Multi-Stage Store Clustering for Planogram Optimization
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Solution Overview
Problem
Assigning unique planograms to large numbers of retail stores spread over large geographical areas is impractical due to the difficulty and cost of supplying, staffing, and arranging items according to different patterns in each store.
Innovation Solution
A multi-stage store clustering technique using various dimensions of data, such as demand purchase patterns, transactional indicators, and customer demographics, to determine optimal sales clusters, which allows for customizable and dynamic clustering without subjective intervention, enabling the creation of modular planograms that can be applied across multiple stores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a unique planogram is assigned to each store, then store performance optimization is improved, but implementation cost and complexity increase
Solution Approach 1:
The patent combines multiple stores into clusters based on similarity metrics (geographic location, sales performance, customer demographics, inventory patterns) and assigns a single planogram to each cluster. This merging approach maintains the benefit of optimized store performance while reducing implementation complexity by treating multiple stores as a single unit for planogram purposes.
Solution Approach 2:
The patent creates universal planograms that can be applied across multiple stores within a cluster. Each planogram serves multiple stores simultaneously, making it multi-functional and reducing the total number of unique planograms needed in the system, thereby lowering implementation complexity while maintaining performance optimization.
2Productivity
If a unique planogram is assigned to each store, then store performance optimization is improved, but supply and staffing costs increase
Solution Approach 1:
By merging stores into clusters with identical or similar planograms, the patent enables consolidated supply chain operations. Stores within a cluster can share inventory pools, procurement processes, and staffing arrangements, significantly reducing the quantity of resources needed compared to managing each store independently with unique planograms.
3Device complexity
If stores are clustered into groups, then implementation cost is reduced, but customization capability decreases
Solution Approach 1:
The patent applies local quality by creating clusters at appropriate granularity levels. Stores with highly similar characteristics are grouped together with the same planogram, while stores with different characteristics form separate clusters with different planograms. This ensures each cluster receives customized treatment appropriate to its specific characteristics while maintaining overall system efficiency.
Data Source
AI summary
Initial sales cluster is divided by the control circuit into a plurality of velocity buckets. Subsequently, each velocity bucket is divided into a plurality of micro-clusters. The micro-clusters are defined according to demographic information or store characteristic information. A importance score and a performance score for each of the micro-clusters is determined. An optimal sales cluster and a corresponding optimal planogram for each retail store in each micro-cluster are determined based upon the importance score and the performance score.