Product Affinity Visualization for Inventory Co-location
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Solution Overview
Problem
Conventional inventory placement techniques do not consider product affinities, leading to increased shipping costs due to products being ordered together being located in different warehouses, necessitating order splitting or consolidation.
Innovation Solution
A computer-implemented method and system that utilizes shopping data to determine product affinities and generate a product graph for visualizing and recommending co-location of high-velocity SKUs based on online shopping basket associations, thereby identifying which items should be co-located for optimal efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If inventory is placed according to estimate of future sales, then inventory placement is simple, but product affinities are not considered leading to increased shipping costs
Solution Approach 1:
The system performs preliminary analysis of shopping basket data to identify product affinities and generate co-location recommendations before inventory placement decisions are made. This allows inventory to be strategically positioned in advance to prevent order splitting, thereby reducing shipping costs while maintaining simple placement procedures.
2Loss of energy
If products with high affinity are co-located, then shipping costs are reduced, but inventory management complexity increases
Solution Approach 1:
The system introduces an intermediary visualization interface that displays product affinity graphs and co-location recommendations. This intermediary layer presents complex affinity analysis results in an intuitive visual format, allowing inventory managers to make informed decisions without directly managing the underlying complexity of affinity calculations and data processing.
3Productivity
If order splitting is avoided through co-location, then shipping efficiency improves, but product graph analysis complexity increases
Solution Approach 1:
The visualization interface uses color-coded indicators to represent different levels of product affinity and co-location recommendations. High-affinity products are highlighted with distinct colors, enabling inventory managers to quickly identify which products should be co-located to prevent order splitting, thereby improving fulfillment efficiency without requiring deep analysis of the underlying product graph complexity.
Data Source
AI summary
The present disclosure relates generally to the field of product supply networks (e.g., for order fulfillment and inventory control). In one specific example, mechanisms are provided for presenting visualizations to aid in co-locating two or more products in the same location (e.g., at a common order fulfillment facility) based upon associations between the products. In various embodiments, systems, methods and computer program products are provided.