Machine Learning Shipping Rule Determination
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Solution Overview
Problem
Current methods for selecting packaging and shipping options for orders are inefficient, leading to wasted time and resources, as well as errors in choosing the most cost-effective options, due to the need for manual determination of suitable packaging and shipping methods for each order.
Innovation Solution
A computer-implemented method using machine learning to analyze previous orders and determine the smallest available package size and additional suitable sizes for shipping, along with calculating shipping prices for each option, to automate the selection of packaging and shipping methods based on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual determination of packaging and shipping methods is used, then employees can make decisions based on experience, but it leads to wasted time and resources as well as errors in choosing cost-effective options
Solution Approach 1:
The system pre-calculates and stores optimal packaging and shipping methods for different order configurations based on historical data. When an order is placed, the system retrieves pre-determined shipping rules rather than calculating them in real-time, significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The system automatically determines packaging size and shipping method selection without requiring manual employee intervention. The automated system uses machine learning models to self-select the most cost-effective options, eliminating human error and freeing employees from repetitive decision-making tasks.
2Ease of manufacture
If multiple package sizes are evaluated to find the smallest suitable package, then cost-effective shipping options are identified, but the process becomes more complex
Solution Approach 1:
The system creates virtual models of different package sizes and their capacity characteristics. Instead of physically testing each package size, the system uses digital twins to simulate and evaluate which packages can accommodate specific order contents, simplifying the evaluation process while maintaining accuracy.
Solution Approach 2:
The system dynamically adjusts evaluation parameters based on order characteristics. Rather than using a fixed complex algorithm for all orders, the machine learning model adapts the complexity of the evaluation process to match the specific needs of each order, simplifying common cases while handling complex scenarios when necessary.
Data Source
AI summary
Systems, methods, and devices for determining shipping rules and shipping methods for an order are disclosed herein. A computer implemented method includes receiving an electronic record of an order placed with a merchant. The method further includes determining a smallest available package size in which the order content can be shipped and one or more additional available package sizes having dimensions larger than the smallest available package size in which the order content can be shipped. The method also includes determining a shipping price to ship the order content and presenting a plurality of shipping methods to a user with the shipping price.