Abstract
Effective stock management is expected to solver the issue of overstocking and understock that may cause revenue losses. This research aims to present an integrated strong inventory management system using cutting-edge technology implementation applied on the publicly available market data. Raw sales data is pre-processed and fine-tuned, time series analysis and a range of machine learning strategies were applied on tuned dataset to produce accurate demand forecasts. Furthermore, we integrated Open AI for evaluating the product's generalized review to help with decision-making in the simplest and most efficient way. It is demonstrated that the simplest strategy to control stock levels to maximize profitability could be achieved by integrating the of projected sales and customer reviews.
Recommended Citation
Li, Honglei and Jamadar, Sowjanuya, "Precise Inventory Forecasting based on Sales History and Reviews: Intellectual Inventory Planning by Machine Learning and OpenAI Integration" (2025). UK Academy for Information Systems Conference Proceedings 2025. 23.
https://aisel.aisnet.org/ukais2025/23