Dynamic Delivery Compensation Using Predicted Order Lateness
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Solution Overview
Problem
Existing online concierge systems face challenges in predicting order lateness and setting optimal compensation values, leading to mispricing that can result in delayed deliveries and negative customer experiences.
Innovation Solution
A machine learning model is employed to dynamically predict order lateness and adjust compensation values based on various factors, including shopper effort and market conditions, to optimize the likelihood of timely order fulfillment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the compensation value is increased to reduce order lateness, then the likelihood of timely delivery is improved, but the operational cost increases
Solution Approach 1:
The system dynamically adjusts the compensation value parameter based on predicted lateness and uplift calculations, rather than using a fixed or manually set value. This allows optimization of the compensation-timeliness relationship while minimizing unnecessary cost expenditure.
Solution Approach 2:
The system uses a feedback loop where the machine learning model predicts lateness, calculates uplift for different compensation scenarios, and adjusts compensation values accordingly. This closed-loop approach ensures compensation is optimized based on actual system performance and predictions.
2Loss of energy
If the compensation value is set too low to reduce operational cost, then the operational cost is reduced, but the likelihood of order lateness increases
Solution Approach 1:
The compensation value is dynamically adjusted based on real-time predictions and calculations, allowing the system to use the minimum necessary compensation to achieve timely delivery, thereby reducing unnecessary operational costs.
Solution Approach 2:
The system calculates the precise uplift needed to achieve timely delivery rather than applying excessive compensation. By determining the minimum effective boost amount, the system avoids over-compensating while still achieving the reliability goal.
3Device complexity
If a fixed compensation value is used for all orders, then the system complexity is reduced, but the ability to optimize delivery timeliness across different order scenarios is worsened
Solution Approach 1:
The system transitions from a static fixed compensation value to a dynamic compensation determination process. The compensation value changes based on order-specific predictions, fulfillment agent characteristics, and calculated uplift, allowing optimization for each unique scenario.
Solution Approach 2:
Instead of applying a uniform compensation value across all orders, the system determines customized compensation values for each order based on local conditions such as specific order characteristics, predicted lateness, and fulfillment agent behavior patterns.
Data Source
AI summary
A system receives an order for fulfillment from a customer device, the order associated with a delivery time. The system determines a base compensation value for the order and sends the order and base compensation value to devices of one or more fulfillment agents. If the order is not accepted within a predetermined time, the system applies a trained machine learning model to updated input features of the order and the fulfillment agents to predict an amount of lateness time past the delivery time. Based on the predicted amount of lateness time, the system determines an updated lateness value, determines an updated compensation value, and sends the order with the updated compensation value to the fulfillment agents. The system repeats prediction, lateness value determination, and compensation adjustment until the order is accepted.