COST VOLUME PROFIL ANALYSIS

Abstract

Cost-Volume-Profit (CVP) analysis is a fundamental financial tool used by businesses to understand the interrelationship between cost structures, sales volume, and profitability. Traditionally, CVP analysis relies on static models and assumptions such as linear cost behavior and fixed selling prices. However, in the era of big data and dynamic markets, traditional approaches often fall short in capturing real-time fluctuations and non-linear patterns. This study leverages the power of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to enhance CVP analysis by incorporating predictive analytics and real-time business intelligence. Using historical financial data, operational metrics, and sales records, this study applies ML algorithms—such as regression analysis, decision trees, and support vector machines—to model and forecast break-even points, profit margins, and contribution margins under varying cost and volume scenarios. In addition, Deep Learning models, particularly Long Short-Term Memory (LSTM) networks, are used to capture temporal trends and seasonality in sales and cost behavior. These intelligent systems provide more accurate, adaptable, and data-driven insights for strategic decision-making, enabling businesses to optimize pricing, production, and financial planning in volatile markets. The results demonstrate that AI-enhanced CVP analysis leads to more resilient and informed business strategies in today's data-rich environment.