Paper: arXiv 2408.03655

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper introduces advanced deep learning architectures like GANs with hyper-graph embeddings and discusses theoretical challenges like data sparsity, indicating moderate-to-high mathematical sophistication. However, it lacks explicit backtest metrics, code, or datasets, relying instead on conceptual validation and preliminary results, placing it lower on empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Simulate Consumer Transactions<br>with SKU Constraints"] --> B["Data Input<br>Retail Transaction &<br>SKU Availability Data"]
  B --> C["Methodology<br>GAN with Hyper-graph Embeddings"]
  C --> D["Computational Process<br>Generator vs Discriminator<br>Under Stock Constraints"]
  D --> E["Outcome 1<br>Synthetic Transaction Generation"]
  D --> F["Outcome 2<br>Enhanced Realism &<br>Assortment Optimization"]

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0 of 52 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.