Linear Regression Analysis in Predicting the Amount of Stock of HP Sparepart Goods in GMT

Gilang Aryudha
Hasibuan, Wilda Rina

Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Vol. 4 No. 1 (2024): October 2024

The rapid advancement of the digital era has made smartphones an essential part of daily life, making the availability of high-quality spare parts crucial for their seamless operation. GMT, a store specializing in smartphone spare parts, faces challenges in predicting fluctuating consumer demand, often leading to either stock shortages or excesses. To address this issue, this research develops a stock prediction system based on linear regression, which analyzes sales data to accurately forecast stock needs. The implementation of this method has resulted in improved accuracy in stock management, enabling GMT to optimize inventory, minimize potential losses, and enhance both customer satisfaction and operational efficiency.