Inventory Assortment Model Using Co-Located Item Interactions
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Solution Overview
Problem
Determining which items to stock in a warehouse is challenging due to limited physical space and varying demand, and existing computer modeling struggles to predict item-item effects, especially for items without prior interaction histories.
Innovation Solution
A machine-learning model is trained using a set of items with user interaction data, considering features of both the item and co-located items, with a subset of items used as training examples to predict interactions, and validated using holdout items, allowing for the simulation of adding new items to an existing assortment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer modeling is used to predict user interactions with items, then prediction capability is improved, but accuracy for new items without prior interaction histories deteriorates
Solution Approach 1:
The patent uses co-located items as intermediaries to bridge the gap between historical data and new items. By leveraging interaction data from items that are physically located together in the warehouse, the model can infer predictions for new items even when those items lack direct historical interaction data. The co-located items serve as a mediator that transfers predictive information from the historical dataset to the new item being evaluated.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing interaction data for co-located items before the new item is introduced. Historical user interaction data is collected and structured in advance, creating a ready-to-use foundation that enables immediate prediction capability when a new item is added to the warehouse, without requiring the new item to have its own historical data first.
2Adaptability or versatility
If more items are stocked in the warehouse, then variety and customer satisfaction are improved, but physical space constraints are worsened
Solution Approach 1:
The system changes the parameter of item selection from arbitrary or manual decisions to data-driven predictions based on user interaction patterns. By transforming the selection criterion into a predictive metric (expected user interactions), the system can objectively evaluate which items to stock, maximizing variety within the constrained physical space by selecting only those items with highest predicted demand.
Solution Approach 2:
The patent replaces manual or mechanical inventory selection processes with an automated machine learning model. Instead of physically examining each item or using simple heuristics, the system uses computational prediction to evaluate item performance, substituting the mechanical/manual selection process with an intelligent algorithm that can process multiple factors simultaneously to optimize space utilization.
3Measurement precision
If machine learning models are trained with historical data, then prediction accuracy for existing items is improved, but ability to predict interactions for items without history deteriorates
Solution Approach 1:
Co-located items function as intermediaries that connect the historical data domain with the new item domain. The model learns from interactions involving co-located items and applies this knowledge to predict interactions for new items. The intermediary relationship allows the model to generalize from historical patterns to new contexts without requiring direct historical data for the target item.
Solution Approach 2:
The system creates a predictive copy or representation of item interaction patterns by analyzing co-located items. Instead of requiring actual historical interactions for each new item, the model copies the interaction behavior observed in similar items (co-located items) and applies this copied pattern to predict the new item's performance, effectively replicating successful interaction patterns.
Data Source
AI summary
An inventory interaction model predicts user interactions with items to be included in an item assortment in a warehouse. The item is described with features that include the co-located items and the respective user interactions, so that the item interactions for the evaluated item incorporate item-item effects in its predictions. To train the model effectively in the absence of prior interaction data for an item, training examples are generated from existing item and user interaction data of co-located items by selecting a portion of the items for the examples and including co-located item data, labeling the training example output with item interactions for the item. The trained model is then applied for an item assortment by describing co-located item features of the item assortment in evaluating candidate items.