Shipping Carton Sizing for Dimensional Weight Cost Reduction
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Solution Overview
Problem
The implementation of Dimensional Weighing in shipping costs has led to increased expenses for ecommerce shipments, necessitating a mechanism to identify or select container dimensions that minimize these costs.
Innovation Solution
A computer-based system utilizing algorithms for optimizing shipping carton dimensions by analyzing goods mix and reducing Dimensional Weighing shipping costs through order sampling, cubing, pre-searching adjustment, and optimal carton set searching, employing a genetic algorithm to determine an optimized carton set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional weight-based shipping pricing is used, then shipping cost calculation is simple, but shipping costs increase significantly under dimensional weighing for low-density parcels
Solution Approach 1:
The system changes the parameter used for shipping cost calculation from weight to dimensional weight for parcels below a threshold density. It calculates dimensional weight by dividing volume by a dimensional weighting factor, then compares this to actual weight and uses the greater of the two for pricing, thereby adjusting the pricing parameter based on parcel characteristics to prevent excessive cost increases for low-density items
2Object-affected harmful factors
If carton size is minimized to reduce dimensional weight, then shipping cost decreases, but space utilization efficiency decreases
Solution Approach 1:
The system dynamically determines optimal carton sizes based on the specific dimensions and density of items being shipped. Rather than using fixed carton sizes, it calculates the minimum required carton volume that accommodates the items while keeping dimensional weight below the weight-based cost threshold, thereby dynamically optimizing the balance between shipping cost and space utilization for each shipping scenario
3Productivity
If multiple carton sizes are used to optimize space utilization, then space utilization improves, but system complexity increases
Solution Approach 1:
The system segments the shipping optimization problem into discrete carton size options based on standard dimensional weighting factors. It identifies specific volume thresholds (such as 1 cubic foot, 2 cubic feet, etc.) and pre-determines optimal carton sizes for each segment, thereby breaking down the complex optimization problem into manageable segments that can be systematically addressed without requiring overly complex decision-making logic
Data Source
AI summary
Described herein are exemplary systems and methods for the optimization of shipping cartons relative to the contents of orders to be placed therein. An exemplary carton optimization method evaluates a sample size of orders received at a given location and then determines an optimal carton set that includes at least one feasible carton for each order, while also minimizing shipping costs based on dimensional weighing-based pricing by reducing the dimensional weight, thereby minimizing void space and increasing the carton space utilization percentage. An exemplary carton optimization method may be further customized to optimize a carton set for an order, based on the cartons already available at a given warehouse or other site. As a result, cartons are utilized and designed more efficiently at the site.