Retail Space Planning via Pattern Recognition
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Solution Overview
Problem
Retailers face challenges in achieving a holistic view of space optimization across multiple stores due to the complexity of macro space optimization, leading to high numbers of space recommendations that are difficult to analyze and implement, and existing methods for creating planograms are inefficient, particularly in defining trade areas and clustering stores based on sales drivers and competition.
Innovation Solution
A method and system for space planning that acquires and processes sales, space, and demographic data across multiple stores using hardware processors to generate final and delta space allocations, and performs pattern extraction through Principal Component Analysis (PCA) to identify patterns, allowing for the creation of optimized floor plans and planograms that can be applied across store clusters rather than individually.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If macro space optimization is carried out at individual store level for all stores simultaneously, then space allocation recommendations are generated for each category of each store, but the number of recommendations increases extremely high making it difficult to bring key inferences
Solution Approach 1:
The patent segments the retail network into store clusters based on geographic proximity and similar characteristics. Instead of analyzing all stores individually, the system performs space optimization at the cluster level, grouping stores that share common traits. This segmentation reduces the number of analysis units from individual stores to clusters, making the overall process more manageable while still providing comprehensive coverage across the retail network.
Solution Approach 2:
The patent merges multiple individual store analyses into cluster-level analyses. By combining stores with similar characteristics into clusters, the system performs space optimization once per cluster rather than once per store. This merging approach consolidates redundant calculations and recommendations, reducing the total number of recommendations generated while maintaining the quality of space allocation decisions across all stores in the cluster.
2Manufacturing precision
If planograms are created at individual store level, then space optimization is precise for each store, but it consumes more effort, time, and cost compared to cluster-level approach
Solution Approach 1:
The patent segments stores into clusters based on geographic and characteristic similarities, enabling the system to create planograms at the cluster level rather than for each individual store. This segmentation allows precise optimization within each cluster while reducing the overall number of planograms that need to be created and maintained, thereby saving time and resources.
Solution Approach 2:
The patent creates universal planograms that can be applied across multiple stores within a cluster. These cluster-level planograms serve as templates that can be replicated or slightly customized for individual stores, providing a multi-functional solution that reduces the total effort required for planogram creation while maintaining precision for each store's specific needs.
3Loss of time
If stores are clustered based on sales drivers such as demographic, competition, weather, then the approach saves effort and time, but it faces challenges in defining trade area and determining competition intensity
Solution Approach 1:
The patent introduces geographic information systems (GIS) and standardized trade area definitions as intermediaries to objectively determine cluster characteristics. Instead of subjectively defining trade areas, the system uses GIS-based methodologies with predefined radius parameters (e.g., 1-mile, 3-mile, 5-mile radii) to consistently measure competition intensity and demographic factors across all stores, eliminating subjectivity and reducing the difficulty of detection and measurement.
Solution Approach 2:
The patent transforms subjective clustering criteria into quantifiable parameters with standardized definitions. Trade areas are defined using specific geographic parameters (radius, shape, boundary), and competition intensity is measured using standardized metrics (number of competitors within defined radius, market share percentages). This parameterization converts qualitative assessments into objective, measurable data that can be consistently applied across all store clusters.
Data Source
AI summary
In retail, macro space optimization is carried out at individual stores to allocate optimum space for each category. Each retailer has many stores and macro space optimization is experimented with different objectives such as expand space, reduce space and constant space of a store individually. Thus, the number of space recommendations analyzed at corporate level increases extremely high and making it difficult to bring key inferences out of these recommendations and creating challenges in implementation of results such as creation of planograms and floor plans. Embodiments of the present disclosure provide a method and system for identifying underlying patterns that reside in space recommendations across stores and creating drastically reduced number of floor plans and planograms in accordance with the identified set of patterns unlike large number of floor plans or planograms generated by state of the art space planning systems.