Pharmacy Prescription Demand Forecasting via Dynamic Recalibration
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Solution Overview
Problem
Existing methods for forecasting 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 and inability to provide accurate, timely predictions for specific drugs at particular 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 using incoming market data from reporting outlets to estimate demand for both sampled and non-sampled outlets, incorporating adjustment factors for unusual events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If historical data and uniform activity assumptions are used for forecasting, then the forecasting system is simple to implement, but the accuracy and reliability of demand predictions deteriorate in rapidly shifting market conditions
Solution Approach 1:
The forecasting system dynamically updates predictions by continuously incorporating incoming market data and recalibrating projection factors. Instead of relying on static historical data, the system adapts to rapidly changing market conditions by adjusting forecasts in real-time based on new information from reporting outlets.
Solution Approach 2:
The system uses feedback from incoming market data to continuously evaluate and recalibrate prediction models. By monitoring actual market conditions and comparing them with forecasts, the system adjusts projection factors to improve accuracy while maintaining operational simplicity through automated feedback loops.
2Productivity
If sample data from limited outlets is used to estimate demand, then data collection is efficient, but the representativeness and accuracy of estimates for unsampled outlets deteriorates
Solution Approach 1:
The system uses an intermediary approach by selecting sample outlets that are representative of the broader market and using them as proxies for unsampled outlets. Through careful selection of reporting outlets and development of projection factors, the system bridges the gap between limited sample data and comprehensive market demand estimation.
Solution Approach 2:
The system changes parameters by developing outlet-specific projection factors that capture the unique characteristics of different outlet types and locations. By adjusting these parameters based on outlet attributes and market conditions, the system improves the representativeness of sample data while maintaining data collection efficiency.
3Productivity
If uniform projection factors are applied to all outlets, then the forecasting process is simple and fast, but the precision of outlet-specific demand predictions deteriorates
Solution Approach 1:
The system segments outlets into different categories based on their characteristics, such as outlet type, location, and product mix. By developing separate projection factors for each segment, the system achieves outlet-specific precision while maintaining forecasting efficiency through standardized procedures for each category.
Solution Approach 2:
The system applies local quality by customizing projection factors for specific outlets or outlet segments rather than using uniform factors for all outlets. Each outlet receives tailored projection factors that reflect its unique market conditions, thereby improving prediction precision without significantly increasing overall process complexity.
Data Source
AI summary
Systems and methods for product level projections of pharmacy prescriptions within product therapy classes are provided. Average wholesale dollar sales amounts are obtained or estimated at channel/outlet/product-therapy levels. All of the average wholesale dollar sales amounts are converted to prescription data at channel/outlet/product-therapy levels using a correlation function. The correlation function is derived by correlating samples of total prescription data where available with corresponding average wholesale dollar sales amounts.