Pressure Sensor Array for Auto-Replenishment of Shelf Inventory
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Retailers face challenges in efficiently monitoring and replenishing on-shelf inventory due to the manual inspection and high operational costs associated with identifying and tracking thousands of diverse products, lacking effective product recognition capabilities.
Innovation Solution
A system utilizing pressure sensor arrays to detect product forces, convert them into pressure maps, and use computer vision models to identify and track product usage, automatically replenishing inventory based on consumption levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to monitor on-shelf inventory, then workers can identify and track products, but the process becomes time-consuming and increases operational costs
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated computer vision system using cameras and machine learning algorithms. The system captures images of products on shelves, processes them through trained models to identify product types, brands, and stock levels, thereby eliminating the need for manual visual inspection while maintaining or improving identification accuracy.
Solution Approach 2:
The system enables self-service inventory monitoring where the automated vision system continuously tracks product stock levels without human intervention. The machine learning model automatically detects when products are low in stock or out of stock and can trigger replenishment workflows, allowing the inventory system to monitor and manage itself autonomously.
2Reliability
If manual product replenishment order submission is performed, then workers can restock products, but productivity decreases as workers are occupied with inspection tasks
Solution Approach 1:
The system implements continuous feedback loops where the vision system monitors inventory levels in real-time, compares them against predefined thresholds, and automatically triggers replenishment orders when stock falls below critical levels. This closed-loop feedback mechanism ensures reliable inventory replenishment while freeing workers from manual monitoring and ordering tasks.
Solution Approach 2:
The system performs preliminary actions by proactively detecting low stock situations before they become critical and automatically initiating replenishment workflows in advance. The machine learning model predicts when products will run out based on consumption patterns and triggers restocking before shelves are completely empty, ensuring continuous product availability.
3Productivity
If automated systems are implemented to monitor inventory, then operational costs can be reduced, but device complexity increases
Solution Approach 1:
The patent employs a multi-functional vision system that simultaneously performs multiple tasks: identifying product types, recognizing brands, detecting stock levels, monitoring product placement, and tracking consumption patterns. By consolidating these functions into a single integrated system with shared hardware resources, the patent reduces overall system complexity compared to having separate specialized systems for each function.
Solution Approach 2:
The system manages complexity by dynamically adjusting operational parameters such as image capture frequency, processing resolution, and detection sensitivity based on inventory priorities and computational resources. The machine learning models can adapt their processing requirements based on product categories and stock urgency, optimizing the balance between monitoring thoroughness and system resource consumption.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Automates the process of inventory monitoring and replenishment, reducing manual labor and operational costs while ensuring timely restocking of products.
Implementation Method 1
a pressure sensor array positioned on the shelf and configured to detect forces exerted on the pressure sensor array by the products located on the shelf
Data Source
AI summary
Systems and methods for identifying, tracking usage of, and replenishing products on a shelf of a product storage unit include a pressure sensor array positioned on the shelf and configured to detect forces exerted on the pressure sensor array by each of the products located on the shelf. An electronic database stores reference pressure array data representative of a reference pressure data model relative to each of the products. A computing device obtains a pressure data set associated with a product that was captured by the pressure sensor array when the product was positioned on the pressure sensor array, and correlates the pressure data set associated with the product with the reference pressure array data stored in the electronic database to determine an identity of the product and a consumption level of the product, automatically replenishing the product when consumption of the product is above a set threshold.