AI-Assisted Inventory Forecasting in Small Enterprises

Abstract

Inventory management poses a significant challenge to SMEs, as they frequently depend on manual solutions or rudi­mentary tools, leading to inaccurate forecasts of consumer de­mands, over-stocking, and under-stocking. This paper proposes an artificial intelligence-based inventory prediction model de­signed to ensure accurate demand forecasting and improved in­ventory control using machine learning techniques. The pro­posed inventory prediction system leverages historical sales records, inventory reports, and seasonality factors to detect con­sumer demand trends and generate predictions. The develop­ment of the proposed system is implemented using Python li­braries such as Pandas, NumPy, and Scikit-learn. Supervised learning algorithms including linear regression and decision trees are utilized to forecast future consumer demand. More­over, the proposed inventory prediction system is capable of performing multiple tasks apart from generating demand fore­casts, such as monitoring inventory levels, sending automated alerts, and visualizing inventory management data in an inter­active dashboard. According to the experimental evaluation re­sults, the proposed system can achieve a predictive accuracy of 80–90% based on the quality of training data sets. In addition, the proposed system can significantly reduce inventory man­agement issues such as over-stocking and under-stocking. The proposed solution is lightweight, inexpensive, and specifically tailored to small enterprises, allowing for advanced inventory optimization without the need for infrastructure.