Automated Planogram Generation for Modular Retail Displays
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Solution Overview
Problem
E-commerce entities face challenges in generating optimal planograms efficiently, as manual processes are time-consuming and often result in suboptimal sales maximization due to the complexity of configuring items on modular displays, considering various constraints and criteria.
Innovation Solution
A computing system automates planogram generation by optimizing item placement on modular displays based on physical constraints, user-defined parameters, and sales strategies, using processors to determine optimal item combinations and positions, reducing the need for manual revisions and edits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual planogram generation is used, then operator experience can be applied, but the process is time-consuming and requires multiple revisions
Solution Approach 1:
The system performs self-service by automatically generating planograms through computational algorithms that evaluate multiple configurations and criteria without requiring manual operator intervention for each planogram creation, thereby reducing time loss while maintaining optimization quality
Solution Approach 2:
The patent replaces the mechanical manual drawing process with a computational system that uses algorithms to automatically determine optimal item placements, substituting human manual operations with automated computational methods to eliminate time-consuming revisions
2Adaptability or versatility
If manual planogram drawing is used, then revisions can be made, but the large number of item configurations exceeds operator capacity to generate optimal planograms
Solution Approach 1:
The system segments the complex planogram configuration problem into manageable components by evaluating items, modulars, and criteria separately through computational algorithms, allowing the system to handle large numbers of configurations by breaking down the overall complexity into discrete evaluatable elements
Solution Approach 2:
The system dynamically evaluates multiple item configurations and modular arrangements through computational algorithms, adapting to different criteria and constraints by systematically assessing numerous possibilities rather than relying on static manual configuration
3Manufacturing precision
If manual planogram generation is used, then operator judgment can be applied, but multiple revisions and edits are required
Solution Approach 1:
The system incorporates feedback mechanisms by evaluating planogram configurations against multiple criteria and constraints, using computational algorithms to assess and refine placements systematically, thereby achieving high optimality precision through automated feedback loops rather than manual revision cycles
Solution Approach 2:
The system performs preliminary computational evaluation of multiple configurations before finalizing the planogram, pre-assessing various item placements and modular arrangements to determine optimal configurations in advance, thereby achieving high precision without requiring subsequent manual revisions
Data Source
AI summary
In some examples, a system may configured to execute the instructions to based on the modular data of a peg modular, the item data and the draw strategy data, implement a set of modular placement optimization operations that generate a first modular dataset. In some examples, the set of modular placement optimization operations includes, determining, from a group of items, a combination of items to place onto the peg modular and, for each item of the combination of items, a placement position on the peg modular, and a number of facings. Moreover, the set of modular placement optimization operations includes generating the first modular dataset. Further, the system may be configured to execute the instructions to, based at least on the modular data the first modular dataset, implement a set of operations that determine whether to add additional facings.