Integrated Inventory Placement and Demand Allocation Across Fulfillment Nodes

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Solution Overview

Problem

Existing inventory placement and demand allocation systems in retail fulfillment networks consider space constraints only, neglecting other critical factors such as demand allocation, geographic demand patterns, shipping costs, and customer experience, leading to inefficient inventory management.

Innovation Solution

A system utilizing machine learning and mathematical optimization to recommend inventory placement and demand allocation simultaneously, considering geographic demand patterns, shipping costs, storage capacity, and customer experience, while managing operational constraints and delivery speed elasticity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If existing inventory placement systems consider only space constraints, then the system complexity is low, but the inventory management efficiency is poor

Engineering Contradiction:

Improveinventory management efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent combines inventory placement and demand allocation into a single integrated system. The machine learning model simultaneously optimizes both inventory placement decisions and demand allocation strategies across multiple fulfillment nodes, considering multiple objectives including customer experience, shipping costs, and inventory efficiency together rather than separately

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system performs multiple functions through a unified machine learning model that handles inventory placement, demand allocation, and multi-objective optimization simultaneously. The model considers diverse factors including geographic demand patterns, shipping costs, storage capacity, and customer experience metrics to provide comprehensive inventory management solutions

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Productivity

If inventory placement considers multiple factors like demand allocation and geographic patterns, then the inventory management efficiency improves, but the system complexity increases

Engineering Contradiction:

Improveinventory management efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical or rule-based inventory placement systems with a machine learning model. This computational approach automatically processes multiple factors including geographic demand patterns, shipping costs, and storage capacity to generate optimized inventory placement and demand allocation decisions without manual intervention

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the approach from considering single parameters (space constraints) to optimizing multiple parameters simultaneously. The machine learning model evaluates and balances multiple objectives including customer experience metrics, shipping costs, inventory turnover, and fulfillment node capacity to determine optimal inventory placement and demand allocation strategies

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If demand allocation is neglected, then the system is simpler to operate, but the customer experience deteriorates

Engineering Contradiction:

Improvesystem ease of operationVSAvoidcustomer experience

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The machine learning model automatically performs demand allocation without requiring manual operational intervention. The system self-determines optimal demand allocation strategies by analyzing geographic demand patterns, customer preferences, and fulfillment node capabilities, then implements these allocations automatically across the network

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250245620A1Systems and methods for inventory placement and demand allocation

Publication Date: 2025.07.31 WALMART APOLLO LLC

AI summary

Systems and methods for inventory placement and demand allocation of items in a retail fulfillment network are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a recommendation request regarding a plurality of nodes in a retail fulfillment network; obtaining feature data based on the recommendation request; computing, using at least one machine learning model, recommendation data based on the feature data, wherein the recommendation data indicates at least one recommendation regarding inventory placement and demand allocation among the nodes in the retail fulfillment network; and transmitting, in response to the recommendation request, the recommendation data to the computing device.