ML Model for Unclaimed Pickup Order Disposal Decisions
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Solution Overview
Problem
Existing systems fail to efficiently handle unclaimed pickup orders, particularly in grocery stores, where existing systems do not provide a solution for handling unclaimed pickup orders, where existing systems do not provide a solution for handling unclaimed pickup orders.
Innovation Solution
A system utilizing a trained machine-learning model is utilized to identify the handling of unclaimed pickup orders, particularly in grocery stores, where a trained machine-learning model is used to handle unclaimed pickup orders by identifying the preferred method for disposal of each bundle of items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual handling of unclaimed pickup orders is used, then personnel can make disposal decisions, but the process is inefficient and time-consuming
Solution Approach 1:
The patent replaces manual mechanical decision-making with an automated machine learning system. The ML model analyzes order data, item characteristics, and store policies to automatically generate disposal recommendations, eliminating the need for manual review and significantly improving handling efficiency while reducing time loss.
Solution Approach 2:
The system enables self-service disposal decision-making through the ML model that autonomously evaluates unclaimed orders and generates disposal recommendations without requiring human intervention. The system serves itself by automatically processing disposal decisions based on pre-established criteria and learned patterns from training data.
2Productivity
If automated disposal methods are implemented, then handling efficiency improves, but the system lacks the ability to make nuanced disposal decisions
Solution Approach 1:
The ML model incorporates multiple adjustable parameters including item category, age, value, store policy preferences, and environmental factors to make nuanced disposal decisions. By varying these parameters, the system adapts its recommendations to different scenarios while maintaining automated efficiency, resolving the contradiction between automation and flexibility.
Solution Approach 2:
The system dynamically adjusts disposal recommendations based on real-time inputs and changing conditions. The ML model can modify its decision-making behavior according to item characteristics, store policies, and environmental factors, providing adaptable and versatile disposal decisions while maintaining automated handling efficiency.
3Volume of stationary object
If all unclaimed orders are disposed of immediately, then storage space is freed, but potential recoverable items are lost
Solution Approach 1:
The system applies different disposal recommendations to different items within unclaimed orders based on their specific characteristics. High-value or recoverable items are identified and flagged for potential recovery, while other items are disposed of to free storage space. This localized, item-specific approach resolves the contradiction between freeing space and preserving recoverable items.
Solution Approach 2:
The ML model distinguishes between items suitable for discarding and those suitable for recovery. It generates differentiated disposal recommendations that prioritize recovery of valuable or recoverable items while efficiently disposing of non-recoverable items, thereby optimizing both storage space utilization and item recovery potential.
Data Source
AI summary
An online system uses a trained model for intelligent handling of unclaimed online pickup orders. After identifying that an order placed by a user of the online system is unclaimed at a location of a source, the online system obtains, from a device of a picker associated with the online system and/or a device associated with the source, signals with information about each item in each bundle of the unclaimed order. The online system applies the trained model to identify, based on the obtained signals, a preferred method for disposal of each bundle. Based on the identified preferred method for disposal of each bundle, the online system generates a disposal decision signal and communicates the disposal decision signal to the device associated with the source that prompts personnel at the location of the source to dispose each bundle of the unclaimed order using the identified preferred disposal method.