Dynamic Inventory Assortment for Subscription Electronics
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Solution Overview
Problem
Current methods for optimizing inventory assortment in online retail, rental, or subscription services lack efficiency due to manual processes and human subjectivity, which can lead to suboptimal resource allocation and inventory management.
Innovation Solution
A computer-implemented method and system for dynamically assorting merchandise by receiving and generating cells defined by item attributes, allocating resources, determining stock keeping units, assigning launch dates, and stocking inventories based on optimized resource allocation and wearability metrics, utilizing data-driven optimization models to automate the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used for inventory assortment optimization, then human subjectivity can be applied, but the speed and accuracy of the process deteriorates
Solution Approach 1:
The patent replaces manual mechanical processes with an automated computer-implemented optimization model. The system receives data, processes it through structured algorithms, and generates inventory assortment recommendations automatically, eliminating human subjectivity while improving both speed and accuracy simultaneously.
Solution Approach 2:
The optimization model operates autonomously by receiving data inputs, processing them through predefined algorithms, and generating solutions without requiring continuous human intervention. The system self-manages the inventory optimization process, eliminating the need for manual analysis while maintaining high accuracy and speed.
2Productivity
If manual inventory optimization processes are used, then flexibility in decision-making is maintained, but resource allocation efficiency deteriorates
Solution Approach 1:
The patent segments the inventory optimization problem into distinct dimensions (product categories, locations, time periods) and attributes (sales velocity, demand patterns, resource constraints). This segmentation allows the complex problem to be processed systematically through the optimization model, improving resource allocation efficiency while managing complexity through structured data organization.
3Productivity
If data-driven automated optimization models are implemented, then speed and accuracy of inventory management are improved, but the complexity of the system increases
Solution Approach 1:
The patent transforms qualitative inventory management parameters into quantitative data that can be processed by the optimization model. By converting subjective decision criteria into measurable attributes (sales velocity, demand forecasts, resource availability), the system achieves high speed and accuracy while managing complexity through standardized parameter definitions and data structures.
Data Source
AI summary
Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically assorting merchandise. For example, a method may include receiving or generating a plurality of cells defined by one or more dimensions, receiving a total number of resources for allocation to the plurality of cells, allocating the total number of resources among the plurality of cells, determining one or more stock keeping units for each cell, determining a quantity of each of the one or more stock keeping units in each cell, assigning one or more launch dates for each of the one or more stock keeping units for each cell, and stocking the one or more inventories with one or more articles corresponding to the stock keeping units in each cell, based on the determined quantity and the assigned one or more launch dates of each of the stock keeping units in each cell.