Optimization of Aggregate Planning Using Linear Regression: An Integrated Approach to Demand Forecasting, Production Capacity, and Inventory Management

Abstract

Integrated planning plays a key role in production management, through which it is improved resource allocation, workforce utilization, inventory levels, and production capacity to meet forecasted demand while minimizing costs. This study examines the integrated planning framework for a manufacturing environment, focusing on the relationships between demand forecasting, production capacity, inventory management, and total cost optimization. Using a dataset of 300 observations, descriptive statistical analysis reveals significant variability in demand forecasts and production capacity, with mean values of 2973.9 and 3015.7 units respectively. Linear regression modeling demonstrates strong predictive performance, it has achieved an R² value of 0.9159 for the training dataset and 0.8028 for the test dataset.Correlation analysis identifies inventory level as the primary cost driver, showing a strong positive correlation of 0.81 with total cost, while demand forecast exhibits a moderate correlation of 0.41, and production capacity shows minimal direct impact at 0.24. The findings underscore the importance of balanced decision-making in aggregate planning, where effective coordination between forecasting accuracy, capacity alignment, and inventory optimization is essential for cost control and operational efficiency. This research contributes to the development of data-driven decision support systems for sustainable supply chain management. Key Words: Aggregate Planning, Linear Regression, Demand Forecasting, Inventory Optimization, Production Capacity