Machine Learning Delivery Time Estimation
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Solution Overview
Problem
Current delivery lead time estimation processes in supply chains are inaccurate and rely on manual, rule-based methods that fail to incorporate complex variables and historical data, leading to inconsistent and often overly long estimated delivery times, which can reduce customer trust and loyalty.
Innovation Solution
A method that calculates similarity-based and proximity-based features using historical order data, clustering, and machine learning models to provide more accurate delivery time estimates, incorporating recency and geographic factors through engineered features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual rule-based methods are used for delivery lead time estimation, then the process is simple to implement, but the accuracy of delivery time estimates deteriorates
Solution Approach 1:
The patent replaces manual rule-based estimation methods with a machine learning-based automated system. The machine learning engine processes historical order data, geographic information, and product characteristics to generate delivery time estimates, substituting human analysts and straightforward rules with an automated computational system that achieves higher accuracy while maintaining ease of implementation through standardized data inputs.
2Ease of operation
If straightforward rules are used for delivery lead time estimation, then the method is easy to operate, but the reliability of estimates deteriorates
Solution Approach 1:
The patent replaces straightforward rule-based operations with a machine learning engine that automatically processes multiple variables including historical delivery data, geographic distance, product type, and order characteristics. This substitution maintains ease of operation through automated processing while significantly improving reliability by incorporating complex relationships between variables that straightforward rules cannot capture.
Solution Approach 2:
The patent transforms the estimation approach by changing from fixed rule-based parameters to dynamic machine learning-derived parameters. The system uses historical data to learn optimal delivery time parameters for different order types, geographic regions, and product categories, allowing the estimation model to adapt to changing conditions and improve reliability over time while remaining easy to operate.
3Use of energy by moving object
If manual analyst methods are used for delivery lead time estimation, then the process requires minimal computational resources, but the precision of estimates deteriorates
Solution Approach 1:
The patent performs preliminary processing of historical order data to create structured datasets that capture key delivery patterns, geographic information, and product characteristics. This pre-processing step organizes data in a way that enables the machine learning engine to efficiently generate precise estimates with minimal computational resources during actual order processing, as the heavy lifting of pattern recognition has already been accomplished during data preparation.
Data Source
AI summary
Techniques are provided for estimating a delivery time for a product in a supply chain. One method comprises obtaining an order for at least one product; calculating a similarity-based feature and/or a proximity-based feature for the order; and applying the calculated similarity-based feature and/or the calculated proximity-based feature for the order to a machine learning engine that generates an estimated delivery time for the order, wherein the machine learning engine is trained using characteristics from historical orders. The similarity-based feature for the order can be calculated using a delivery time value of historical orders in a given order cluster where the order was assigned based on a predefined distance metric between the order and features of each order cluster. The proximity-based feature for the order can be calculated using a delivery time value of the historical orders that satisfy one or more predefined recency criteria.