Abstract

Small and medium-sized enterprises (SMEs) play a pivotal role in global economic growth yet face significant challenges in managing inventory effectively due to resource constraints and market volatility. Understanding stock movement trends is essential for optimizing inventory levels, reducing holding costs, and improving operational efficiency. This paper presents a comprehensive review of existing statistical models applied to stock movement analysis within SMEs, synthesizing insights from inventory theory, time series analysis, and demand forecasting. Drawing upon over one hundred scholarly sources, the study identifies key variables influencing stock trends and evaluates statistical techniques including ARIMA, exponential smoothing, regression analysis, and Markov chains. The paper proposes a conceptual statistical modeling framework tailored to SME contexts, emphasizing adaptability, accuracy, and computational feasibility. This framework aims to support inventory managers in decision-making processes by enhancing the understanding of stock dynamics in fluctuating demand environments. The study contributes to bridging the gap between theoretical models and practical SME applications, highlighting avenues for future empirical research.