Fulfillment Program Surplus Prediction
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Solution Overview
Problem
Supply chain disruptions, such as poor weather or raw material shortages, can lead to items being unavailable for purchase, necessitating an approach to identify users with surplus items who can provide them to those with immediate needs, particularly in common geographic areas for smoother transactions.
Innovation Solution
A computer-implemented method that identifies users predicted to have a surplus of an item by analyzing consumption patterns and geographic proximity, facilitating transactions between buyers and sellers through a fulfillment program that coordinates the transfer of out-of-stock items using the surplus from these users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system identifies users with surplus items to fulfill orders during supply chain disruptions, then order fulfillment reliability is improved, but the complexity of identifying and coordinating with individual users increases
Solution Approach 1:
The patent introduces a fulfillment program as an intermediary system that mediates between retailers and end consumers. This program identifies users with surplus items, coordinates transfers, and manages the fulfillment process, thereby resolving the contradiction by adding a coordinating layer that improves reliability while managing complexity through automation rather than direct peer-to-peer coordination
2Ease of operation
If the system selects users based on geographic proximity to buyer location, then transaction smoothness and delivery efficiency are improved, but the complexity of location-based filtering and selection increases
Solution Approach 1:
The patent applies parameter changes by using geographic location as a key selection criterion. The fulfillment program filters potential sellers based on their proximity to the buyer's location, transforming a complex multi-factor selection problem into a more manageable location-based filtering process that improves transaction smoothness while controlling complexity through focused parameter optimization
3Measurement precision
If the system predicts user surplus quantities by analyzing consumption patterns, then accuracy in identifying suitable sellers is improved, but the complexity of data analysis and prediction increases
Solution Approach 1:
The patent implements feedback mechanisms where the fulfillment program continuously monitors consumption patterns, transfer histories, and inventory levels of participating users. This feedback loop enables the system to refine its surplus predictions over time, improving measurement precision while managing analysis complexity through iterative learning and pattern recognition rather than requiring perfectly accurate initial predictions
Data Source
AI summary
In an approach, a processor receives a request to purchase an item from a retailer, where the request includes a location. A processor receives a request to purchase an item from a retailer, where the request includes location information associated with a buyer. A processor identifies that the item is unavailable from the retailer. A processor determines a plurality of users predicted to have a surplus quantity of the item beyond each respective user's estimated needs at a time of the request. A processor sends an offer to the plurality of users to transfer the item in accordance with the request. A processor, responsive to receiving an acceptance of the offer, selects at least one accepting user, of the plurality of users, based on geographic proximity to the location information. A processor coordinates fulfillment of the request by the at least one user.