MATHEMATICAL MODELS FOR DEMAND FORECASTING IN PROCUREMENT: BALANCING INVENTORY AND AVOIDING STOCKOUTS

This study investigates the application of mathematical models for demand forecasting in procurement, aiming to balance inventory levels and prevent stockouts. The research employs a mixed-methods approach, combining quantitative analysis from peer-reviewed studies (2020-2024) with qualitative insights from procurement professionals. Key mathematical techniques include regression analysis, machine learning algorithms, and time series models. Findings reveal a strong correlation (0.98) between forecasted and actual demand, validating the effectiveness of mathematical forecasting. However, regression analysis indicates that merely increasing procurement spending (slope = 0.112) does not significantly reduce stockout costs, highlighting inefficiencies in inventory management. A Chi-Square test (χ² = 5.80, p = 0.215) shows no statistically significant variance in stockouts across years, though 2023 and 2024 exhibited increased shortages, indicating the need for enhanced forecasting strategies. The study concludes that integrating predictive analytics with real-time inventory tracking enhances procurement resilience. Recommendations include adopting AI-driven forecasting, diversifying suppliers, and implementing ERP-integrated models to optimize stock levels.