Supply chain management is a critical aspect of modern businesses, with companies striving to optimize their operations for efficiency and profitability. Accurate forecasting and prediction play a pivotal role in achieving these objectives. The study investigates the use of the Analytic Hierarchy Process (AHP) as a robust decision-making tool in supply chain forecasting and prediction. The core of this study involves the development of an AHP-based forecasting and prediction framework tailored to the supply chain domain. AHP is a systematic approach that enables decision-makers to evaluate various forecasting models using a hierarchy of criteria, sub-criteria, and alternatives. The framework also enables the incorporation of expert opinions, historical data, and real-time information, ensuring a comprehensive and adaptable approach to forecasting. Case studies and empirical evidence are presented to demonstrate the effectiveness of the AHP-based framework in improving supply chain forecasting accuracy and decision-making. These examples showcase how AHP can assist in demand forecasting, inventory management, supplier selection, and other critical supply chain activities
