Delivery Date Prediction Using Clustering and Regression Models
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Solution Overview
Problem
Existing delivery date scheduling systems are labor-intensive, costly, and prone to inaccuracy due to reliance on user experience and communication, lacking a reliable method to assess the accuracy of scheduled delivery dates.
Innovation Solution
A multi-step predictive analytics solution that uses a system comprising an application server, data store, and client system to perform clustering, classification, and regression modeling to determine the reliability of scheduled delivery dates, providing an estimated window for delivery when inaccuracy is predicted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing delivery date scheduling systems rely on user experience and communication, then delivery dates can be scheduled, but the process is labor-intensive, costly, and prone to inaccuracy
Solution Approach 1:
The patent replaces manual user experience-based scheduling with an automated machine learning system that uses historical data, communication logs, and product information to predict delivery dates. This substitution of mechanical human judgment with automated computational analysis resolves the contradiction by providing accurate predictions without the labor-intensive and error-prone manual process.
Solution Approach 2:
The system creates a predictive model that copies and learns from historical delivery patterns, communication data, and product characteristics. By training machine learning algorithms on past performance data, the system replicates successful delivery scheduling patterns while eliminating the need for manual expert judgment in each new case.
2Reliability
If buffer windows are added to delivery dates to account for uncertainty, then reliability is improved, but productivity and efficiency are reduced due to extended time intervals
Solution Approach 1:
The patent dynamically adjusts delivery date parameters based on learned patterns from historical data. Instead of using fixed buffer windows, the system modifies delivery date predictions based on product-specific factors, communication patterns, and historical performance, thereby reducing unnecessary time buffers while maintaining reliability.
Solution Approach 2:
The system applies partial buffering by providing confidence intervals or probability distributions around delivery dates rather than adding excessive fixed time buffers. This allows stakeholders to understand the likelihood of on-time delivery without automatically extending all delivery schedules by large margins.
3Loss of time
If manual scheduling processes are used with personal experience and product knowledge, then delivery dates can be provided, but the process is costly and labor-intensive
Solution Approach 1:
The system enables self-service delivery date scheduling by automatically analyzing historical data, product information, and communication patterns to generate predictions without requiring manual intervention. The machine learning model serves itself by continuously learning from new data and improving its predictions autonomously.
Solution Approach 2:
The system performs preliminary analysis of historical delivery data, communication patterns, and product characteristics during off-peak times to pre-compute predictive models. This preliminary processing enables rapid delivery date predictions when needed without consuming operational time resources.
Data Source
AI summary
A method includes receiving a plurality of items, grouping the plurality of items into a plurality of clusters, where each of the plurality of clusters comprises items having similar features to one another, applying a classification model to each cluster to predict whether each item of a cluster will be delivered on time or delivered late, applying a regression model that determines an expected measure of tardiness of each item predicted to be delivered late, and outputting a delivery date prediction for each item predicted to be delivered late based on the expected measure of tardiness of the item.