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 based on incoming market data, allowing for accurate and reliable predictions of demand for specific drugs at particular pharmacies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If historical data and static assumptions are used for demand prediction, then the prediction model is simple and easy to implement, but the accuracy and timeliness of forecasts deteriorate in rapidly shifting market conditions
Solution Approach 1:
The patent implements dynamic prediction models that continuously adapt to changing market conditions by incorporating real-time data from multiple sources (prescription data, inventory data, market trends). The system updates predictions dynamically rather than relying on static historical assumptions, allowing it to respond to rapidly shifting pharmaceutical market conditions while maintaining forecast accuracy for individual pharmacies.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring actual sales data and comparing it with predictions. This feedback loop allows the model to learn from discrepancies and improve future predictions, resolving the contradiction between model simplicity and forecast accuracy in dynamic markets.
2Productivity
If sample data from limited outlets is used for estimation, then data collection is efficient and cost-effective, but the representativeness and accuracy of market-wide estimates deteriorate
Solution Approach 1:
The patent uses sophisticated statistical models and algorithms as intermediaries to bridge the gap between limited sample data and market-wide estimates. These intermediaries process and analyze sample data from participating pharmacies, applying weighting factors and adjustment mechanisms to produce accurate market-wide demand estimates without requiring comprehensive data collection from all outlets.
Solution Approach 2:
The prediction system is designed to handle multiple functions simultaneously: it generates forecasts for individual pharmacies, produces market-wide estimates, identifies trends across different product categories, and provides insights for both sampled and unsampled outlets. This multi-functionality allows the system to maximize the value of limited sample data while maintaining accuracy across different application levels.
3Stability of the object's composition
If static prediction models based on historical data are used, then the system is stable and easy to maintain, but the adaptability to rapidly changing market conditions deteriorates
Solution Approach 1:
The patent implements dynamic prediction models that continuously adapt to changing market conditions by incorporating real-time data from multiple sources (prescription data, inventory data, market trends). The system updates predictions dynamically rather than relying on static historical assumptions, allowing it to respond to rapidly shifting pharmaceutical market conditions while maintaining forecast accuracy for individual pharmacies.
Solution Approach 2:
The system operates continuously by constantly ingesting new data and updating predictions in near-real-time. This continuous operation ensures the system remains stable through consistent performance while simultaneously adapting to market changes through ongoing data processing and model refinement, eliminating the trade-off between stability and adaptability.
Data Source
AI summary
Prescriptions are imputed or allocated to a non-reporting sample outlet in a universe of outlets that generally report prescription data to a database. Prescriptions are imputed or allocated based on the outlet's recent prescription or physician distribution. Prescription allocations to an outlet for a particular product in a product group are proportional to the ratio of the recent prescriptions for that particular product and for the product group.