SPC Glossary: H, I, J, K

Your quick reference to statistical process control for manufacturing quality management systems.

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Quality Management System Glossary: H, I, J, K

H

Histogram

A diagram consisting of rectangles whose area is proportional to the frequency of a variable and whose width is equal to the class interval. Gives a rough sense of the density of the underlying distribution of the data and is often used for density estimation—that is, estimating the probability density function of the underlying variable. The total area of a histogram used for probability density is always normalized to 1.

Hypothesis Testing

A procedure that is used on a sample from a population to investigate the applicability of an assertion (inference) to the entire population. Hypothesis testing can also be used to test assertions about multiple populations using multiple samples.

I

Imperfection

A quality characteristic’s departure from its intended level or state without any association to conformance to specification, requirements, or to the usability of a product or service. Also see Blemish, Defect, and Nonconformity.

In-Control Process

A process in which the statistical measure being evaluated is in a state of statistical control; in other words, the variations among the observed sampling results can be attributed to a constant system of chance causes. Also see Out-of-Control Process.

Individual

A single unit or a single measurement of a quality characteristic, usually denoted as X. This measurement is analyzed using an Individuals Chart, CUSUM, or EWMA chart.

Individuals Chart

Also called an I-chart or X-chart.
A control chart for processes in which individual measurements of the process are plotted for analysis.

Inspection

A verification activity.
For example, measuring, examining, testing, and gauging one or more characteristics of a product or service and comparing the results with specified requirements to determine whether conformity is achieved for each characteristic.

Inspection Cost

The cost associated with inspecting a product to ensure it meets the internal or external customer’s needs and requirements; an appraisal cost.

Inspection Lot

This is the lot or batch of product to be inspected for acceptance.

Inspection States

>In the ANSI/ASQ and ISO Acceptance Sampling Standards there are three Inspection States (or statuses): Normal, Tightened, and Reduced. The definitions for each state are found in the applicable standard under a heading called Switching Rules.

Normal: Per the ANSI/ASQ z1.4, “Normal inspection will be used at the start of inspection unless otherwise directed by the responsible authority.”
Normal to Tightened: Per the ANSI/ASQ z1.4, “When Normal inspection is in effect, tightened inspection shall be instituted when 2 out of 5 or fewer consecutive lots or batches have been non-acceptable on original inspection.”
Tightened to Normal: Per the ANSI/ASQ z1.4, “When tightened inspection is in effect, normal inspection shall be instituted when 5 consecutive lots or batches have been considered acceptable on original inspection.”
Normal to Reduced: This switching rule requires several different things to happen. When normal inspection is in effect and the following apply, the state may change to Reduced if:

The preceding 10 lots or batches (or more) have all been accepted on original inspection
The total number of nonconforming units in the samples from those 10 preceding lots is equal to or less than the applicable limit number given (depending on the standard)
Production is at a steady rate
Reduced inspection is desired by responsible authority

International Organization for Standardization (ISO)

An independent, nongovernmental international organization with a membership of 161 national standards bodies that unites experts to share knowledge and develop voluntary, consensus-based, market-relevant international standards, guidelines, and other types of documents.

ISO 9000 Series Standards

A set of international standards on quality management and quality assurance developed to help organizations effectively document the quality system elements to be implemented to maintain an efficient quality system. The standards, initially published in 1987, are not specific to any particular industry, product, or service. The standards were developed by the International Organization for Standardization (ISO). The standards underwent major revision in 2000 and now include ISO 9000:2005 (definitions), ISO 9001:2008 (requirements), ISO 9004:2009 (continuous improvement) and ISO 9001: 2015 (risk management).

ISO 9001

A voluntary quality management system standard developed by the International Organization for Standardization (ISO). First released in 1987 and one of several documents in the ISO 9000 family.

J

Just-In-Time Manufacturing (JIT)

Also known as Just-In-Time Production.
A methodology aimed primarily at reducing flow times within a production system, as well as response times from suppliers and to customers.

K

Key Process Characteristic

A process parameter that can affect safety or compliance with regulations, fit, function, performance or subsequent processing of product.

Key Product Characteristic

A product characteristic that can affect safety or compliance with regulations, fit, function, performance or subsequent processing of product.

Kruskal-Wallis Test

A non-parametric test for determining whether samples originate from the same distribution. It is used for comparing two or more independent samples of equal or different sample sizes. While analysis of variance tests depends on the assumption that all populations under comparison are normally distributed, the Kruskal-Wallis test places no such restriction on the comparison. It is a logical extension of the Wilcoxon Mann-Whitney Test.

SPC Glossary: D, E, F, G

Your quick reference to statistical process control for manufacturing quality management systems.

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Quality Management System Glossary: D, E, F, G

D

Data

Collected facts.
There are two basic kinds of numerical data: measured or variable data (such as 12 ounces, 10 miles, and 0.50 inches) and counted (or attribute) data (such as 112 defects).

Data Collection and Analysis

The process to determine what data are to be collected, how the data are collected, and how the data are to be analyzed.

Data Collection and Analysis Tools

A set of tools that help with data collection and analysis. These tools include check sheets, spreadsheets, histograms, trend charts, and control charts.

Defect

A product’s or service’s nonfulfillment of an intended requirement or reasonable expectation for use, including safety considerations. There are four classes of defects: Class 1, very serious, leads directly to severe injury or catastrophic economic loss; Class 2, serious, leads directly to significant injury or significant economic loss; Class 3, major, is related to major problems with respect to intended normal or reasonably foreseeable use; and Class 4, minor, is related to minor problems with respect to intended normal or reasonably foreseeable use. Also see Blemish, Imperfection, and Nonconformity.

Defective

A unit of product that contains one or more quality characteristic defects.

Deming Cycle

Also known as the Plan-Do-Study-Act cycle, popularized by W. Edwards Deming.
Also see Plan-Do-Check-Act Cycle.

Deviation

The difference or distance of an individual observation or data value from the center point (often the mean) of the set distribution.

Distribution

A mathematical model that relates the value of a variable with the probability of the occurrence of that value in the population.

DMAIC

Also known as Six Sigma DMAIC.
Define, Measure, Analyze, Improve, and Control. A data-driven quality strategy for improving processes, and an integral part of a Six Sigma quality initiative.

E

EWMA Charts

Also known as EWMA Control Charts.
An Exponentially Weighted Moving Average control chart uses current and historical data to detect small changes in the process. Typically, the most recent data are given the most weight, and progressively smaller weights are given to older data.

F

F Distribution

The F distribution is the probability distribution associated with the F statistic.

F Statistic

An F statistic is a value you get when you run an Analysis of Variance (ANOVA) test or a regression analysis to find out whether the means between two populations are significantly different.

Failure

The inability of an item, product, or service to perform required functions on demand due to one or more defects.

Feature

See Characteristic.

First Pass Yield (FPY)

Also referred to as the quality rate.
The percentage of units that completes a process and meets quality guidelines without being scrapped, rerun, retested, returned, or diverted into an offline repair area. Calculated by dividing the units entering the process minus the defective units by the total number of units entering the process.

First Time Quality (FTQ)

Also known as First Time Quality Formula.
Calculation of the percentage of good parts at the beginning of a production run.

Fitness for Use

The degree to which a product or service meets the requirements for its intended use.

14 Points

W. Edwards Deming’s 14 management practices to help organizations increase their quality and productivity: 1) Create constancy of purpose for improving products and services; 2) Adopt the new philosophy; 3) Cease dependence on inspection to achieve quality; 4) End the practice of awarding business on price alone; instead, minimize total cost by working with a single supplier; 5) Improve constantly and forever every process for planning, production and service; 6) Institute training on the job; 7) Adopt and institute leadership; 8) Drive out fear; 9) Break down barriers between staff areas; 10) Eliminate slogans, exhortations, and targets for the workforce; 11) Eliminate numerical quotas for the workforce and numerical goals for management; 12) Remove barriers that rob people of pride of workmanship and eliminate the annual rating or merit system; 13) Institute a rigorous program of education and self-improvement for everyone; and 14) Put everybody in the organization to work to accomplish the transformation.

Frequency Distribution (Statistical)

A list, table, or graph that displays the frequency of various outcomes in a sample.

G

Gauge Repeatability and Reproducibility (R&R)

A gauge R&R indicates whether the inspectors are consistent in their measurements of the same part (repeatability) and whether the variation between inspectors is consistent (reproducibility).

Repeatability—How much variability in the measurement system is caused by the measurement device.
Reproducibility—How much variability in the measurement system is caused by differences between operators.
Whether your measurement system variability is small compared with the process variability.
Whether your measurement system is capable of distinguishing between different parts.

Geometric Dimensioning and Tolerancing (GD&T)

A language of symbols and standards designed and used by engineers and manufacturers to describe a product and facilitate communication between entities working together to produce something.

Go/No-Go

State of a unit or product. Two parameters are possible: Go (conforms to specifications) and No-Go (does not conform to specifications).

Green Belt (GB)

See Six Sigma Green Belt.

Statistical Process Control 101

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Statistical Process Control

Have you heard about statistical process control (SPC) but aren’t quite sure what it is or how it could improve your bottom line? We’ve put together this short guide to answer some of the most common SPC manufacturing questions.

Statistical Process Control (SPC) Definition

At its most basic, statistical process control (SPC) is a systematic approach of collecting and analyzing process data for prediction and improvement purposes. SPC is about understanding process behavior so that you can continuously improve results.

As you learn about SPC, you’ll encounter terms that describe central tendency:

Mean: the arithmetic average of a set of collected values
Mode: the value that occurs most often within a set of collected values
Median: the value that defines where half a set of collected values is above the value and half is below

You will also come across terms that describe the width or spread of data:

Variation: a term used to describe the amount of dispersion in a set of data
Range: a measure of dispersion that is equal to the maximum value minus the minimum value from a given set of data
Standard deviation: a measure used to quantify a data set’s dispersion from its mean value

SPC Origins: Shewhart Statistical Process Control (SPC) Charts

Dr. Walter A. Shewhart (1891–1967), a physicist at Bell Labs who specialized in the use of statistical methods for analyzing random behavior of small particles, was responsible for the application of statistical methods to process control. Up until Shewhart, quality control methods were focused on inspecting finished goods and sorting out the nonconforming product.

As an alternative to inspection, Shewhart introduced the concept of continuous inspection during production and plotting the results on a time-ordered graph that we now know as a control chart. By studying the plot point patterns, Shewhart realized some levels of variation are normal while others are anomalies.

Using known understandings of the normal distribution, Shewhart established limits to be drawn on the charts that would separate expected levels of variation from the unexpected. He later coined the terms common cause and assignable cause variation.

Dr. Shewhart concluded that every process exhibits variation: either controlled variation (common cause) or uncontrolled variation (assignable cause). He defined a process as being controlled when “through the use of past experience, we can predict, at least within limits, how the process may be expected to vary in the future.”

He went on to develop descriptive statistics to aid manufacturing, including the Shewhart Statistical Process Control Chart—now known as the X-bar and Range (Xbar-R) chart. The purpose of the Shewhart Statistical Process Control Chart is to present distributions of data over time to allow processes to be improved during production. This chart changes the focus of quality control from detecting defects after production to preventing defects during production.

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SPC Glossary: C

Your quick reference to statistical process control for manufacturing quality management systems.

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Quality Management System Glossary: C

Calibration

The comparison of a measurement instrument or system of unverified accuracy to a measurement instrument or system of known accuracy to detect any variation from the required performance specification.

Capability

The total range of inherent variation in a stable process; determined by using data from control charts.

Cause

An identified reason for the presence of a defect or problem.

Cause-and-Effect Diagram

Also called a fishbone diagram or an Ishikawa diagram (after its developer).

A quality control tool used to analyze potential causes of problems in a product or process.

C-Chart

See Count Chart.

Centerline

A line on a graph that represents the overall average (mean) operating level of the process.

Central Limit Theorem

Also known as Central Limit Theorem Formula.

An important statistical theorem that states that subgroup averages tend to be normally distributed even if the output overall is not. This concept allows control charts to be widely used for process control even if the underlying process is not normally distributed.

Central Tendency

Also known as Measures of Central Tendency.

The tendency of data gathered from a process to cluster toward a middle value somewhere between the high and low values of measurement.

Characteristic

A factor, element, or measure that defines and differentiates a process, function, product, service, or other entity.

Chart

A tool for organizing, summarizing, and depicting data in graphic form.

Check Sheet

A simple data recording device. The check sheet is custom-designed by the user, which allows him or her to readily interpret the results.

Classification of Defects

The listing of possible defects of a unit, classified according to their level of severity. Commonly used classifications include: A, B, C, or D; critical, major, minor, or incidental; and critical, major, or minor. A separate acceptance sampling plan is generally applied to each class of defects.

Common Cause

Cause of variation that is inherent in a process over time. A common cause affects every outcome of the process and everyone working in the process. Also see Special Cause.

Consumer’s Risk

Pertains to sampling and the potential risk that bad products will be accepted and shipped to the consumer.

Continuous Flow Process

A method of manufacturing that aims to move a single unit in each step of a process, rather than treating units as batches for each step.

Continuous Improvement (CI)

Also known as Continuous Quality Improvement and Continual Improvement.

The ongoing improvement of products, services, or processes through incremental (over time) and/or breakthrough (all at once) improvements.

Continuous Sampling

Used when the product is manufactured in a continuous flow and is not able to be grouped into lots (batches). Two parameters are considered: Frequency (f) and Clearing Number (i). This is a progressive type of plan in which the Clearing Number is X (example = 60) and the frequency is 1/X (example = 1/20). The manufacturer inspects 100 percent of the product until (i)=60 is reached. If defect-free, the Frequency (example = 1/20) applies and now every (f)=20th sample is inspected. If at least one defect is found in the first (i)=60, 100-percent inspection continues until the Clearing Number is reached.

Control Chart

A graph used to study how a process changes over time. Frequently shows a central line to help detect a trend of plotted values toward either upper or lower Control Limit.

Control Limit

Also known as Process Control Limit and Natural Process Limit.

The boundaries of a process within specified confidence levels expressed as the Upper Control Limit (UCL) and the Lower Control Limit (LCL).

Control Plan (CP)

Written descriptions of the systems for controlling part and process quality by addressing the key characteristics and engineering requirements.

Corrective Action

A solution meant to reduce or eliminate an identified problem.

Correlation (Statistical)

A measure of the relationship between two data sets of variables.

Costs of Poor Quality (COPQ)

The costs that would disappear if systems, processes, and products were perfect. These costs are organized into four categories: internal failure costs (costs associated with defects found before the customer receives the product or service); external failure costs (costs associated with defects found after the customer receives the product or service); appraisal costs (costs incurred to determine the degree of conformance to quality requirements); and prevention costs (costs incurred to keep failure and appraisal costs to a minimum).

Cost of Quality (COQ)

A means to quantify the total cost of quality-related efforts and deficiencies. Considered by some to be synonymous with COPQ.

Count Chart

A Control Chart for evaluating the stability of a process in terms of the count of events of a given classification occurring in a sample. Commonly referred to as a c-chart.

Count Data

See Attribute Data.

Count-Per-Unit Chart

Also known as a u-chart.

A type of control chart used to monitor count-type data where the sample size is greater than one, typically the average number of nonconformities per unit.

Cp

A measure of dispersion, sometimes described as the engineering tolerance divided by the natural tolerance. The ratio of tolerance to 6 sigma (i.e., the Upper Specification Limit [USL], minus the Lower Specification Limit [LSL], divided by 6 sigma.

Cpk Index

Also known as Process Capability Index.

Equals the lesser of the Upper Specification Limit minus the mean divided by 3 sigma or the mean minus the Lower Specification Limit divided by 3 sigma. The greater the Cpk value, the better.

Cumulative Sum Control Chart (CUSUM)

A type of control chart used to monitor small shifts in the process mean. It uses the cumulative sum of deviations from a target.

SPC Glossary: A-B

Your quick reference to statistical process control for manufacturing quality management systems.

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Quality Management System Glossary: A-B

A

Acceptance Number

The maximum number of defects or defectives allowed in a sample from a lot (batch) of product to consider that lot acceptable.

Acceptance Quality Limit (AQL)

Also known as Acceptable Quality Limit, Acceptance Quality Level, Acceptable Quality Level, or AQL level.

The AQL is the lowest tolerable average (mean) of a process in percentage or ratio that is still considered acceptable.

Acceptance Sampling

A method of inspection in which statistical sampling of a lot (batch) of product is used to determine whether that lot of product is acceptable. Acceptance sampling comprises two types: attribute sampling and variable sampling.

Attribute sampling is a statistical method by which the lot is accepted or rejected based on one sample. The sample is recorded as a pass or fail depending on the number of defects or defectives found within that sample when compared to the Acceptance Number. Attribute inspections are typically subjective (visual) interpretations of the product.
Variable sampling is similar to attribute sampling. However, rather than recording the number of defects, each piece within the sample from the lot of product is measured and those values are assessed against a specification limit. The result of that assessment may indicate the lot passes or fails. Sample sizes for variable sampling are usually smaller than attribute sampling because measurements are more accurate than subjective interpretation.

Acceptance Sampling Plan

The specific criteria by which a product is to be examined for acceptance utilizing Acceptance Sampling methods. The size of the lot (batch) of product combined with the Acceptance Quality Limit, as well as other considerations (depending on the plan being used and the characteristics being inspected), determine the sample size as well as the acceptance number. Some of the most commonly used standards today are ANSI/ASQ z1.4 (Attributes), ANSI/ASQ z1.9 (Variables), Lot Tolerance Percent Defective (LTPD), and Zero Acceptance Number (as described by Nicholas Squeglia in Zero Acceptance Number Sampling Plans, ASQC Quality Press).

Accuracy

The difference of agreement between an observed value and an accepted reference value.

Analysis of Means (ANOM)

A statistical procedure for troubleshooting industrial processes and analyzing the results of experimental designs with factors at fixed levels. When you need to compare multiple group means, you can use the ANOM as an alternative to the one-way analysis of variance F.

Analysis of Variance (ANOVA)

Also known as Variance Analysis, ANOVA Variance, ANOVA Analysis.

A basic statistical technique for determining the proportion of influence that a factor, or set of factors, has on total variation. ANOVA tests for differences between means; it’s similar to many other tests and experiments in that its purpose is to determine whether the response variable (i.e., your dependent variable) is changed by manipulating the independent variable.

AS9100

Also known as AS9100 Standard and AS9100 Quality.

A widely adopted and standardized quality management system for the aerospace industry. It is known as EN9100 in Europe and JISQ9100 in Japan.

Assignable Cause

Also known as Assignable Cause Variation.

An identifiable, specific cause of variation in a given process or measurement. Also see Special Cause.

Attribute Data

Also known as Go/No-Go information.

Qualitative data that can be counted for recording and analysis. Control charts based on attribute data include: percent chart, number of affected units chart, count chart, count-per-unit chart, quality score chart, and demerit chart. Also see Go/No-Go.

Average

See Mean.

Averages Chart

Also known as Averages Control Chart.

A control chart in which the subgroup average, X-bar, is used to evaluate the stability of the process level. Also see X-Bar Chart.

Average Outgoing Quality (AOQ)

Also known as Average Outgoing Quality Formula.

The expected average quality level of an outgoing product for a given value of incoming product quality. Depends on the incoming quality, the probability that the lot will be accepted, and the sample and lot sizes.

Average Outgoing Quality Limit (AOQL)

Represents the maximum percent defective in the outgoing product. AOQL is the maximum average outgoing quality over all possible levels of incoming quality for a given acceptance sampling plan and disposal specification.

Average Run Lengths (ARL)

The number of points, on average, that will be plotted on a control chart before an out-of-control condition is indicated (e.g., a point plotting outside the control limits).

B

Blemish

An imperfection severe enough to be noticed but that should not cause any real impairment with respect to the intended normal, or reasonably foreseeable, use. Also see Defect, Imperfection, and Nonconformity.

Bias

The offset of a measured value from the true population value.

Binomial Distribution

Also known as Binomial Distribution Formula.

A discrete probability distribution used for counting the number of successes and failures or conforming and nonconforming units. This distribution underlies the p-chart and the np-chart.

Box and Whisker Plot

A plot used in exploratory data analysis to picture the centering and variation of the data based on quartiles. After the data are ordered, the 25th, 50th, and 75th percentiles are identified. The box contains the data between the 25th and 75th percentiles.

SPC Glossary

Your quick reference to statistical process control for manufacturing quality management systems.

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Quality Management System Glossary

Every manufacturing quality management professional who uses statistical process control (SPC) runs into questions occasionally. That’s why we’ve compiled this SPC glossary to serve as a quick reference when you’re looking for an answer, need to explain a concept to a colleague—or just can’t remember that term that’s on the tip of your tongue.

Feel free to bookmark this reference so you always have the definition you’re looking for—and be sure to visit our other SPC reference resources.

WHAT IS STATISTICAL PROCESS CONTROL?
Learn the definition of SPC and what this industry-standard methodology is used for.

SPC 101
Dig in deeper to understand why and how SPC is used in manufacturing quality control.

DEFINITIVE GUIDE TO SPC CHARTS
Learn why and how to use different control charts, see examples, and explore use cases.

Measurable Benefits with Real-Time SPC

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Real-Life SPC

At InfinityQS^®, we design and support practical solutions. Our expert industrial statisticians bring Six Sigma Black Belt certification and experience in the areas that matter most:

Deep understanding of how manufacturing works in dozens of industries
Solving the challenges of today’s technical and economic landscape
Supporting the needs of operations, quality, IT, and executive teams

Our customers report measurable improvements and a robust ROI. It’s just one reason InfinityQS has a 97% client retention rate and a 94% client satisfaction rating across thousands of clients and tens of thousands of installations.

Improve Data Collection, Analysis, and Reporting

We support data that many other vendors don’t, including non-normal distributions, short runs, and startup activities.

Our Unified Data Repository stores and processes data so that you can directly compare quality across multiple—
- product codes
- production lines
- production sites
Analyze complex, real-time or historical data within one chart or report.
You don’t need to export and manually manipulate data to support complex analyses.

Real-Life Client Results

14.4% average reduction in data-collection time

17.1% average reduction in reporting time

“Resolving issues we didn’t even know we had.”

“No other system would allow us to integrate real-time process data from disparate systems into MES or launch automated alerts and actions to give our engineers intelligence and feedback. InfinityQS has proven vital in resolving issues we didn’t even know we had.”

Success Story

Reduce Scrap, Waste, and Risk

Easily analyze quality data to optimize processes, minimize waste, and uncover significant savings.

Reduce production scrap and waste.
Improve process capability.
Simplify communications by using automatic notifications.

Real-Life Client Results

12.7% average reduction in weekly scrap14.1% average reduction in warranty claims

12.9% average reduction in defect costs

10.7% average reduction in escapes

66% annual dollar savings from reduced scrap alone.

One InfinityQS customer saw a 66% annual dollar savings from reduced scrap alone.

Success Story

Optimize Manufacturing Operations

We offer both on-premises and cloud-hosted SPC solutions. Get the most from your SPC investment by leveraging InfinityQS training, engineering, and help systems to tailor your deployment to meet your unique needs.

Give your operations team an SPC solution that is easy to learn and use—and that won’t slow down production.
Help your quality team anticipate and prevent quality issues.
Use InfinityQS SPC solutions to minimize IT burden.
Provide your management team immediate insight into what’s happening across the company.
Get a real-time SPC solution that’s easy to use and affordable to try and buy.

Real-Life Client Results

14.1% average reduction in overtime

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14.1% average reduction in overtime

InfinityQS solutions help you turn quality from a problem to a profit center.

Success Story

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How To Use Quality Metrics To Improve Quality Management in Manufacturing

A manufacturing quality platform makes it easier for manufacturers to apply quality control data.

Trusted by hundreds of manufacturing companies around the world:

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Quality Metrics Are a Manufacturing By-product That Could Be a Major Asset.

Manufacturers produce a lot of data—sometimes more than they can collect, and often more than they can use. Having data isn’t a manufacturing problem. Making sense of it is. Manufacturing companies need help identifying their most useful metrics—right now, and for the long run.

A comprehensive approach to quality management answers the data overload problem and brings clarity to the most complex data (and quality) challenges. A quality management discipline standardizes data practices, so leaders have access to data when they need it—and confidence in the quality of that data.

With the right information at hand—pooled together without a herculean effort—managers can spend more time asking questions and exploring possibilities. Leaders can find ways to apply Statistical Process Control (SPC) to optimize quality and manufacturing processes overall. And they can proactively respond to manufacturing opportunities instead of always reacting to challenges.

Learn to use quality metrics to shift your operations into a more proactive quality management mode.

Manufacturing quality metrics on their own are just the tip of the iceberg. What can your data tell you?

Who Uses Quality Metrics—and How? Learn Why Quality Metrics Matter in Manufacturing

Quality metrics demonstrate how the organization is performing on waste reduction, quality control, and responsiveness, as well as other measures that matter to anyone making, using, or investing in your products.

Just like performance, quality metrics can change minute by minute (or second by second). That’s why it’s important for quality management practices to enable real-time visibility. With instant views of quality metrics, production staff and plant managers can make the right decisions in the moment. Likewise, analysts and executives can use the same data to steer the organization toward positive strategic outcomes.

Quality metrics are actionable at every level—if they’re complete, consistent, and accessible.

Complete data means you have everything you need to answer questions and solve problems.
Consistent data is collected and presented in the same way, no matter where it’s collected. Consistency enables comparative analyses—and accurate conclusions.
Accessible data is easy for the people to retrieve, whenever and however they need it—including remotely. The cloud enables companies to centralize their quality control metrics to make them widely available.

Examples of Quality Control Metrics in Action

Does everybody in the organization really need access to real-time, SPC-based metrics?

Yes—although everyone doesn’t need the same control charts. Some users may not need control charts at all. A quality platform consolidates all your quality data into one place, then automatically presents the most pertinent information to users based on role and responsibility.

Here are examples of how SPC-based quality control metrics apply to different roles in your manufacturing organization:

Operational staff, including plant managers and production leaders, use SPC-based data to monitor quality and manage potential issues on the line in real time. They use data and quality analytics tools to manage:

Quality assurance, so final products pass strict inspection standards
Process optimizations that reduce scrap and waste
Productivity, guided by data-driven decisions that decrease downtime, speed changeovers, or increase line throughput

With SPC-driven data, operators can investigate quality issues on the spot—and test new quality initiatives.

Quality control and process improvement professionals use real-time and historical quality metrics to monitor trends. By looking at key quality metrics across lines, products, and locations, they can uncover optimal manufacturing processes for the entire organization. They use quality metrics to improve:

Quality control practices and oversight—even when they’re not physically present
Quality and process standardization by taking empirical best practices and applying them across the organization
Record keeping and other compliance activities

Quality platforms standardize data collection and reporting—making quality data more complete, accurate, and accessible. That makes it more auditable and actionable.

Manufacturing executives rely on accurate quality metrics to guide organizational strategies. When SPC-based quality metrics are accessible, the executive team can:

Improve decision making
Promote the consistency needed to fulfill customer and brand expectations
Reduce cost via substantial, organization-wide process improvements
Increase profits by maximizing throughput, uptime, and quality

Quality control data can answer all these manufacturing needs—using the right quality analytics tools.

Uncover Quality Metrics To Maximize Performance

What would happen if you only read 2% of your emails?

That’s essentially how many manufacturers approach their quality data: they dig into exception data and ignore the vast majority of quality metrics. By doing so, they miss opportunities to optimize manufacturing across the company.

But here’s some good news: Manufacturers already collect the data they need to improve performance. Once it’s standardized and centralized, quality priorities come into focus—and data can be used to proactively improve quality, customer satisfaction, and profitability.

Top Quality Metrics for Manufacturers

Manufacturing companies of every size—in every industry—can benefit from monitoring the following common quality metrics:

Defects: How many defects occur per million parts, or per million opportunities (if you have subassemblies and thus multiple opportunities for failure)?
Customer complaints/returns: How often do customers complain, or reject products over a designated period? Resolution activities and warranty costs are important to measure.
Scrap: How much material is left on the plant floor instead of becoming part of the finished product? And where does scrap originate—from vendors, setup, or other internal processes?
Yield: Yield is a classic measure of process or plant effectiveness. Consider calculating first-pass yield metrics, in addition to total yield, to find out how often products are manufactured correctly the first time.
Overall Equipment Effectiveness (OEE): How productively are you using equipment? OEE measures equipment performance by uptime (availability), output (performance), and how often products are in spec (quality).
Throughput: How many products can you make in a certain timeframe—per machine, line, and plant?
Supplier quality: How often do raw materials meet your quality specifications and requirements? How much do non-conforming materials cost the business?
Delivery: How often are products delivered on time, in perfect condition, and invoiced correctly?
Internal timing: How long does a customer have to wait—from order to delivery? How much time does it take to switch lines, introduce a new product to market, or execute change orders?
Capacity utilization: How does your output compare to capacity?
Schedule realization: How often do you reach production targets?
Audit metrics: Are audits completed on time? How often? And how many non-compliance notices do you receive?
Maintenance metrics: How often is scheduled maintenance completed on time? How many maintenance activities are planned versus emergencies? How much downtime do you experience because of maintenance—and what does that cost?

Measuring the Cost of Quality

Cost of Quality (COQ) is possibly the most important metric because it captures two perspectives: the cost of poor quality and the cost to invest in good quality.

Here’s how: First, COQ accounts for internal failures—such as scrap and rework—and for defects that reach the customer and have to be resolved through warranties, corrections, and adverse event reporting. COQ also tracks proactive audit costs, like product inspections and quality tests, and preventive measures to protect quality—such as SPC, quality planning, and training.

Companies that embrace quality as a discipline spend less on quality—across the entire organization. In fact, quality becomes a key driver for cost avoidance and other strategies that improve profitability.

Asking the Right Questions

If the answers to quality improvement are in the data, then you need to know the right questions to ask. If you’re not sure where to start, try asking your business and operations leaders these four questions:

1. How do you identify your biggest opportunities for process and product quality improvement?
A quality platform centralizes quality data from across your company—making it easier to analyze. Built-in analytics do most of the data aggregation, slicing, and dicing automatically, exposing—in bold colors and charts—where to take action.

2. Once you identify opportunities, how do you prioritize resources to address them?
Based on goals that you establish, a quality platform grades process performance across products, processes, and sites. With report card-like grading systems, quality opportunities are easier to see—and prioritize.

3. How do you know people are collecting the data they are supposed to collect?
How confident are you that data collections are happening on time, every time? On the right form? In the same format? And are you notified when data collections are missed?

Modern quality solutions eliminate these worries—and improve the accuracy of your quality data. Technology standardizes collection methods across the company and calculates results in a standardized manner. An intelligent quality management solution alerts operators when collections are due and notifies mangers when collections are missed. When operators are empowered to stop wasting time babysitting data collection, they can spend more time understanding and applying that data to process improvement.

4. How do you know what your biggest challenges are?
When you have so much data collected, opportunities hide in the blind spots—especially if managers and operators have to dig through control charts or reformat spreadsheets to make them useful. It’s no wonder that quality data is often only evaluated monthly or quarterly; it takes that long just to compile and format it.

A purpose-built manufacturing quality platform automates important calculations and unites them in a dashboard view—in real time. Meaningful information rises to the surface, and leaders can click into supporting details to uncover root cause—or opportunities—so they can start developing resolutions immediately.

See a New Side of Quality

See Enact in action—and how easy it is to activate your quality data using charts, dashboards, and tiles.

Leverage Quality Metrics & Optimize Manufacturing

Modern quality management tools make it easier to extract value from your quality metrics. By uniting your quality data, leaders can view their entire organization in a whole new way.

With more accurate and complete quality data, manufacturers can answer complex questions—and meet different users’ quality needs.

Dashboards are configurable for different roles within an organization. The same data, when explored differently, can solve urgent issues on the plant floor—or pinpoint company-wide best practices.

Take a tour of Enact operator dashboards.

Other visual displays, such as bubble charts, help leaders compare quality metrics across sites. From there, managers can dig into supporting information to explain performance variations (such as on-time data collections or yield) to uncover improvement opportunities.

Learn how it’s possible to prioritize improvement opportunities.

Data Stream Grading is an advanced analytics tool that rolls up quality metrics across an organization—and lets you drill down into specific details. Data Stream Grading measures performance yield against your potential, then assigns it a grade. From there, it’s easy to see quick wins and high-impact projects.

See a Data Stream Grading “report card.”

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Quality Management Principles To Build Your Discipline

Modern SPC tools can help hold you accountable to the highest quality management standards.

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If You Want a Company Culture That Supports Quality, Start with Established Quality Management Principles.

Caring about quality is nice. But it doesn’t improve products, performance, or profit. To do that, you need to establish standards—quality principles—across your organization.

Strong quality principles are supported by data—and provide targets that everyone in the company can pursue.

How do you set quality principles? Industry groups and international quality standards, such as ISO 9001 and ISO 22000, pave the way. Standards and accrediting organizations offer foundational quality management principles, as well as a baseline for quality management.

But they’re not the end state, or even the limit on what you can achieve. When you master quality management, these principles become more than just items on a checklist—they become ingrained in your organization’s culture. Quality sits at the center of all daily activities, as well as big-picture decisions, conversations, and plans.

When quality becomes a part of everything you do—and how you do it—compliance with industry standards is simpler. Learn how to make the principles of quality management an essential part of your workplace culture.

SPC-based quality software can embed quality principles—and empower users—at every level using actionable quality insights.

What Are Quality Management Principles?

Many quality standards and compliance requirements are established externally. Sometimes customers set the bar, but most often industry bodies and action groups establish requirements. The International Organization for Standardization (ISO), for example, issues quality management principles to help manufacturers work more efficiently and reduce product failures.

Standards established by the ISO and others became the “norm,” and often dictate best practices. Their quality management principles influence how things are done—and what customers expect.

Measuring Quality Compliance

Setting standards is a great first step. But without measurement, it’s impossible to make progress. Statistical Process Control (SPC) methodology, which many manufacturers already use to control quality, is an important tool for measuring quality compliance. In fact, some certifications—such as SQF from the Safe Quality Food Institute—require the use of SPC to comply with safety and quality standards.

Why Do Quality Management Principles Matter?

Manufacturers meet external quality standards to achieve certification—and to validate to customers and prospects that they’re operating in the most consistent and productive manner. Standards such as ISO 9001 cover more than just the plant floor—they address how quality permeates leadership, engagement, relationship management, decision making, and more. That’s why quality management principles are an important piece of building a culture around quality.

Start with the Basics: ISO Quality Management Principles

The ISO 9000 family of standards are based on seven quality management principles:

Customer Focus: How manufacturers use quality management to meet or exceed customer expectations. Manufacturers can achieve customer focus by deeply understanding customers’ needs—and by measuring and monitoring customer satisfaction.
Leadership: Leaders create working conditions that support quality and align quality to organizational strategies, policies, and processes. They can also make sure quality initiatives are properly resourced.
People Engagement: People need appropriate training to support quality, but also recognition and empowerment to take initiative toward quality improvement. Engaged workers understand how their individual contributions affect quality performance, and are empowered to speak up, collaborate, and contribute to continuous improvement.
Process Approach: Consistent and predictable results are a key measure of quality. This ISO 9001 principle requires manufacturers to understand and manage interrelated processes in ways that optimize performance. To do this, organizations map out interdependencies and design smooth and reliable manufacturing processes, from start to finish.
Continuous Improvement: This quality management principle is designed to help manufacturers react to changes in the internal and external environment and create new opportunities. Continuous improvement affects process performance, organizational capabilities, and customer satisfaction. It also requires proactive audits, planning, and analyses.
Evidence-based Decision Making: Here, ISO instills data, analysis, and evaluation as manufacturers’ best resources for success. Manufacturers need to understand the cause-and-effect relationships between various inputs and processes—and be able to objectively model consequences. To do this, manufacturers need access to accurate, reliable, and timely information, and the data needs to be available to the right people.
Relationship Management: Manufacturing companies need a network of suppliers, partners, investors, and workers to produce quality products. Those relationships need to be proactively managed so that everyone stays aligned on goals, values, and quality expectations. ISO suggest that companies measure the performance of relevant parties and provide feedback to enhance quality.

Every industry has a set of quality management principles—basic concepts or standards of quality—to comply with. In organizations that master quality, these principles are embedded in daily language and decision making and set the bar for quality.

Common Quality Standards: What Are They? And What Do They Mean?

The ISO establishes quality standards and principles that apply to manufacturers worldwide—regardless of product or output. Manufacturers are expected to follow ISO standards in addition to product-specific or geographic requirements. Food handling, for example, is held to different standards than car parts or computer chips.

The guidance from industry groups can be very specific, even granular. Here are some of the most common quality standards that are applied in manufacturing:

ISO 9001
Some of the ISO’s best-known standards fall under ISO 9001. It applies to manufacturing operations broadly, regardless of company size, location, or industry.

ISO 9001 builds on the seven quality management principles to build efficiencies, meet statutory and regulatory requirements, and put customers first. To achieve ISO 9001 certification, companies must document how they apply, track, and manage ISO’s quality management principles.

ISO 22000
ISO 22000 provides safety standards for the global food supply chain. These standards benefit consumers, of course, but also protect food and beverage manufacturers that work with global growers, suppliers, transport companies, and retailers.

Through Hazard Analysis and Critical Control Points (HACCP), ISO prescribes proactive measures to lower contamination risk and protect food. The seven principles of HACCP are designed to stop hazardous materials from entering the production process—as opposed to identifying them during final inspection.

Good Manufacturing Practice
Good Manufacturing Practice (GMP) provides standards for quality governance in highly regulated industries—such as pharmaceutical and medical device manufacturing, cosmetics, and food and beverage manufacturing. Regulations cover manufacturing process, facilities, and personnel—all to ensure consumer safety. GMP requires equipment and product testing, employee competencies, and thorough documentation.

In the U.S., the Food and Drug Administration enforces GMP standards and regulations; Health Canada, the European Commission, and the World Health Organization regulate GMP worldwide.

Safe Quality Food
The Food Industry Association created the Safe Quality Food (SQF) Program, a rigorous “farm-to-fork” certification to control food safety risks. It ensures that suppliers have produced, prepared, and handled food according to international and local food safety regulations—and to the highest possible standards.

The SQF Program is broken down into levels and codes, many of which build upon the HACCP rules established by the ISO. They cover food safety fundamentals, safety and quality, and ethical sourcing. Auditing is a core component of SQF, as is third-party assessment.

IATF 16949
IATF 16949 is the international standard for automotive quality management systems, which was established jointly by the International Automotive Task Force (IATF) and the ISO. It applies to any manufacturing organization that makes components, assemblies, or parts for the automotive industry.

IATF 16949 encompasses the QMPs of ISO 9001, but it is process oriented, too. To earn certification, manufacturers must demonstrate how their quality management processes support continuous improvement, prevent defects, and reduce variation and waste in the supply chain.

From Principled to Practiced

See how a modern SPC solution can support industry-required quality management principles. Put quality data to practical use—and see dramatic improvements in your manufacturing organization.

New SPC Tools for a New Era of Quality

Use a Quality Platform To Put Principles into Practice

When they’re applied correctly, quality management principles aren’t just checklists. Sure, there are lots of processes and control measures to check along the way, but the benefits to the organization are systemic.

A manufacturing quality platform unites your quality metrics and exposes context and purpose. With modern SPC software and analytics tools, you can spot quality challenges and opportunities more clearly.

Whether you’re pursuing a certification, preparing for an audit, or trying to continuously improve operations, a manufacturing quality platform helps you comply—and surpass—the most stringent quality standards. Modern tools support compliance, operational improvement, and decision making—and make it easier to get a handle on the bigger quality picture.

Overcome Obstacles to Compliance

Do you have access to all the data you need if a regulator, client, or internal auditor makes a request? How can you be sure? And how long will it take for you to dig it up?Digital quality platforms remove many of the challenges related to compiling and reporting on compliance data—all your data resides in one centralized location, and it’s always within reach.Compliance metrics are more complete, accurate, and accessible when they’re digital. You can see exactly who entered data, when, and how data affected other compliance measures.

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Help Users Focus on Their Jobs

Are your operators and quality professionals drowning in data? Or do they ignore it because it’s overwhelming?

A quality platform filters data for users automatically, and presents them with only the information they need—when they need it. You set the parameters, and the software lets you know when quality checks are due or processes are out of spec. That way, users can focus on their jobs—instead of babysitting compliance activities. Quality becomes embedded into your manufacturing processes, and proving it doesn’t take center stage.

Watch this video to learn how quality intelligence can be tailored for each user.

Prioritize Improvement Opportunities

Unifying quality and compliance data is important. But then what? How do you make sense of the data? Or apply it to continuous improvement efforts?

Quality platforms simplify analyses—across multiple production lines, products, and locations. Managers can access data from anywhere and compare the information in a standardized format—no spreadsheet manipulation required.

Being able to dig into data—rolling it up organization-wide or drilling down to a particular worker or line—gives manufacturing leaders a distinct advantage. They can pinpoint what’s working—and what’s not—and create replicable best practices across the organization. They can also quantify the value of quality improvements to help prioritize initiatives.

Learn how centralized and standardized data enables clear prioritization.

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