Partial Least Squares Regression

Partial Least Squares Regression (PLSR), developed by Herman Wold in the 1960s, is a multivariate statistical method designed to handle datasets with more variables than observations and high multicollinearity. It combines features of Principal Component Analysis and multiple regression by finding a set of orthogonal latent variables that maximize the covariance between predictors (X) and responses (Y). In enterprise risk management, while standards like ISO/SAE 21434 for automotive cybersecurity do not mandate specific algorithms, they require robust Threat Analysis and Risk Assessment (TARA). PLSR is a powerful tool to fulfill this by modeling complex relationships in high-dimensional data (e.g., CAN bus traffic) to predict anomalies. Furthermore, standards like ASTM E1655 explicitly list PLS as a standard practice for multivariate analysis, validating its technical use.

Practical application of PLSR in ERM involves three key steps. 1) Problem Definition & Data Aggregation: Define a risk objective, such as predicting electric vehicle battery thermal runaway, and collect relevant high-dimensional predictor (e.g., cell voltages, temperatures) and response data. 2) Model Development & Validation: Build the PLSR model and use cross-validation to determine the optimal number of latent variables, ensuring predictive accuracy and preventing overfitting. 3) Deployment & Monitoring: Deploy the validated model onto an ECU or cloud platform for real-time analysis, triggering alerts when risk scores exceed thresholds. A leading EV manufacturer uses this method to increase early warning time for thermal runaway, achieving a measurable outcome of over 30% improvement in risk prediction accuracy.

Taiwanese enterprises face three primary challenges. First, Data Quality and Integration: Data from complex automotive supply chains often lack standardization. The solution is to establish a unified data governance framework aligned with standards like ISO 8000. Second, Talent Gap: A shortage of professionals with hybrid skills in statistics, machine learning, and automotive engineering. This can be mitigated through internal upskilling and partnerships with expert consultants. Third, Model Interpretability for Compliance: The abstract nature of PLSR's latent variables makes explaining model logic to regulators difficult. Using techniques like Variable Importance in Projection (VIP) scores and maintaining meticulous documentation in line with ISO/SAE 21434 requirements are key solutions.

Winners Consulting specializes in partial least squares regression for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact