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

TL;DR

This paper presents a cloud-native diffusion model system that automates planogram creation for retail stores, drastically reducing design time and costs while ensuring constraint satisfaction and scalability.

Contribution

It introduces a novel diffusion model-based architecture for automated planogram synthesis, integrating retail constraints and scalable cloud deployment.

Findings

Reduces planogram design time by 98.3%.

Achieves 94.4% constraint satisfaction.

Cuts creation expenses by 97.5%.

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.