Pharmaceutical Sales Forecasting with Holiday Adjustment
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Solution Overview
Problem
Current methods cannot accurately forecast cumulative pharmaceutical sales for a given month until all wholesalers have reported, making it speculative due to erratic parameters like national holidays, and there is a need for a robust and accurate prediction.
Innovation Solution
A system that acquires daily sales data from a sampling of wholesalers, extrapolates total sales using market share information, adjusts predictions based on past accuracy, accounts for holiday impacts, and provides confidence limits to ensure accurate forecasting of cumulative monthly sales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cumulative sales data is collected from all wholesalers after the predefined time period, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by collecting sales data from wholesalers throughout the predefined time period as transactions occur, rather than waiting until the end. This allows the system to have data ready for immediate analysis and forecasting when needed, eliminating the time delay while maintaining data accuracy through continuous collection.
Solution Approach 2:
The system implements feedback mechanisms where preliminary sales data collected during the time period is continuously analyzed and fed back into the forecasting model. This allows for real-time adjustments and accurate cumulative sales projections without waiting for the period to end, resolving the contradiction between having complete data and experiencing time delay.
2Loss of time
If sales forecasting is performed with limited data before the time period ends, then loss of time is reduced, but measurement precision deteriorates
Solution Approach 1:
The forecasting system uses feedback from multiple sources including preliminary sales data collected during the period, historical sales patterns, and market intelligence. This multi-source feedback mechanism allows the system to generate accurate forecasts even with incomplete period data, maintaining measurement precision while reducing time loss.
Solution Approach 2:
The system introduces intermediary elements such as statistical models and forecasting algorithms that bridge the gap between limited preliminary data and accurate cumulative sales predictions. These intermediaries process available data and compensate for missing information, enabling timely forecasts without sacrificing accuracy.
3Adaptability or versatility
If multiple erratic parameters are considered in sales analysis, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system segments the complex forecasting task into distinct modules: data collection from wholesalers, preliminary data processing, historical pattern analysis, multiple parameter consideration (including holidays and market conditions), and forecast generation. This segmentation allows the system to handle multiple erratic parameters systematically without overwhelming complexity, improving adaptability while managing system structure.
Data Source
AI summary
A system and method for providing a monthly cumulative prediction of pharmaceutical sales of a particular pharmaceutical, or group of pharmaceuticals, in a current month is disclosed. In particular, the technique of the present invention provides for extrapolating a sample of sales data for a given day across that entire day (114), extrapolating sales data for at least 14 prior days across an entire month (116); adjusting the monthly cumulative prediction by comparing past predicted values with past actual values (118); adjusting the monthly cumulative prediction to account for sales anomalies created by national or local holidays (120); and provides confidence intervals indicating the believed accuracy of the current monthly cumulative prediction (122).