Dynamic Discount for Scheduled Delivery via Batching Forecasting
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Solution Overview
Problem
Existing methods struggle to accurately forecast delivery fares for scheduled orders several days ahead, especially without real-time market signals, and fail to design effective discount mechanisms that rely heavily on forecasting accuracy.
Innovation Solution
A method that predicts delivery fares and batching rates for scheduled orders using historical data and machine learning algorithms, such as quantile regression and neural networks, to determine dynamic discounts based on predicted batching rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If delivery fares are forecasted several days ahead using historical data without real-time market signals, then scheduled delivery service is enabled, but forecasting accuracy deteriorates
Solution Approach 1:
The system performs preliminary forecasting of delivery fares using historical data and machine learning models before the actual delivery time. Multiple forecasted values are generated in advance, and confidence intervals are calculated to account for uncertainty. This allows customers to book deliveries several days ahead while the system prepares fare estimates with associated reliability metrics.
Solution Approach 2:
The system incorporates feedback mechanisms where actual delivery fares and batching outcomes are compared against forecasted values. This feedback loop enables continuous refinement of the machine learning models, improving forecasting accuracy over time while maintaining the ability to provide advance bookings.
2Adaptability or versatility
If dynamic discount mechanisms are designed based on forecasting, then customer behavior can be influenced, but system complexity increases
Solution Approach 1:
The system changes the parameter of delivery fare by applying dynamic discounts based on forecasted batching rates and confidence intervals. Discounts are adjusted as parameters according to predicted demand patterns, batching opportunities, and forecast reliability, enabling flexible customer behavior optimization without requiring complex manual intervention.
Solution Approach 2:
The discount mechanism is made dynamic by continuously updating forecasted fares and batching rates using machine learning models. The system adapts discount levels in real-time based on changing market conditions, customer behavior patterns, and predicted batching opportunities, rather than using static discount rules.
3Productivity
If discounts are provided to encourage non-peak hour orders, then driver utilization improves, but forecasting accuracy requirement increases
Solution Approach 1:
The system applies periodic discount patterns that vary by time of day and day of week, encouraging customers to shift orders to non-peak periods. By creating predictable discount cycles, the system reduces uncertainty in demand forecasting while still achieving improved driver utilization through strategic timing of promotional periods.
4Loss of energy
If order batching is optimized to improve network efficiency, then delivery cost decreases, but batching rate prediction accuracy becomes more critical
Solution Approach 1:
The system merges multiple forecasted delivery orders into batches based on predicted batching rates and spatial-temporal proximity. By combining orders that are likely to be delivered together, the system achieves cost savings through route optimization while using machine learning models to predict batching opportunities with sufficient accuracy.
Solution Approach 2:
The system segments the delivery network into different batching zones and time windows, allowing independent optimization of batching strategies for different regions and periods. This segmentation reduces the complexity of predicting batching rates across the entire network while still achieving overall cost reductions through localized batching optimization.
Data Source
AI summary
Aspects concern a method for forecasting a delivery fare and determining a batching possibility prediction-based dynamic discount for a scheduled order of a goods delivery service, the method including predicting a delivery fare for a scheduled delivery in an available time slot, and predicting a batching rate for the scheduled delivery to be batched with at least one other order in the available time slot. The method further includes determining a discount for the scheduled delivery in the available time slot, based on the predicted batching rate, determining a final delivery fare for the scheduled delivery in the available time slot, based on the predicted delivery fare and the determined discount, and displaying the determined final delivery fare for user selection of the scheduled delivery.