Carton Damage Detection With ML-Based Routing Attribution
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Solution Overview
Problem
Existing supply chain systems struggle to accurately attribute carton damage to specific locations or vendors due to inconsistent human recording of defects and lack of reliable root cause analysis, leading to inefficiencies and increased costs.
Innovation Solution
Implementing a machine learning model-based carton damage detection system with overhead cameras and local computing systems to automatically capture and analyze images of cartons, determining damage likelihood and storing records in a database, allowing for automated rejection and root cause identification of defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual defect recording by human users is used, then flexibility and adaptability are maintained, but measurement precision and reliability of damage attribution deteriorate due to inconsistent recording and inaccurate root cause selection
Solution Approach 1:
The patent replaces the manual mechanical process of human defect recording with an automated image-based detection system using machine learning models. The system captures images of cartons and automatically identifies damage, eliminating human subjectivity and inconsistency while maintaining system flexibility through configurable parameters and multiple detection modes.
2Measurement precision
If comprehensive defect information is manually recorded, then measurement precision improves, but ease of operation deteriorates due to cumbersome manual entry
Solution Approach 1:
The system performs self-service by automatically capturing images, detecting damage, and recording defect information without requiring manual user input. The machine learning model autonomously processes cartons, generates damage assessments, and stores results in the database, eliminating the burden of manual data entry while ensuring consistent, high-quality defect recording.
3Measurement precision
If automated image-based detection is implemented, then measurement precision and productivity improve, but device complexity increases due to machine learning models and image processing systems
Solution Approach 1:
The patent segments the damage detection system into distinct functional modules: image capture subsystem, machine learning model processing subsystem, and database storage subsystem. This modular segmentation allows each component to be optimized independently and facilitates easier maintenance and deployment, reducing the practical complexity despite the advanced capabilities.
4Productivity
If high volume carton processing is achieved, then productivity increases, but measurement precision may deteriorate due to reduced inspection time
Solution Approach 1:
The system maintains continuous operation by processing cartons in an uninterrupted automated flow. The machine learning model continuously analyzes images as cartons move through the system, eliminating the need to slow down or pause for inspection. This continuous action sustains both high productivity and consistent measurement precision without trade-offs.
Data Source
AI summary
Methods and systems for automated detection of carton defects are disclosed. One method includes capturing one or more images of a carton via a camera system at a routing location within a warehouse of a retail supply chain, and applying a machine learning model to determine a likelihood of damage of the carton. The method can include, based on the likelihood of damage being above a particular threshold, identifying the carton as damaged. A carton assessment record can be stored in a carton damage tracking database, including the one or more images of the carton alongside the likelihood of damage and the routing location.