Dynamic Sales Forecasting Adjustment Factors
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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
A system and method for forecasting market demand that involves continuous evaluation and recalibration of sample data, dynamic updating of prediction models, and the use of adjustment factors to account for unusual events, allowing for accurate and reliable predictions of drug demand at specific pharmacies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If historical data and uniform activity assumptions are used for forecasting, then the forecasting method is simple and easy to implement, but the estimation accuracy deteriorates under rapidly shifting market conditions
Solution Approach 1:
The patent applies dynamics by continuously updating the forecasting model with recent actual sales data. Instead of relying on static historical assumptions, the system dynamically recalibrates prediction parameters (such as beta weights and adjustment factors) based on the most recent performance, allowing the model to adapt to rapidly changing market conditions while maintaining computational feasibility
Solution Approach 2:
The patent implements feedback mechanisms by comparing actual sales data against predicted sales, then using the differences (residuals) to adjust future predictions. The system calculates adjustment factors based on the ratio of actual to predicted sales and applies these factors to subsequent forecasts, creating a closed-loop system that continuously improves accuracy without requiring complex reengineering
2Productivity
If sample data from limited outlets is used for estimation, then data collection is efficient and cost-effective, but the representativeness and reliability of estimates for all outlets deteriorates
Solution Approach 1:
The patent applies local quality by treating different outlet groups differently in the estimation process. Instead of applying a uniform scaling factor to all unsampled outlets, the system calculates separate adjustment factors for different outlet types, geographic regions, or product categories based on their specific characteristics and recent performance patterns. This allows the model to maintain reliability by accounting for local variations while preserving the efficiency of sampled data collection
Solution Approach 2:
The patent uses sampled outlets as intermediaries to infer characteristics of unsampled outlets. By establishing relationships between sampled and unsampled outlets through correlation analysis and adjustment factors, the system allows information from the limited sample to indirectly represent the broader population, bridging the gap between efficient data collection and comprehensive estimation
3Device complexity
If uniform scaling factors are applied to all outlets, then the estimation process is computationally simple, but the accuracy deteriorates when outlets have different activity levels and trends
Solution Approach 1:
The patent applies segmentation by dividing the outlet population into distinct groups based on characteristics such as size, location, product mix, or historical performance. Each segment receives its own adjustment factor or beta weight rather than a uniform scaling factor. This segmentation allows the model to capture heterogeneity in outlet behavior while maintaining computational simplicity through modular calculation approaches for each segment
Solution Approach 2:
The patent changes parameters dynamically by adjusting scaling factors, beta weights, and adjustment factors based on recent performance data. Instead of using fixed uniform parameters, the system modifies these parameters for different outlets or outlet groups based on their specific trends and variability. This allows the model to adapt to different activity levels and trends while keeping the computational framework relatively simple through parameter adjustment rather than structural complexity
Data Source
AI summary
Timely projections of product sales for a reporting time period are obtained by combining actual sales data received from reporting stores and estimated sales data for non-reporting stores. The projections are adjusted to account for trailing data, which may be reported after the end of the subject time period.