Delivery Confidence Interval Prediction Using Deep Neural Networks
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Solution Overview
Problem
Conventional methods for estimating delivery times often result in inefficient use of information and increased costs due to inaccurate or overly broad delivery windows, leading to missed deliveries and increased expenses for both delivery companies and consumers.
Innovation Solution
The system uses machine learning techniques, specifically deep neural network regression analysis, to determine delivery confidence intervals by normalizing and categorizing delivery information associated with predefined geographic areas like Zip8 or Zip9 codes, providing more accurate delivery windows and reducing the likelihood of packages being stolen or returned.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to estimate delivery times, then the process is simple and fast, but the accuracy of delivery windows is poor leading to missed deliveries
Solution Approach 1:
The patent segments delivery time estimation into multiple confidence intervals (e.g., 50%, 75%, 90% confidence levels) rather than providing a single estimate. This segmentation allows the system to offer progressively more accurate predictions while maintaining computational efficiency, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The system performs preliminary data collection and normalization of historical delivery information before actual delivery time estimation. By pre-processing and organizing delivery data, location data, and package characteristics in advance, the system reduces computational complexity during real-time estimation while improving accuracy through comprehensive data analysis.
2Reliability
If broad delivery windows are used to ensure accuracy, then missed deliveries decrease, but delivery efficiency and cost increase
Solution Approach 1:
The patent implements dynamic delivery window adjustment based on multiple confidence intervals. Instead of using fixed broad windows, the system dynamically determines appropriate time windows based on the selected confidence level, package characteristics, and historical data. This allows the system to maintain high reliability for critical deliveries while improving efficiency for standard deliveries, resolving the contradiction between reliability and productivity.
3Measurement precision
If accurate delivery windows are provided, then customer satisfaction improves, but computational resources and processing time increase
Solution Approach 1:
The system implements a tiered approach where basic delivery time estimates are generated quickly using simplified models, while more accurate confidence interval calculations are performed selectively based on customer requirements and delivery criticality. This partial application of complex analysis reduces processing time while maintaining accuracy where needed, resolving the contradiction between precision and time loss.
Data Source
AI summary
Embodiments are disclosed for determining delivery confidence intervals. An example method for determining a confidence interval includes the following operations. Delivery information is received from one or more sources, wherein the delivery information comprises data associated with at least one predefined location perimeter. The data associated with the at least one predefined location perimeter is normalized. The normalized data is categorized into training data used to perform a deep neural network regression analysis. A predicted delivery confidence interval is determined by constructing a predictive learning model by conducting a regression of the data using deep neural network regression. The predicted delivery confidence interval is stored in a results table in association with the predefined location perimeter. And, upon receiving a request from a visibility management system, accessing the results table to provide predicted delivery windows to consignees.