Why Machine Learning Matters in Process Manufacturing

As process manufacturing becomes more automated and digitally integrated, the volume and complexity of process data has exploded. Sensors log thousands of variables in real time. Metrics are tracked across shifts, batches, and machines. Traditional statistical methods—while still valuable—sometimes fall short in handling the scale, messiness, and nuance of this data.

This is where Machine Learning (ML) steps in, and Minitab’s Predictive Analytics can support you. In short, ML enables manufacturers to uncover patterns, predict outcomes, and optimize performance in ways that weren’t possible before. Unlike classical regression, ML doesn’t require strict assumptions about data structure. It learns directly from real-world examples—handling multicollinearity, lagging effects, nonlinear behavior, and more.

In classical modeling, the aim is to define mathematical relationships between input variables (X’s) and output variables (Y’s). But in many processes, the underlying function is too complex—or unknown. ML doesn’t try to guess the formula. It learns patterns directly from data, using example after example to build a model that predicts Y when given new X values. This makes it ideal for manufacturing environments, where processes are intricate and variable interactions are hard to define. ML learns without requiring a human to pre-specify the rules.

Here are six common data analysis traps that Minitab’s Predictive Analytics suite is suited to combat. We still encourage all practitioners at the Black Belt and Master Black Belt level to be fully comfortable with multiple regression techniques before utilizing ML. Our aim is to support practitioners to condense the number of plausible input variables to the significant few for further exploration via Design of Experiments, which is well supported by Minitab.

What is Predictive Analytics? Watch to learn more. 

The six traps

Trap #1: Dirty Data

Historical data may be contaminated with extreme values, outliers and missing values. These issues create problems estimating reliable regression equation coefficients.

• Extreme Values – a single value, X_i, may be far from the rest of the data; if this is the case, X_i may exert high leverage on the estimation of regressions.
• Outliers – X_i may not be far from the other X values, but the model’s residual (actual - prediction) may be large and greater than 3 standard deviations assuming the residuals are normally distributed with an overall average = 0.
• Missing Values – in Stepwise and Best Subsets Regression, entire rows of data will be eliminated if any chosen predictor (X) has a missing value in the row.

Trap #2: Big Data

The size of the data is related to the number of rows and the number of columns.

• If the number of predictors (p) is large relative to the number of observations (n), then this becomes very complex, or even computationally impossible, for classical regression.
• In classical regression, n must be larger than p in order to estimate the model error (s) and compute P-values for each predictor. In the absence of estimate model error (s), there is no r-squared value.
• Without r-squared and residuals, we cannot know if the regression equation models the data well.

Trap #3: Multicollinearity

When the inputs (Xs) are correlated (dependent) with each other. Correlation coefficients between two predictors greater than 0.5 are signs of trouble.

• The classical regression session window provides information about multicollinearity.
• Variance Inflation Factor (VIF) – measures how much the variance of an estimated regression coefficient increases if predictors are correlated. VIF = 1 / (1 – r²). If VIF > 5, this could be a serious problem for the model.
• R-squared and R-squared (adjusted) – Adding correlated predictors in a classical regression model causes these values to diverge. R-squared (adjusted) penalizes the modeler from including predictors which are correlated to other predictors already present in the model.

Trap #4: Interactions

When the influence of one predictor (X₁) depends on the setting of a second independent predictor (X₂).

• Interactions Increase Model Terms – Mathematically, the number of interactions increases exponentially with the number of predictors. Interactions may be 2-way, 3-way, 4-way, etc. In practice, 2-way interactions are frequent, but higher order interactions are rare.
• Global vs Local Interactions – Classical regression forces interactions to be global; if an interaction is discovered to be significant, it must occur equally across all dimensions of the predictor space. Localized interactions may occur in industry but are difficult to model with classical regression.

Trap #5: Non-Linearity

Classical regression is ‘linear’ by design. The common linear regression expression is Y = mx + b. This basic formula can be extended to other types of linear equations. For example, X² is a linear function. However, 2^X is not a linear function. For a function to be linear, it must be linear in the exponents.

Non-linear functions cannot be modeled with simple regression, stepwise regression, or best subsets regression. If non-linearity is expected, the user must supply the underlying non-linear relationship or choose from among several alternatives.

ML assumes all X-Y relationships are non-linear. This assumption means that even linear functions can be modeled in straight-forward fashion with ML algorithms. The user does not need to have knowledge of the appropriate non-linear function to proceed with ML.

Trap #6: Lagging Effects

In the analysis of continuous process manufacturing data, the analyst must frequently create or shift each predictor (X) forward in time to match the expected response (Y). While classical regression can handle lagging effects as well, ML models often do a better job of accommodating them.

For example, a chemical process has one important predictor (X) of a response variable (Y). The nominal residence time of the process is 4 hours. If the operator makes a change in X, the response variable (Y) changes 4 hours after the change in X. Of course, this simple example makes some big assumptions. Sometimes plug-flow processes aren’t exactly plug-flow and back mixing plays a role. Sometimes, the effect of the change in X spreads out over time vs the response in Y. In these situations, it is necessary to evaluate multiple time shifts of the predictor (X).

From Traps to Transformation

Traditional methods remain valuable, but they aren’t always built for the scale and complexity of modern process manufacturing data. Machine Learning in Minitab’s Predictive Analytics helps overcome these challenges by handling non-linearity, lagging effects, and messy real-world variables automatically. With it, you can move beyond simply analyzing your data to predicting outcomes, preventing failures, and optimizing performance with confidence.

Ready to turn your data into decisions? Contact Minitab today.