Demand Rule Partitioning for Multi-Channel Forecasting
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Solution Overview
Problem
Retailers face challenges in effectively forecasting and optimizing demand across multiple purchasing choices due to the complexity of interrelationships between different purchasing channels, such as online and brick-and-mortar stores, and the difficulty in understanding how various marketing strategies influence consumer behavior.
Innovation Solution
A processor executes a partitioning rule that receives a purchasing choice calendar, marketing calendar, and sales history to generate demand rules for multiple calculation paths, allowing for the forecasting of demand and sales across various user choices, and optimizes pricing, promotions, and inventory management based on these rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If retailers use traditional forecasting methods for single purchasing choice, then the forecasting process is simple, but it cannot capture interrelationships between multiple purchasing choices and channels
Solution Approach 1:
The patent segments the forecasting system into multiple independent calculation paths, each handling a specific purchasing choice (online, brick-and-mortar, mobile). Each path processes demand independently using the same forecasting logic, allowing the system to scale to multiple channels without increasing overall complexity. The segmentation enables parallel processing of different purchasing choices while maintaining manageable complexity in each individual path.
2Loss of information
If retailers manually analyze interrelationships between purchasing choices, then understanding of consumer behavior is achieved, but the process is time-consuming and inefficient
Solution Approach 1:
The patent implements feedback mechanisms where demand forecasts from one purchasing choice path are used as inputs to other paths. For example, online demand forecasts feed into brick-and-mortar demand calculations and vice versa, automatically capturing interrelationships between channels. This feedback loop enables the system to understand consumer behavior across channels without manual analysis, significantly reducing the time required while maintaining comprehensive understanding of purchasing choice interrelationships.
3Productivity
If retailers optimize pricing and promotions for each channel separately, then simplicity is maintained, but overall demand optimization is reduced
Solution Approach 1:
The patent creates universal demand rules that function across all purchasing choice paths simultaneously. The same pricing and promotion optimization logic is applied in each path, but the rules automatically adapt to channel-specific characteristics. This multi-functionality allows the system to optimize demand across all channels with a unified approach, improving overall productivity while avoiding the need to create separate complex optimization systems for each channel.
Data Source
AI summary
A demand rule can handle simultaneous retail purchasing options available to users such as brick and mortar, online, mobile, catalog, kiosk, coupon, loyalty, volume discounts, and multiples. The method can include a demand rule for each single purchasing option, a rule for marketing influence, and a rule for partitioning user demand across purchasing options. The demand rule for each single purchasing option, rule for marketing influence, and rule for partitioning user demand across purchasing options can each be measured independently to enable computationally efficient methods for forecasting and sales optimization.