FP Growth Based Consumer Purchase Patterns for Inventory Optimization in Perishable MSME Supply Chains
DOI:
https://doi.org/10.29408/edumatic.v10i2.35983Keywords:
association rule mining, fp-growth, inventory decision support, knowledge discovery in databases, perishable productsAbstract
Perishable inventory management remains a major challenge for fresh produce MSMEs because of demand uncertainty, product deterioration, and limited data-driven replenishment strategies. Although FP-Growth has been widely applied in market basket analysis, its role in transforming consumer purchasing patterns into operational inventory decisions is limited. This study develops an FP-Growth-based decision-support framework to identify purchasing associations that can support replenishment planning in perishable retail environments. This study adopts the Knowledge Discovery in Databases (KDD) framework and analyzes 100 real-world transaction records from a traditional fresh produce retailer. Transaction data were preprocessed and analyzed using association rule mining with a minimum support of 18% and a minimum confidence of 90%. The results generated 12 relevant association rules, with the strongest rule involving shallots and oranges leading to chili purchases, with 100% confidence and a lift value of 1.493. These findings indicate that FP-Growth can extract interpretable purchasing relationships to support coordinated procurements and inventory planning. This study contributes to the literature by extending association rule mining from descriptive market analysis to an explainable inventory decision-support approach for perishable MSMEs. Further validation using larger and multi-location datasets is recommended for future studies.
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