Computer Vision Pallet Verification for Accurate Store Delivery
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Solution Overview
Problem
The delivery of products from distribution centers to stores is plagued by errors and inefficiencies, including mis-picked or missing items, time-consuming pallet rearrangement, and lengthy product verification processes, leading to increased operational costs and delays.
Innovation Solution
A delivery system integrating machine learning and computer vision with serialized RFID/Barcode shipping pallets to validate pallet contents electronically, optimize loading sequences, and streamline product verification at distribution centers and stores, using mobile devices for image comparison and electronic ledgering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual pallet building and loading is used, then labor flexibility is maintained, but order accuracy decreases due to human error in picking and loading
Solution Approach 1:
The patent replaces manual mechanical pallet building and verification processes with an automated system comprising computer vision cameras, machine learning algorithms, and RFID readers. The system automatically captures images of pallets, identifies products using machine learning models, verifies quantities through computer vision, and tracks pallets using RFID technology, thereby eliminating human error in picking and loading operations.
Solution Approach 2:
The system enables self-verification of pallet contents through automated computer vision and machine learning algorithms that independently validate product identification and quantities without human intervention. The automated pallet building system also self-corrects errors by detecting and flagging mismatches between picked items and order requirements, allowing the system to self-validate and self-correct without manual checking.
2Loss of time
If traditional product verification methods are used, then simplicity is maintained, but verification time increases significantly
Solution Approach 1:
The system performs preliminary verification of pallet contents at the distribution center before delivery to the store. Computer vision cameras capture images of pallets during the packing process, machine learning algorithms identify products and quantities in advance, and RFID readers tag pallets with unique identifiers. This preliminary verification ensures that pallets are correctly assembled and labeled before they leave the distribution center, eliminating the need for time-consuming manual verification at the store.
Solution Approach 2:
The patent replaces manual physical counting and verification of products with automated computer vision systems, machine learning algorithms, and RFID tracking. The system uses cameras to capture pallet images, algorithms to automatically identify and count products, and RFID readers to track pallet locations and statuses, thereby reducing verification time from minutes to seconds and enabling rapid verification without manual intervention.
3Reliability
If pallets are loaded in arbitrary sequence, then loading speed is maintained, but delivery efficiency decreases due to incorrect pallet placement
Solution Approach 1:
The system implements real-time feedback mechanisms where RFID readers continuously monitor pallet locations, computer vision systems verify pallet contents match the order, and machine learning algorithms detect and alert operators to potential errors immediately during the loading process. This feedback loop ensures that pallets are placed on the correct trailers in the correct sequence, and any mismatches are caught and corrected before delivery, thereby ensuring both accuracy and efficiency.
Solution Approach 2:
The patent employs dynamic routing and loading sequence optimization where the system adapts the loading process based on real-time conditions. The machine learning system analyzes order requirements, pallet contents, and trailer configurations to dynamically determine the optimal loading sequence and allocation. RFID tracking enables real-time updates of pallet locations and statuses, allowing the system to dynamically adjust loading operations to ensure correct pallet placement while maintaining high throughput.
Data Source
AI summary
A delivery system generates a pick sheet containing a plurality of SKUs based upon an order. A loaded pallet is imaged to identify the SKUs on the loaded pallet, which are compared to the order prior to the loaded pallet leaving the distribution center. The loaded pallet may be imaged while being wrapped with stretch wrap. At the point of delivery, the loaded pallet may be imaged again and analyzed to compare with the pick sheet.