  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.
