Door-step Time Prediction Using Speed Profiles
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Solution Overview
Problem
Conventional transportation logistics systems inaccurately determine door-step time (DST) for last mile deliveries, leading to incorrect route configurations, resource underutilization, stress on delivery associates, and potential spoilage of perishable goods, resulting in lost business opportunities and revenue loss.
Innovation Solution
A system and method that generate a speed profile for delivery locations using location data, apply a machine learning model to predict DST, and optimize delivery routes based on these predictions, incorporating features such as delivery location characteristics and timestamps to improve route planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use fixed or estimated door-step time values for route planning, then route configuration is simple, but delivery accuracy and reliability deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting location data during actual deliveries and generating speed profiles in advance. Historical delivery data is processed to create predictive models that estimate door-step time before route planning occurs, enabling accurate time allocation without real-time complexity
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting actual delivery location data and comparing it with predicted times. This feedback loop refines the speed profiles and machine learning models over time, improving door-step time prediction accuracy while maintaining systematic operation
2Reliability
If door-step time is overestimated, then delivery reliability improves, but resource utilization deteriorates
Solution Approach 1:
The system dynamically adjusts door-step time parameters based on specific delivery location characteristics, vehicle types, and historical performance data. Instead of using fixed conservative estimates, the system optimizes time allocation for each delivery scenario, improving both reliability and resource utilization simultaneously
3Productivity
If door-step time is underestimated, then resource utilization improves, but delivery reliability deteriorates
Solution Approach 1:
The system performs preliminary analysis of delivery location characteristics and generates accurate door-step time estimates before deliveries occur. This allows delivery associates to plan their routes efficiently without needing excessive buffer time, improving productivity while maintaining reliability through accurate predictions
4Productivity
If accurate door-step time prediction is implemented, then delivery route optimization improves, but computational complexity increases
Solution Approach 1:
The system segments the delivery process into distinct phases (transit time, door-step time, loading/unloading) and applies different computational approaches to each. Machine learning models predict only the door-step time component based on location features, while route optimization uses these predictions alongside standard routing algorithms, dividing computational complexity into manageable segments
Data Source
AI summary
An approach generates a speed profile for one or more delivery locations. The speed profile corresponds to location data of a delivery order for a respective delivery location, and indicates a plurality of events associated with a movement of a delivery order to the respective delivery location. The approach generates, based on the speed profile and location data corresponding to the delivery order, feature data of the respective delivery location. The approach applies a machine learning model to the generated feature data to output a door-step time prediction for the respective delivery location. The door-step time prediction is based on a time difference between timestamps of two events of the plurality of events associated with the movement of the delivery order. The approach generates a planned delivery route for the one or more delivery locations, based on the one or more delivery locations and respective the door-step time predictions.