AI Video Audit for Warehouse Picking Deviations
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Solution Overview
Problem
Traditional quality control processes in the picking sub-process of order fulfillment are inefficient and costly due to manual audits that are time-consuming, random, and often anticipateable, leading to reduced effectiveness and peer pressure issues in high-volume, high-value environments with little data on process performance until completion.
Innovation Solution
A system utilizing cameras and AI processors to capture and analyze the picking process in real-time, detecting deviations and triggering quality corrections based on historical data, focusing on high-risk items or pallets, and employing blind spot analysis to determine audit priorities, rather than traditional random sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual random sampling audits are performed on completed pallets, then quality control is achieved, but the process is extremely time-consuming and costly
Solution Approach 1:
The system performs preliminary actions by capturing video data of the picking process in real-time as it occurs, rather than waiting until the pallet is completed. The AI processor analyzes this data during the picking process itself, enabling quality control before the pallet is finalized, thus reducing post-completion audit time while maintaining reliability
Solution Approach 2:
The patent replaces the manual mechanical audit process with an automated AI-based video analysis system. Instead of human operators manually counting and checking items on completed pallets, the system uses computer vision and machine learning to automatically detect deviations during the picking process, eliminating the time-consuming manual audit while preserving quality control effectiveness
2Reliability
If 1% random sampling of pallets is performed, then some quality control is achieved, but the audit volume is costly and effectiveness is reduced due to indiscriminate sampling
Solution Approach 1:
The system applies local quality by focusing audit resources on specific high-risk areas rather than uniformly sampling all pallets. The AI processor identifies deviations in critical picking operations and prioritizes analysis of pallets with detected anomalies, concentrating quality control efforts where they are most needed rather than spreading resources thinly across all pallets
Solution Approach 2:
The system implements feedback by continuously monitoring the picking process and using AI analysis to identify deviations in real-time. When deviations are detected, the system provides immediate feedback and triggers targeted audits only for affected pallets, rather than performing blanket sampling. This feedback-driven approach reduces overall audit volume while maintaining or improving quality control coverage
3Ease of operation
If sampling happens at predictable intervals, then audit scheduling is simple, but warehouse operators can anticipate which pallet is due next and pay particular attention to loading it well
Solution Approach 1:
The system transitions from static, predetermined audit intervals to dynamic, real-time audit triggering based on actual process conditions. The AI processor continuously analyzes video data and dynamically determines which pallets require auditing based on detected deviations, picker behavior patterns, and risk factors. This dynamic approach maintains operational simplicity while eliminating predictability, as audit priorities adjust automatically based on real-time observations rather than fixed schedules
4Reliability
If inspection is performed by colleagues, then peer review is achieved, but there is strong peer pressure to not identify non-conformance
Solution Approach 1:
The system enables self-service quality control by having the AI processor independently analyze picking process data without human intervention in the actual auditing function. The automated system objectively detects deviations and generates audit priorities without being influenced by peer relationships, ensuring reliability while keeping the overall process simple through automation rather than adding complex human review layers
Data Source
AI summary
A system for performing smart auditing of a picking sub-process of an order fulfilment process in a warehousing environment includes a set of cameras arranged to capture videos of the picking sub-process in real-time, and an Artificial Intelligence (AI) based processor communicatively coupled to the set of cameras. The AI based processor includes an input component for receiving and processing the captured videos to generate a processed video, and a processing component including an order process data analysis component for analysing the processed video to detect order process specific deviations, and a historical data analysis component for analysing the processed video based on historical behavioural data of corresponding order picker. The AI based processor further includes a triggering component for triggering a quality process correcting event for one or more pallets based on the analysis performed by the processing component.