Abstract
Seasonal retail businesses, particularly firecracker stores, operate within high-stakes, compressed selling windows characterized by extreme demand volatility and significant inventory risks. Traditional manual forecasting methods often fail to capture non-linear surges, leading to prohibitive overstocking costs or lost revenue due to stockouts during peak festival periods. This paper presents an AI-driven framework for demand forecasting and inventory optimization using the XGBoost (Extreme Gradient Boosting) algorithm. By integrating historical sales data with high-impact seasonal features-including festival proximity, holiday indicators, and external environmental factors the proposed model identifies complex patterns that traditional statistical methods overlook. The system further employs dynamic safety stock calculations to optimize inventory levels and maximize serviceability. Experimental results demonstrate that the XGBoost-based approach significantly outperforms traditional moving-average and linear regression models, achieving a reduction in Mean Absolute Percentage Error (MAPE). The findings suggest that AI-based optimization not only enhances profitability but also improves operational safety by minimizing the risks associated with the prolonged storage of hazardous seasonal goods.
Citations
IRE Journals:
Asan Nainar M, Rajavel J "AI-Based Demand Forecasting and Inventory Optimization for Seasonal Retail Cracker Stores" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 129-134 https://doi.org/10.64388/IREV9I10-1715847
IEEE:
Asan Nainar M, Rajavel J
"AI-Based Demand Forecasting and Inventory Optimization for Seasonal Retail Cracker Stores" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1715847
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12