Multi-Channel Demand Forecasting for Inventory Control
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Solution Overview
Problem
Existing demand forecasting methods struggle to accurately predict future demand for products, especially in large retail organizations with multiple locations and channels, due to complexities such as seasonal changes and incorrect attribution of demand to specific locations, especially in cases of online orders.
Innovation Solution
A system and method for multi-channel demand forecasting that generates item-location forecasts on a per-item, per-location basis, allowing for selective model selection and training, and user override options, enabling efficient computation across large supply chains with thousands of locations and millions of items, and accurately attributes demand to individual locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If demand forecasting is performed using statistical models and algorithms relying on past data, then demand prediction can be calculated systematically, but accuracy deteriorates when seasonal changes and multi-channel complexities are involved
Solution Approach 1:
The patent segments demand forecasting into separate models for different sales channels (in-store, online pickup, online delivery) and different location types. This allows each channel to be forecasted independently with channel-specific parameters, improving accuracy while maintaining systematic processing capability.
Solution Approach 2:
The patent applies location-specific demand models that account for local characteristics such as geographic region, store size, and local market conditions. Each location receives customized forecasting parameters rather than applying a uniform model, thereby improving local forecast accuracy.
2Loss of information
If online orders are attributed to fulfillment locations, then demand data can be captured at specific locations, but attribution accuracy deteriorates when customers do not choose the fulfillment location
Solution Approach 1:
The patent segments online demand into two distinct components: localized demand (when customers choose specific pickup locations) and non-localized demand (when customers accept default fulfillment). This segmentation prevents incorrect attribution by treating these fundamentally different demand types separately.
Solution Approach 2:
The patent introduces an intermediary classification layer that determines whether online demand should be attributed to a specific location or kept at the chain level. This intermediary step prevents direct and potentially incorrect attribution of all online orders to fulfillment locations.
3Measurement precision
If per-item, per-location forecasting is implemented across large supply chains, then location-specific accuracy improves, but computational complexity increases significantly
Solution Approach 1:
The patent segments the forecasting system into hierarchical levels (chain-level and location-level models) and sales channels. This segmentation allows computational resources to be allocated efficiently, with simpler models at the chain level and more detailed models only where needed at location levels.
Solution Approach 2:
The patent dynamically adjusts model parameters based on data availability and location characteristics. Locations with sufficient historical data receive more complex models, while locations with limited data use simplified models, optimizing the balance between accuracy and computational complexity.
Data Source
AI summary
Methods and systems for forecasting demand for items across multiple channels are disclosed. In some implementations, multi-channel demand forecasting may be performed on a per-item, per-location basis, by selectively generating item-location forecasts for each item and location within a supply chain for each channel, or disaggregating a chain level forecast on a per-item basis to each location. Particular selection of an appropriate model, and selective training of models, allows for efficient computation of such forecasts across a large supply chain with thousands of locations and hundreds of thousands, or millions, of items for which forecasts are generated.