# Production Process Characterization Terms | A Guide

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July, 2020

*Updated October 2020*

Because Production Process Characterization (PPC) synthesizes the fields of data science, experimental design, and risk management, it is described by terms derived from all three of these fields. Here, we’ll map out a concise list of the key terminolgy involved in PPC to serve as an aid to further reading on this extensive subject. To use this Production Process Characterization Terms guide, simply scroll down to read the definition of each term or use the Contents listing on the left hand side of the page to jump to a specific PPC term.

**Black Box: ** A process model that maps all inputs (e.g. equipment settings, environmental variables, recipes) and their interaction with outputs (e.g. measurable characteristics like thickness or homogeneity) for the purpose of observing their relationships to one another.

**Controlled Variation:** A consistently stable pattern of variation over time, characterized by uniform fluctuation.

**Distribution: ** The measures of location, spread, and shape from a given data set, modeled graphically or numerically.

**Error Propagation: **A linear model of the sources of variation and their contribution to process errors.

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**Factors: **Controlled and uncontrolled inputs that explain response behavior.

**Fishbone:** A process diagram that maps the complexity of a process by listing out the general categories (e.g. machines, materials, measurements, and methods) that may influence the measurable characteristics of a product.

**Location:** The expected value of the measured output.

**Mapping: **A map of outputs over their expected operating ranges, tied to a series of detailed experiments.

**Measurement: **A discrete, sequential variable with infinite range of possible variables (e.g. thickness, temperature, pressure, or particulate count).

**Modeling:** The method of graphically and mathematically representing relationships.

**Nominal: **A discrete, non-sequential variable with a finite range of possible variables (e.g. high/medium/low, operators, shifts).

**Passive:** The step in PPC wherein the process is allowed to run for the purpose of estimating stability and capability.

**Populations and Sampling:** A relationship between the entire set of potentially relevant data and the actually observed and measured data. When the characteristics of the sample can be used to predict the characteristics of the population, the sample can be considered adequate.

**Process Variability:** A classification of how variation characterizes the range of data values, used to quantify process stability and capability.

**Response:** Process outputs taken from a sample.

**Screening:** The step in PPC wherein all *potential* process inputs and outputs are identified, and used to conduct a set of experiments to determine which of those inputs and outputs are key to the characterization process.

**Shape:** A model that describes the distribution of variation, used to determine whether variation is symmetric, skewed, or multimodal.

**Spread:** The expected variation associated with the output, used to determine the range of expected values.

**Stable Process: **A process that runs in a consistent manner that can be reliably predicted.

**Uncontrolled Variation:** A variation characterized by an unpredictable pattern of change.

The overarching goal of PPC is to eliminate uncontrolled variation, and emerge with a mathematical process model for monitoring and improving the production process. ere

With a strong understanding of the impact of each parameter onto product quality and yield, processes can be granularly controlled and optimized for highly predictable results.

To accomplish Production Process Characterization using an industrial measurement system, a Red Meter may be used.

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