Inventory Allocation Optimization via Desirability Prediction
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Solution Overview
Problem
Existing product suggestion systems for clothing and fashion-related products often fail to consider global inventory impact, leading to suboptimal choices for customers as items allocated to one customer become unavailable to others, resulting in poor selection options for the remaining group.
Innovation Solution
A global optimization technique for inventory allocation that uses desirability prediction values and adjustment approximation values, evaluated through machine learning models, to optimize product suggestions across all customers, taking into account current and future inventory states, customer preferences, and global constraints such as quality metrics and variety constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If product suggestions are optimized locally for individual customers, then each customer receives personalized recommendations, but the global inventory allocation becomes suboptimal and remaining customers have poor product choices
Solution Approach 1:
The patent segments the inventory allocation problem into customer-specific subproblems while maintaining global coordination through iterative optimization. Each customer's product suggestions are optimized individually based on their preferences and the current inventory state, then the solution is refined across the entire customer group to achieve global optimality.
Solution Approach 2:
The patent implements a feedback mechanism where the system evaluates the impact of allocating specific products to individual customers on the overall inventory availability for other customers. This feedback loop allows the system to adjust allocations iteratively, ensuring that local personalization decisions contribute to global optimization rather than degrading it.
2Reliability
If inventory is allocated to maximize individual customer satisfaction, then each customer receives their preferred products, but computational resources increase and the system becomes less efficient
Solution Approach 1:
The patent performs preliminary actions by pre-calculating customer preferences, product attributes, and initial inventory states before the optimization process begins. This preprocessing step organizes data in a way that reduces the computational burden during the actual optimization phase, allowing the system to handle complex multi-customer scenarios efficiently.
Solution Approach 2:
The patent applies partial optimization by focusing computational efforts on the most critical allocation decisions rather than exhaustively optimizing every possible customer-product pair. The system identifies and prioritizes key allocation choices that have the greatest impact on global satisfaction, reducing overall computational complexity while maintaining solution quality.
Data Source
AI summary
An article type adjustment approximation value for each physical article type of a plurality of physical article types of limited quantities is predetermined. A determination is made to assign to a selected client among a set of clients, a set of physical article types among the plurality of physical article types. A group of eligible physical article types for the selected client among the plurality of physical article types is identified. Using one or more machine learning prediction models, a desirability prediction value for each physical article type in the group of eligible physical article types is determined. The desirability prediction values are adjusted using the corresponding predetermined article type adjustment approximation value and a corresponding predetermined client adjustment approximation value to determine corresponding adjusted prediction values. The corresponding adjusted prediction values are used to determine the set of physical article types to be assigned to the selected client.