AI Packaging Box Recommendation System
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Solution Overview
Problem
Current methods for packaging products do not efficiently recommend the optimal box size based on product volume information, leading to increased storage and logistics costs and potential damage to products during transportation.
Innovation Solution
A method and system that utilize machine learning, specifically an artificial neural network, to recommend the best packaging box by inputting product volume information and calculating a packaging score for each box, thereby identifying the box with the highest priority score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the box size is reduced to lower storage costs and improve logistics efficiency, then storage costs and logistics costs are reduced, but product damage risk increases and packaging reliability deteriorates
Solution Approach 1:
The system changes the parameter of box size selection by using machine learning to determine the optimal box dimensions based on product volume characteristics. The ML model processes product dimension data and outputs packaging scores for different box sizes, enabling dynamic parameter adjustment that balances storage efficiency with product protection requirements.
2Productivity
If the box size is reduced to improve logistics efficiency and reduce storage costs, then logistics costs are reduced, but product damage risk increases
Solution Approach 1:
The system incorporates feedback mechanisms where packaging scores are calculated based on product volume information and box characteristics. The ML model continuously learns from packaging data to refine its recommendations, creating a feedback loop that optimizes the balance between logistics efficiency and product safety over time.
3Device complexity
If traditional packaging methods are used without machine learning, then the system complexity is low, but packaging optimization and cost reduction capabilities are limited
Solution Approach 1:
The patent replaces traditional manual or rule-based packaging determination methods with a machine learning-based system. The ML model substitutes complex decision-making algorithms with trained neural networks that automatically process product volume data and recommend optimal box sizes, achieving higher packaging optimization capability.
Data Source
AI summary
A method and system for recommending a type of box to package products using volume information of the products is proposed. The proposal relates to a method for learning which box can most efficiently load one or more products and recommending it using artificial intelligence.The method comprises a first step of receiving first product specification data having product size information and extracting product size information from the first product specification data; a second step of inputting the extracted product size information into a machine learning model, and the machine learning model outputting a first packaging score for each box; and a third step of searching for a box with the highest priority packaging score among the first packaging scores for each box, and obtaining and outputting box specification information corresponding to the searched box.