Optimized planograms
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Solution Overview
Problem
Current systems for in-store and online product displays lack efficiency in maximizing sales and minimizing inventory costs, as they rely on manual selection and placement methods that do not account for real-time data and consumer behavior.
Innovation Solution
An automated method for selecting and assigning products to planogram templates based on constraints such as brand, category, season, and performance history, using a dynamic index calculation to optimize product placement and update displays in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual selection and placement of products is used, then implementation simplicity is maintained, but sales maximization efficiency deteriorates
Solution Approach 1:
The system performs automatic product selection and placement without requiring manual intervention. The planogram generation system autonomously analyzes sales data, consumer behavior, and product attributes to generate optimized display layouts, enabling the system to serve itself rather than relying on manual operations.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Instead of manually selecting and placing products, the system uses server-based applications that process data, apply optimization algorithms, and generate planograms automatically, substituting human labor with computational mechanics.
2Adaptability or versatility
If static planogram templates are used, then implementation simplicity is maintained, but adaptability to real-time sales data deteriorates
Solution Approach 1:
The system transitions from static planogram templates to dynamic, continuously updated displays. Planograms are automatically regenerated based on real-time sales data and consumer behavior changes, allowing the display strategy to adapt dynamically to current market conditions rather than remaining fixed.
Solution Approach 2:
The system implements continuous feedback loops where sales data and consumer behavior information are collected, analyzed, and used to automatically adjust and regenerate planograms. This feedback mechanism enables the system to learn from performance data and continuously optimize product placement strategies.
3Productivity
If comprehensive product constraints are considered, then product placement optimization is improved, but processing complexity deteriorates
Solution Approach 1:
The system segments the complex constraint processing into distinct categories (brand constraints, category constraints, seasonal constraints, performance history constraints). Each constraint type is processed independently by specialized algorithms, breaking down the overall complexity into manageable segments that can be handled systematically.
Solution Approach 2:
The system changes parameters such as brand, category, season, and performance metrics to evaluate different product placement scenarios. By systematically varying these parameters according to defined constraints, the system optimizes product placement without manually processing every possible combination, reducing computational complexity through parameter-based filtering.
Data Source
AI summary
Systems and methods disclosed herein relate to the selection of planogram templates for display in retail stores and in online retail environments and the automatic selection, assignment, and monitoring of items assigned to locations in those templates. Additionally, planogram templates, product assortments, and assignments may be dynamically updated in real time based upon feedback and other factors.