@article{4730,
  author = {Dit Suthiwong},
  title = {Advanced Retail Analytics Using Market Basket Mining, Product Networks, and Time-Series Forecasting},
  journal = {Journal of Information & Systems Management},
  year = {2026},
  volume = {16},
  number = {2},
  doi = {https://doi.org/10.6025/jism/2026/16/2/75-87},
  url = {https://www.dline.info/jism/fulltext/v16n2/jismv16n2_3.pdf},
  abstract = {The rapid expansion of digital retail has generated extensive transactional data, creating unprecedented
opportunities for advanced, data-driven decision-making. Traditional Market Basket Analysis (MBA) and
association rule mining frequently struggle with large, sparse datasets, often producing trivial rules while
overlooking temporal and network-level purchasing dynamics. To address these limitations, this study
introduces an integrated analytical framework that synergizes association rule mining, product network
analytics, temporal modeling, clustering, and time-series forecasting. Leveraging a synthetic dataset of
30,000 grocery transactions spanning four months, the pipeline systematically extracts meaningful product
affinities and visualizes complex purchasing pathways through association rule graphs and Sankey diagrams.
A product co-occurrence network subsequently identifies structural purchasing communities and central
hub items that drive strategic cross-selling opportunities. Temporal trend analysis captures fluctuating
customer transaction volumes, while a regression-based forecasting model extrapolates future demand
trajectories to optimize inventory planning and workforce scheduling. By synthesizing these complementary
analytical layers, the framework transcends conventional basket analysis to deliver a holistic understanding
of retail ecosystems. The findings demonstrate how multi-dimensional analytics significantly enhance nextbasket
recommendation engines, inform store layout optimization, and improve demand prediction accuracy.
Ultimately, this unified approach equips retailers with actionable insights to personalize marketing strategies,
streamline operational efficiency, and adapt proactively to evolving consumer behaviors in highly competitive
market environments.},
}