Automated Shelf Device Dynamic Planogram Adaptation
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Solution Overview
Problem
Current automated shelf systems lack the ability to autonomously optimize product placement and customer guidance, failing to integrate real-time consumer interaction data and operational parameters effectively, leading to suboptimal customer experience and operational inefficiencies in retail environments.
Innovation Solution
An automated shelf device that utilizes planogram data, product information, and operational data to calculate user interaction, enabling predictive and adaptive decision-making, with integrated sensing and actuation capabilities for intelligent product placement and customer guidance, using digital shelf plans and data linking to provide personalized product recommendations and optimize logistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automated shelf systems use static shelf planning software, then product placement can be standardized, but the system cannot adapt to real-time consumer interaction or optimize dynamically
Solution Approach 1:
The patent transforms static shelf planning into a dynamic system by continuously updating digital shelf plans with real-time operating data, sensor data, and consumer interaction data. The system adapts product placement and customer guidance based on changing conditions, making the previously static software solution dynamic and responsive to real-time inputs.
Solution Approach 2:
The system implements feedback loops by collecting sensor data from weight sensors, image recognition cameras, and other monitoring devices, then using this feedback to update digital shelf plans and adjust product placement strategies. Consumer interaction data feeds back into the system to optimize future product arrangements and customer guidance.
2Measurement precision
If the system integrates multiple data sources (sensor data, operating data, consumer interaction data), then decision-making becomes more accurate, but data processing complexity increases
Solution Approach 1:
The patent segments data processing into distinct modules: sensor data acquisition, operating data collection, consumer interaction analysis, and integrated decision-making. Each module processes specific types of data independently before combining results, reducing overall complexity while maintaining comprehensive data integration for accurate measurements and decisions.
3Productivity
If the automated shelf device operates autonomously with predictive capabilities, then operational efficiency improves, but the initial system setup and data infrastructure requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing operating data, sensor data, and product information in structured digital shelf plans before they are needed for decision-making. This preliminary data preparation enables faster, more efficient autonomous operations without requiring complex real-time processing during critical operations.
Data Source
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AI summary
The present invention relates to a method for an automated racking system of an intelligent storage or order picking system, wherein the method comprises the following steps: providing (S1) planogram data depicting the automated racking system, which includes digital rack plans of the automated racking system and which are successively expandable; providing (S2) product information data defining products placed in the automated racking system; creating (S3) product linking data between the product information data; providing (S4) operational data of the automated racking system; creating (S5) data linking data between the operational data and the planogram data;and calculating (S6) user interaction data based on the provided planogram data, the provided product information data, the created product linking data, the provided operational data, and the created data linking data.;