Packing Optimization Using Carrier Rate Tables
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Solution Overview
Problem
Existing packing and cartonization methods fail to achieve a cost-optimal shipping solution as they primarily focus on volume utilization and do not consider a wide range of cost factors, leading to high shipping cost estimates and increased shopping cart abandonment in e-commerce.
Innovation Solution
A method and system that optimize packing by ranking products and containers based on weight and dimension information, using rate tables from identified shipping carriers, and determining layout configurations to minimize shipping costs, while providing a visual or machine-readable packing instruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If existing packing methods focus on volume utilization to minimize number of boxes, then the number of cartons is reduced, but shipping cost is not optimized because cost factors beyond dimensions are ignored
Solution Approach 1:
The system changes the optimization parameters from purely dimensional (volume, length, width, height) to include multiple cost-related parameters such as shipping rates, weight, dimensional weight, and carrier-specific pricing factors. This allows the packing solution to be evaluated based on total shipping cost rather than just volume efficiency.
Solution Approach 2:
The invention adds a new dimension to the packing optimization problem by incorporating cost as a separate optimization objective alongside traditional dimensional constraints. This transforms the problem from a purely geometric optimization to a multi-objective optimization that includes economic factors.
2Productivity
If shipping cost estimators use historical average or volume-based calculations, then the estimation process is simple and quick, but the accuracy of shipping cost estimate is insufficient leading to cart abandonment
Solution Approach 1:
The system performs preliminary actions by pre-fetching and caching rate tables from multiple carriers before the packing optimization is needed. This allows the optimization algorithm to access accurate, up-to-date shipping rates without adding significant computation time to the estimation process.
Solution Approach 2:
The system creates detailed digital models of packing configurations and uses these to calculate precise shipping costs by referencing actual carrier rate tables, replacing rough historical averages with accurate simulated packing scenarios that can be evaluated against real pricing data.
Data Source
AI summary
Systems and methods are disclosed for packing optimization and visualization. In one example, a method for managing a shipment of a plurality of products, comprises: obtaining a request for a packing instruction from a user via a user interface; ranking the products based on their weight information and dimension information to generate a first ranking list; ranking one or more containers based on their dimension information to generate a second ranking list; determining, based on the first ranking list and the second ranking list, a plurality of layout configurations for packing the products into at least one container selected from the one or more containers; selecting a layout configuration from the plurality of layout configurations based at least partially on a rate table including shipping rate information of one or more shipping carriers identified by the user, wherein the selected layout configuration minimizes a cost of shipping the products; generating a visual illustration of the layout configuration; and providing a packing instruction comprising the visual illustration to the user via the user interface.