Item Placement Optimization Using Utility Scores
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Solution Overview
Problem
Traditional physical stores face challenges in optimizing inventory placement and assortment to maximize revenue and minimize costs, as they rely on manual methods that do not account for user preferences and space utilization effectively.
Innovation Solution
A system that analyzes transaction and item data using machine-learned models to determine utility scores for items based on user preferences and available space, optimizing item placement and quantities to maximize revenue and reduce restocking costs by using artificial neural networks and classifiers to recommend optimal assortments and locations within facilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used for inventory placement and assortment selection, then implementation simplicity is maintained, but revenue optimization and space utilization are insufficient
Solution Approach 1:
The patent replaces manual inventory placement methods with an automated computer-based system that uses machine learning models and algorithms to analyze transaction data, determine utility scores, and optimize item assortments and placements, thereby substituting human manual operations with automated computational processes
Solution Approach 2:
The system enables self-service optimization by automatically analyzing transaction data, generating utility scores for items, determining optimal assortments, and recommending placements without requiring manual intervention, allowing the inventory system to optimize itself autonomously
2Adaptability or versatility
If traditional sales-based item selection is used, then simplicity is maintained, but user preferences and customer satisfaction are not effectively accounted for
Solution Approach 1:
The patent replaces traditional sales-based item selection with a machine learning-based system that analyzes transaction data to determine utility scores reflecting user preferences, substituting simple sales counting with sophisticated preference analysis algorithms
Solution Approach 2:
The system incorporates feedback from transaction data to continuously learn and update utility scores for items based on actual customer purchasing behavior, enabling the system to adapt to changing user preferences over time through iterative learning
3Productivity
If inventory locations are not optimized based on utility scores, then placement simplicity is maintained, but restocking costs and inefficiency increase
Solution Approach 1:
The patent replaces intuitive or manual inventory placement with an automated optimization system that calculates utility scores for items and recommends specific placements at inventory locations, substituting human judgment with data-driven algorithmic optimization
Solution Approach 2:
The system performs preliminary analysis of transaction data and utility score calculation before making placement recommendations, allowing the optimization to be prepared in advance and enabling proactive inventory management rather than reactive adjustments
Data Source
AI summary
This disclosure describes, in part, systems for recommending items and item placements within a facility. For instance, the system may receive transaction data and inventory data associated with at least the facility. The system may then use the transaction data and the inventory data to determine utility scores for items at the facility. Additionally, the system may receive data representing dimensions of the items and/or data representing dimensions of available space associated with the facility. The system may then use the utility scores, the dimensions of the items, and/or the dimensions of the available space to determine the amounts of space to provide to the items at the facility. Furthermore, the system can generate data representing an assortment of the items, where the assortment indicates at least the amounts of space, and provide the data to one or more computing devices.