Engineering as a Decision Science: Data-Driven Problem Solving
Gone are the days of gut-based troubleshooting. Today, engineering thrives on data—structured, statistically significant, and timely. Treating engineering as a decision science means moving from intuition to information.
Statistical Thinking: Understanding variance, confidence intervals, and hypothesis testing is crucial in diagnosing process failures or shifts.
Correlation vs. Causation: Tools like regression analysis and ANOVA (Analysis of Variance) help discern whether changes in output are linked to actual process parameters or just noise.
Machine Learning in Engineering: Supervised learning models such as decision trees and support vector machines are increasingly used to predict tool failures, optimize settings, and uncover hidden patterns in production data.
STEMP Solutions Perspective
We build data pipelines that combine shop floor signals with statistical insight. Whether you’re struggling with latent defectivity or wafer-to-wafer variability, STEMP helps convert your process data into confident decisions.
