Dynamic Picker Task Completion Prediction and Intervention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional online concierge systems rely on fixed and static time thresholds for warning and removing pickers, which are inefficient and inflexible, failing to consider the dynamic nature of order completion and picker performance.
Innovation Solution
An online concierge system employs a trained machine-learning model to predict future completion times for order tasks and determines appropriate remedial actions based on the picker's progress, using a second model to assess the effects of interventions such as warnings or removals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed static time thresholds are used for warning and removing pickers, then the system operation is simple and rules are easy to implement, but the system becomes inefficient and inflexible, failing to adapt to dynamic order completion scenarios
Solution Approach 1:
The patent applies dynamics by replacing fixed static time thresholds with dynamic, learned time predictions from a machine learning model. The system continuously monitors picker progress and adjusts expected completion times based on historical data and patterns, allowing the system to adapt to varying order complexities, picker speeds, and time-of-day effects while maintaining automated operation.
Solution Approach 2:
The patent changes the parameter of time threshold from fixed to variable based on machine learning predictions. Instead of using constant values like 10 minutes or 30 minutes, the system learns optimal time parameters from historical order data, adjusting expected completion times dynamically based on the specific order characteristics, picker performance history, and contextual factors.
2Reliability
If conventional fixed rules are used for order monitoring, then the system is easy to operate, but it causes unnecessary picker removals and fails to provide timely interventions
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously monitors picker progress against predicted completion times and provides feedback on performance assessment accuracy. The system learns from actual order outcomes to refine its predictions, ensuring more reliable identification of pickers who need intervention versus those who will complete orders on time, thereby reducing false positives in picker removal decisions.
Solution Approach 2:
The patent applies preliminary action by using the machine learning model to predict future completion times before actual delays occur. The system proactively identifies pickers who are likely to miss deadlines based on their current progress and historical patterns, allowing for timely interventions before the problems manifest, rather than reacting after fixed thresholds are breached.
3Productivity
If static time thresholds are used, then implementation is straightforward, but the system cannot consider the effect of remedial actions on order completion
Solution Approach 1:
The patent uses feedback from historical order data to train the machine learning model to predict the effects of different interventions. The system learns which interventions (warnings, reminders, or removals) most effectively improve order completion for different picker types and situations, allowing for optimized productivity while the model handles the complexity of intervention evaluation.
Solution Approach 2:
The machine learning model performs self-service by automatically determining optimal interventions based on its analysis of historical data and current picker status. Rather than requiring manual review of each intervention case, the system autonomously identifies the most effective action to take, improving productivity while the model's training process absorbs the complexity of intervention effectiveness evaluation.
Data Source
AI summary
Embodiments are related to automatic prediction of times for completion of tasks for an order by a picker associated with an online system and determination of an appropriate intervention for the picker. The online system applies a computer model to predict a plurality of times for completion of a plurality of tasks associated with the first order. The online system determines that the picker who accepted the first order did not complete a task of the plurality of tasks at a predicted time of the plurality of times increased by a threshold time. The online system determines an intervention associated with the picker, based in part on the determination that the picker did not complete the task. The online system causes a device of the picker to display a message that corresponds to the determined intervention.