Warehouse Space Recommendation Using Sales-Zone Prediction
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Solution Overview
Problem
Individuals and smaller entities lack access to warehouse space and are unaware of how much space they need or where to warehouse their inventory to be closer to buyers.
Innovation Solution
A machine learning model is trained using transaction histories to determine the probability of items selling in different locations, recommending transaction zones and warehouse spaces for inventory placement based on item attributes and sales probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large third-party vendors make warehouse space available to other large entities, then warehouse space utilization is improved, but access for individuals and smaller entities deteriorates
Solution Approach 1:
The patent segments warehouse space into different tiers or categories (e.g., premium, standard, economy) with varying features, prices, and accessibility levels. This allows the system to serve multiple customer types simultaneously - large entities can access premium spaces while individuals and smaller entities can access standard or economy spaces, thereby resolving the contradiction between maximizing space utilization and providing broad access.
Solution Approach 2:
The patent creates a universal warehousing platform that serves multiple functions: it accommodates large third-party vendors, enables individuals to warehouse items, provides recommendation services, and facilitates transactions between various user types. This multi-functional system allows warehouse space to be utilized by diverse entities simultaneously, resolving the contradiction between optimized utilization and broad accessibility.
2Speed
If individuals and smaller entities want to warehouse inventory closer to buyers, then delivery speed is improved, but determination of appropriate warehouse location and space requirement becomes more complex
Solution Approach 1:
The patent implements an automated recommendation system that performs self-service functions: it automatically analyzes user input (item categories, quantities, buyer locations), queries the database for suitable warehouse spaces, calculates space requirements, and generates location recommendations without requiring users to manually determine optimal warehouse locations. This resolves the contradiction by automating the complex determination process while enabling faster delivery positioning.
Solution Approach 2:
The patent replaces the manual mechanical process of determining warehouse locations and space requirements with an automated information processing system. The system uses database queries, algorithmic calculations, and automated recommendation generation to substitute for manual planning and analysis, thereby reducing complexity while improving delivery speed optimization.
3Adaptability or versatility
If warehouse spaces are made available to individuals and smaller entities, then accessibility is improved, but determination of appropriate space requirements becomes more difficult
Solution Approach 1:
The system provides self-service functionality where users simply input their item categories and quantities, and the system automatically determines appropriate space requirements by querying the database and performing calculations. This eliminates the need for users to manually assess their space needs, making the service accessible to individuals and smaller entities while accurately determining space requirements.
Solution Approach 2:
The patent introduces an intermediary recommendation system that mediates between user requests and warehouse space allocation. This intermediary automatically analyzes user input, determines space requirements based on item characteristics and quantities, and matches users with appropriate warehouse spaces, thereby simplifying the process for individuals and smaller entities while ensuring accurate space determination.
Data Source
AI summary
Systems and methods for providing warehousing services that utilize a machine learning model are provided. The system trains a machine learning model with training data comprising item attributes and transaction locations extracted from past transactions for each item category to identify a plurality of transaction zones where each item category has a highest probability for selling. Subsequently, the system receives a warehouse request to warehouse inventory in a remote location. At least one transaction zone is determined based on item attributes of the inventory by applying the trained machine learning model. Based on the determined at least one transaction zone, the system determines one or more warehouse spaces that satisfy a spacing requirement for the inventory and causes presentation of the warehouse recommendation. The warehouse recommendation can indicate the one or more warehouse spaces.