Dynamic Projection Factors for Pharmacy Demand Forecasting
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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 ratio estimators are used for demand forecasting, then the forecasting method is simple to implement, but the accuracy of demand forecasts deteriorates in rapidly shifting market conditions
Solution Approach 1:
The patent implements dynamic forecasting by continuously updating projection factors as new sales data becomes available. Instead of relying on static historical ratios, the system dynamically recalibrates projection factors for each outlet based on incoming market data, allowing the forecasting model to adapt to rapidly changing market conditions while maintaining individual outlet-level accuracy
Solution Approach 2:
The system incorporates feedback mechanisms where actual sales data from outlets is continuously fed back into the model to recalculate and refine projection factors. This feedback loop enables the system to learn from past predictions versus actual outcomes, progressively improving forecast accuracy without requiring complex manual recalibration
2Productivity
If sample data from a subset of outlets is used to estimate sales at all outlets, then the data collection burden is reduced, but the precision of individual outlet estimates deteriorates due to biased assumptions
Solution Approach 1:
The patent applies local quality by calculating distinct projection factors for each individual outlet rather than applying a uniform ratio across all outlets. Each outlet's projection factor is tailored to its specific characteristics and performance patterns, enabling precise individual outlet estimates while still utilizing sample data from a subset of outlets for calibration
Solution Approach 2:
The system changes parameters by transitioning from fixed historical ratios to dynamic projection factors that are continuously updated. These projection factors serve as adjustable parameters that reflect current market conditions and individual outlet performance, allowing the model to maintain precision even with limited sample data
3Device complexity
If geographic-based ratio estimators are used to scale up sample data, then the forecasting approach is computationally simple, but the reliability of estimates deteriorates because assumptions about uniform prescriber behavior are false
Solution Approach 1:
The patent segments the forecasting approach by outlet rather than by geographic region. Each outlet is treated as a distinct entity with its own projection factor, eliminating the false assumption of uniform behavior across geographic areas. This segmentation increases reliability by recognizing and modeling individual outlet variability while keeping computational complexity manageable through automated calculations
4Stability of the object's composition
If static historical models are used for demand prediction, then the model structure is simple and stable, but the adaptability to rapidly shifting market conditions deteriorates
Solution Approach 1:
The patent transforms the static historical model into a dynamic system where projection factors are continuously updated as new sales data arrives. This dynamic approach maintains model stability through consistent methodology while achieving adaptability to changing market conditions by incorporating real-time data recalibration for each outlet
Solution Approach 2:
The system implements continuous useful action by constantly updating projection factors as new sales data becomes available. Rather than periodic recalibration, the model continuously adapts to market changes, maintaining both stability through systematic updates and adaptability through ongoing data incorporation
Data Source
AI summary
Projection factors are used to project product sales data from sample retail outlets to the universe of outlets. Weekly forecasts of market conditions and product demand are generated based on the projected product sales data. The projection factors and the weekly forecasts are updated during the course of the forecasted week, for example, daily, as data on actual product sales during the forecasted week is received.