Consumer Transactions Simulation through Generative Adversarial Networks

TL;DR

This paper introduces a novel GAN-based system for generating realistic synthetic retail transaction data that incorporates consumer behavior and SKU constraints, improving demand forecasting and inventory management.

Contribution

It presents a new GAN architecture integrating SKU data and advanced embeddings to simulate consumer transactions under stock constraints, addressing data scarcity issues.

Findings

Generated transactions show higher realism compared to previous models.

Enhanced simulation accuracy aids demand forecasting.

System demonstrates practical potential for retail strategy optimization.

Abstract

In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecasting and fine-tune inventory management. This paper presents an innovative application of Generative Adversarial Networks (GANs) to generate synthetic retail transaction data, specifically focusing on a novel system architecture that combines consumer behavior modeling with stock-keeping unit (SKU) availability constraints to address real-world assortment optimization challenges. We diverge from conventional methodologies by integrating SKU data into our GAN architecture and using more sophisticated embedding methods (e.g., hyper-graphs). This design choice enables our system to generate not only simulated consumer purchase behaviors but also reflects the dynamic interplay between consumer behavior and SKU availability -- an aspect often overlooked, among others, because of data scarcity in legacy retail simulation models. Our GAN model generates transactions under stock constraints, pioneering a resourceful experimental system with practical implications for real-world retail operation and strategy. Preliminary results demonstrate enhanced realism in simulated transactions measured by comparing generated items with real ones using methods employed earlier in related studies. This underscores the potential for more accurate predictive modeling.