Display space optimization
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Solution Overview
Problem
Retailers face challenges in optimizing product assortment and shelf arrangement to maximize key performance indicators like revenue and profit, given the complexity of product geometry, business rules, and limited display space, which existing methods struggle to address effectively.
Innovation Solution
A system and method that pre-process and formulate the assortment and shelf space optimization problem using Mixed Integer Programming, interacting with a solver to produce an optimal planogram that maximizes the retailer's selected key performance indicator while honoring business rules and constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional manual methods are used for assortment and shelf space optimization, then the process is simple to understand and implement, but the ability to handle product complexity and find optimal arrangements becomes inadequate
Solution Approach 1:
The patent segments the optimization problem into distinct components: product data processing, shelf geometry modeling, business rule validation, and optimization algorithm execution. This segmentation allows the system to handle complex product assortments by breaking down the overall problem into manageable modules, each addressing specific aspects of the optimization challenge.
Solution Approach 2:
The patent introduces an intermediary optimization engine that acts as a mediator between product data, shelf constraints, and business rules. This intermediary component translates complex inputs into optimal shelf arrangements, bridging the gap between raw data and actionable insights while managing system complexity through structured intermediate processing layers.
2Measurement precision
If comprehensive product data and business rules are incorporated into the optimization model, then the solution accuracy and business compliance improve, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-processing product data, validating business rules, and establishing shelf geometry models before executing the optimization algorithm. This pre-processing step prepares data in advance, reducing the computational burden during the actual optimization process and enabling faster, more accurate solutions when comprehensive data is involved.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting optimization parameters based on data quality, product category, and computational resources available. This allows the system to balance solution accuracy with processing time by modifying algorithm parameters such as convergence thresholds, sampling rates, and constraint weighting according to specific operational contexts.
3Productivity
If the optimization model considers multiple key performance indicators simultaneously, then the overall business value improves, but the problem becomes more intractable and difficult to solve
Solution Approach 1:
The patent applies local quality by allowing different KPIs to have varying weights and priorities depending on the specific product category, shelf location, and business context. This enables the optimization model to consider multiple KPIs simultaneously while managing complexity through localized parameter adjustments rather than uniform treatment of all objectives.
Solution Approach 2:
The patent transforms the multi-KPI optimization problem by introducing a hierarchical dimension where KPIs are organized into different levels of importance. This dimensional restructuring converts a potentially intractable multi-objective problem into a more manageable hierarchical optimization framework, allowing the system to balance multiple business values while maintaining problem tractability.
Data Source
AI summary
Systems, methods, and other embodiments associated with assortment and display space optimization are described. In one embodiment, a method creates an optimal planogram. The example method includes receiving data describing i) a set of items described by item dimensions, ii) display space dimensions; iii) business rules, and iv) a key performance indicator. A set of possible shelf positions is identified for each item. An expected sales volume is calculated for each item and shelf position pair based, at least in part, on a selected demand model. The method includes providing i) the expected sales volume for the item and shelf position pairs, ii) a set of constraints that embody the business rules, and iii) an objective function to an optimization problem solver that computes a solution. Based on the solution, a planogram is output that specifies the assortment of items and respective optimal shelf positions of the items.