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Cloud-Native Generative AI for Automated Planogram Synthesis: A Diffusion Model Approach for Multi-Store Retail Optimization

Ravi Teja Pagidoju, Shriya Agarwal

TL;DR

This work tackles the time-intensive task of planogram design in retail by introducing a cloud-native diffusion-model system that learns from historical layouts across many stores to generate novel, store-specific shelf plans under business and physical constraints. It advances the field by integrating constraint-aware training directly into a diffusion framework and by deploying a scalable three-layer cloud-edge architecture for training, inference, and integration with existing retail systems. Key results from simulation-based evaluation show dramatic improvements: planogram design time drops from around 30 hours to 0.5 hours (98.3% faster), cost per planogram falls by about 97.5%, and average constraint satisfaction reaches 94.4%, with strong business case metrics including a 4.4-month break-even and a 5-year NPV of ~$89.7M. The approach demonstrates the viability of generative AI for automated retail space optimization, with practical implications for operational efficiency, merchandising quality, and rapid adaptation to market dynamics.

Abstract

Planogram creation is a significant challenge for retail, requiring an average of 30 hours per complex layout. This paper introduces a cloud-native architecture using diffusion models to automatically generate store-specific planograms. Unlike conventional optimization methods that reorganize existing layouts, our system learns from successful shelf arrangements across multiple retail locations to create new planogram configurations. The architecture combines cloud-based model training via AWS with edge deployment for real-time inference. The diffusion model integrates retail-specific constraints through a modified loss function. Simulation-based analysis demonstrates the system reduces planogram design time by 98.3% (from 30 to 0.5 hours) while achieving 94.4% constraint satisfaction. Economic analysis reveals a 97.5% reduction in creation expenses with a 4.4-month break-even period. The cloud-native architecture scales linearly, supporting up to 10,000 concurrent store requests. This work demonstrates the viability of generative AI for automated retail space optimization.

Cloud-Native Generative AI for Automated Planogram Synthesis: A Diffusion Model Approach for Multi-Store Retail Optimization

TL;DR

This work tackles the time-intensive task of planogram design in retail by introducing a cloud-native diffusion-model system that learns from historical layouts across many stores to generate novel, store-specific shelf plans under business and physical constraints. It advances the field by integrating constraint-aware training directly into a diffusion framework and by deploying a scalable three-layer cloud-edge architecture for training, inference, and integration with existing retail systems. Key results from simulation-based evaluation show dramatic improvements: planogram design time drops from around 30 hours to 0.5 hours (98.3% faster), cost per planogram falls by about 97.5%, and average constraint satisfaction reaches 94.4%, with strong business case metrics including a 4.4-month break-even and a 5-year NPV of ~$89.7M. The approach demonstrates the viability of generative AI for automated retail space optimization, with practical implications for operational efficiency, merchandising quality, and rapid adaptation to market dynamics.

Abstract

Planogram creation is a significant challenge for retail, requiring an average of 30 hours per complex layout. This paper introduces a cloud-native architecture using diffusion models to automatically generate store-specific planograms. Unlike conventional optimization methods that reorganize existing layouts, our system learns from successful shelf arrangements across multiple retail locations to create new planogram configurations. The architecture combines cloud-based model training via AWS with edge deployment for real-time inference. The diffusion model integrates retail-specific constraints through a modified loss function. Simulation-based analysis demonstrates the system reduces planogram design time by 98.3% (from 30 to 0.5 hours) while achieving 94.4% constraint satisfaction. Economic analysis reveals a 97.5% reduction in creation expenses with a 4.4-month break-even period. The cloud-native architecture scales linearly, supporting up to 10,000 concurrent store requests. This work demonstrates the viability of generative AI for automated retail space optimization.
Paper Structure (25 sections, 7 equations, 4 figures, 2 tables)

This paper contains 25 sections, 7 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background and Literature Review
  3. Traditional Planogram Optimization
  4. Machine Learning in Retail
  5. Diffusion Models
  6. Cloud-Native Architectures
  7. Methodology
  8. Problem Formulation
  9. Diffusion Model Architecture
  10. Training Dataset and Process.
  11. Constraint-Aware Training
  12. Cloud-Native Architecture
  13. Deployment Strategy
  14. Results and Analysis
  15. Experimental Setup
  16. ...and 10 more sections

Figures (4)

  • Figure 1: Time and cost comparison of planogram creation methods
  • Figure 2: Constraint satisfaction rates for AI-generated planograms across five key metrics with standard deviation error bars. Dashed line indicates 90% minimum threshold.
  • Figure 3: Scalability analysis of cloud-native architecture
  • Figure 4: Return on investment timeline