Abstarct: In today’s industrial environment, organizations face challenges such as global competition, rising costs, and increasing customer expectations regarding quality. Statistical Quality Control (SQC), as a fundamental tool, plays a significant role in ensuring process stability and reducing variability in manufacturing systems. Traditional control charts distinguish between random and special variations, thereby enabling the identification of process deviations and the implementation of corrective actions. However, these charts are predominantly univariate and are not capable of simultaneously and comprehensively analyzing the complex interactions among multiple quality characteristics. This study presents a novel frxamework for designing multivariate control charts baxsed on an Opportunity Loss-baxsed Polar Coordinate Distance approach. By transforming opportunity losses into distances within a polar coordinate space, the proposed method provides a more accurate evaluation of product quality status. Unlike conventional control charts with symmetric control limits, the proposed approach allows for asymmetric limits and demonstrates higher sensitivity in detecting small process variations. The primary objective of this research is to design and evaluate these control charts and to compare their performance with established multivariate methods, including Hotelling’s T², MEDMAE, Minimum Covariance Determinant (MCD), and Minimum Volume Ellipsoid (MVE). In a case study conducted in a biscuit manufacturing company, real industrial data—including weight, moisture content, taste, sugar percentage, pH level, microbial load, surface uniformity, color, thickness, texture crispness, aroma, and fat content—were analyzed. The results indicate that the proposed approach improves model accuracy and exhibits strong capability in detecting small quality variations. Furthermore, the direct use of raw data without preprocessing demonstrates the sensitivity and practical effectiveness of the method in industrial applications, particularly in the food industry.