Shelf Replenishment via Mobile Image Analysis
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Solution Overview
Problem
Retail stores face challenges in efficiently managing on-shelf inventory visibility, leading to inefficiencies in replenishment processes that impact labor costs and customer experience.
Innovation Solution
A system and method that preprocess sales data to determine optimal bucket sizes and predict sales events for individual SKUs, generating stock positions and top-up quantities, and creating a pick-up list using smart batching based on SKU priorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual gap scan or camera-based gap scan is used for inventory monitoring, then stock visibility is improved, but labor effort and infrastructure requirements increase
Solution Approach 1:
The system enables self-service inventory monitoring by leveraging existing mobile devices carried by customers and employees. Instead of requiring specialized scanning infrastructure, the system uses the phones' cameras and processors to capture and analyze shelf images, allowing the inventory system to serve itself through user-generated content.
Solution Approach 2:
The patent replaces mechanical scanning systems (manual gap scanners and camera-based gap scan equipment) with a software-based solution running on mobile devices. Image processing and inventory detection are performed algorithmically rather than through dedicated mechanical scanning hardware, reducing infrastructure complexity.
2Loss of information
If manual gap scan or camera-based gap scan is used for inventory monitoring, then stock visibility is improved, but labor effort increases
Solution Approach 1:
Customers and employees inadvertently perform inventory monitoring by taking photos with their mobile devices during normal shopping activities. The system automatically processes these images to detect out-of-stock conditions, eliminating the need for dedicated labor to perform manual gap scans.
Solution Approach 2:
The system uses copies of shelf images captured by mobile device cameras instead of requiring physical inspection. These image copies are processed automatically to detect inventory status, replacing manual visual inspection with automated image analysis.
3Device complexity
If reactive replenishment approaches are used, then simple monitoring is maintained, but productivity and profitability decrease
Solution Approach 1:
The system performs preliminary detection of out-of-stock conditions by continuously analyzing images from mobile devices. By identifying stock issues before they impact customers, the system enables proactive replenishment actions, improving productivity while maintaining monitoring simplicity through automated alert generation.
Solution Approach 2:
The system establishes a feedback loop where detected out-of-stock conditions automatically trigger replenishment notifications to relevant personnel. This closed-loop feedback mechanism transforms simple monitoring into an active productivity tool by ensuring timely responses to inventory issues.
4Ease of operation
If timely replenishment is not performed, then labor effort is minimized, but loss of sale increases
Solution Approach 1:
The system detects out-of-stock conditions preliminarily and generates replenishment alerts before complete stockouts occur. This allows scheduled replenishment at optimal times, ensuring sale availability while minimizing unnecessary labor effort by only triggering alerts when actually needed.
Solution Approach 2:
Automated feedback notifications are sent to replenishment personnel when stock levels are low, creating a reliable system that maintains sale availability without requiring constant manual monitoring. The feedback mechanism ensures timely action only when necessary, balancing labor effort with reliability.
Data Source
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AI summary
Retail stores have limited visibility of on shelf inventory. Conventional approaches for targeted replenishment are reactive in nature and are also infrastructure and labor heavy. Present disclosure provides systems and methods for identification and replenishment of targeted items on shelves of stores wherein input data pertaining to sales of items is pre-processed and stock keeping unit (SKU) wise optimal bucket size is determined for predicting sales events for individual SKU based on historical events. Top-up requests are generated for each SKU for the planning bucket sizes and further a pick-up list using smart batching of the top-up requests is created based on SKU priorities. The pick-up list and top-up requests are executed to ensure items are topped up at the right time. Further, rate of sales or forecast the rate of sales are continually monitored throughout the day to ensure items are identified for targeted replenishment in retail stores.