Machine-Learning Alerts for Spoilage Risk During Item Picking
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Solution Overview
Problem
Conventional online concierge systems lack mechanisms to predict potential customer complaints for items with expiration dates or spoilage, leading to negative customer experiences and appeasements.
Innovation Solution
Implementing machine-learned predictive models to determine prediction values for items, which are used to generate alerts for pickers based on comparison with threshold values, ensuring timely intervention to mitigate potential complaints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional online concierge systems are used without predictive models, then the system complexity remains low, but customer complaint rate increases due to inability to predict quality issues
Solution Approach 1:
The patent applies preliminary action by training machine-learned predictive models in advance using historical data about item quality and customer complaints. These pre-trained models then predict potential quality issues for items before they are picked, allowing the system to proactively identify at-risk items and alert pickers to exercise extra care, thereby reducing customer complaints without requiring complex real-time decision-making during the picking process
2Reliability
If machine-learned predictive models are implemented to predict customer complaints, then customer complaint rate decreases, but computational resources and processing time increase
Solution Approach 1:
The predictive models are trained in advance using historical data, and the trained models are deployed to make predictions during the picking process. This preliminary training approach allows the system to use pre-computed model parameters rather than performing complex computations in real-time during item selection, reducing computational resource consumption while maintaining prediction accuracy
Solution Approach 2:
The patent replaces complex real-time computational analysis with pre-trained machine learning models that make predictions through simplified inference processes. The models substitute for what would otherwise require complex real-time data processing and analysis, reducing computational burden during the actual picking operation while maintaining high prediction accuracy for identifying at-risk items
3Reliability
If alerts are provided to pickers for all items, then quality control improves, but picker workload and time increase
Solution Approach 1:
The system applies local quality by providing targeted alerts only to pickers for specific items that the predictive models identify as having high risk of quality issues. Rather than treating all items uniformly, the system dynamically determines which items require special attention based on their individual characteristics and predicted risk levels, allowing pickers to focus their extra attention only where most needed while maintaining efficient overall processing
Solution Approach 2:
The system changes the parameter of alert provision from a static approach (alerting all items or no items) to a dynamic approach where alert thresholds and priorities are adjusted based on predicted risk parameters. The machine-learned models continuously refine their predictions based on input data about item characteristics, and the system adjusts alerting behavior accordingly, optimizing the balance between quality control and picker efficiency
Data Source
AI summary
A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.