Automated Planogram Generation for Multi-Criteria Shelf Placement
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Solution Overview
Problem
E-commerce entities face challenges in generating optimal planograms efficiently, as existing methods are time-consuming and reliant on manual revisions, often failing to maximize sales due to the complexity of item configurations and modular types.
Innovation Solution
A computing system that automates planogram generation by considering physical constraints and user-defined parameters, optimizing item placement on shelves and pegs to enhance sales and reduce manual editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual planogram generation is used by operators, then the process can be completed with existing tools, but the time required to generate and revise planograms increases significantly
Solution Approach 1:
The system enables self-service planogram generation by automatically determining optimal item placements based on input parameters without requiring manual operator intervention for each configuration decision. The automated engine evaluates multiple configurations and generates planograms independently, eliminating the time-consuming manual revision cycle.
Solution Approach 2:
The patent replaces the manual mechanical process of drawing and revising planograms with an automated computational system. The system uses algorithms to evaluate configurations and generate optimized planograms, substituting human operators with an automated engine that processes calculations and generates results without manual intervention.
2Adaptability or versatility
If operators manually create planograms based on experience, then subjectivity is reduced, but the ability to optimize for multiple criteria (sales, shoppability, constraints) is limited
Solution Approach 1:
The system segments the complex optimization problem into distinct evaluation criteria (sales optimization, shoppability, physical constraints, business rules). Each criterion is evaluated separately by the automated engine, allowing comprehensive multi-criteria optimization while managing complexity through structured analysis of individual factors.
Solution Approach 2:
The system handles multiple optimization criteria by changing and adjusting various parameters (item placements, facings, positions) to find optimal configurations. The automated engine evaluates different parameter combinations against multiple criteria simultaneously, enabling versatile optimization that exceeds human capability while managing complexity through systematic parameter evaluation.
3Quantity of substance
If the number of item configurations and modular types increases, then more items can be placed on shelves, but the complexity of finding optimal planograms increases
Solution Approach 1:
The system dynamically evaluates configurations by adjusting item placements and facings based on multiple criteria. The automated engine adapts to different modular types and item quantities by changing configuration parameters dynamically, finding optimal solutions regardless of the number of items or modular complexity without requiring manual reconfiguration for each scenario.
Data Source
AI summary
In some examples, a system may be configured to execute the instructions to, based at least on the modular data of a shelf modular implement a set of modular placement optimization operations that generate a first modular dataset. In some examples, the set of modular placement optimization operations include, determining, from a group of items, a combination of items to place onto a shelf modular, and, for each item of the combination of items, a placement position on the shelf modular, and a number of facings. Additionally, the set of modular placement optimization operations include generating the first modular dataset. Moreover, the system may be configured to execute the instructions to, based at least in part on the first modular dataset, and the draw strategy data, determine whether to implement a set of re-run operations.