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X-bar and sigma chart formulas

X-bar control limits are based on either range or sigma, depending on which chart it is paired with. When the X-bar chart is paired with a sigma chart, the most common (and recommended) method of computing control limits based on 3 standard deviations is:

X-bar

x-bar-formula

n is the number of observations

x-double-bar-formula

k is the number of subgroups

f-x-bar-sigma-ucl

Upper control limit

f-x-bar-sigma-lcl

Lower control limit

Sigma

s-bar-formula

k is the number of subgroups

range-bar-formula

k is the number of subgroups

f-sigma-ucl

Upper control limit

f-sigma-lcl

Lower control limit

Subgroup Size
A3
B3
B4
2
2.659
—–
3.267
3
1.954
—–
2.568
4
1.628
—–
2.266
5
1.427
—–
2.089
6
1.287
0.030
1.970
7
1.182
0.118
1.882
8
1.099
0.185
1.815
9
1.032
0.239
1.761
10
0.975
0.284
1.716
11
0.927
0.321
1.679
12
0.886
0.354
1.646
13
0.850
0.382
1.618
14
0.817
0.406
1.594
15
0.789
0.428
1.572
16
0.763
0.448
1.552
17
0.739
0.466
1.534
18
0.718
0.482
1.518
19
0.698
0.497
1.503
20
0.680
0.510
1.490
21
0.663
0.523
1.477
22
0.647
0.534
1.466
23
0.638
0.545
1.455
24
0.619
0.555
1.445
25
0.606
0.565
1.435

Formulas and Tables

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X-bar and range chart formulas

X-bar control limits are based on either range or sigma, depending on which chart it is paired with. When the X-bar chart is paired with a range chart, the most common (and recommended) method of computing control limits based on 3 standard deviations is:

X-bar

x-bar-formula

n is the number of observations

x-double-bar-formula

k is the number of subgroups

Upper control limit

Lower control limit

Range

range-bar-formula

k is the number of subgroups

Upper control limit

Lower control limit

Tabular values for X-bar and range charts
Subgroup Size
A2
d2
D3
D4
2
1.880
1.128
—–
3.268
3
1.023
1.693
—–
2.574
4
0.729
2.059
—–
2.282
5
0.577
2.326
—–
2.114
6
0.483
2.534
—–
2.004
7
0.419
2.704
0.076
1.924
8
0.373
2.847
0.136
1.864
9
0.337
2.970
0.184
1.816
10
0.308
3.078
0.223
1.777
11
0.285
3.173
0.256
1.744
12
0.266
3.258
0.283
1.717
13
0.249
3.336
0.307
1.693
14
0.235
3.407
0.328
1.672
15
0.223
3.472
0.347
1.653
16
0.212
3.532
0.363
1.637
17
0.203
3.588
0.378
1.622
18
0.194
3.640
0.391
1.608
19
0.187
3.689
0.403
1.597
20
0.180
3.735
0.415
1.585
21
0.173
3.778
0.425
1.575
22
0.167
3.819
0.434
1.566
23
0.162
3.858
0.443
1.557
24
0.157
3.895
0.451
1.548
25
0.153
3.931
0.459
1.541

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Additional Resources

Recommended websites

ASQ – American Society of Quality site offers quality resources and information. Also visit ASQ’s magazine Quality Progress online.

iSixSigma – Quality resources for achieving Six Sigma results, this site includes articles and discussion forums.

Quality Digest – The online edition of Quality Digest magazine offers articles, news, and resources.

Quality Magazine – Quality Magazine’s online edition offers articles and a reader forum.

SPC Press – SPC press and Statistical Process Controls, Inc. specialize in statistical tools for understanding and using data. They offer public and in-house seminars, consulting, implementation assistance, books, videos, training aids, and specialty items.

MicroRidge – Hardware and software solutions for RS232 and network data acquisition. Products include our line of gage interfaces and software keyboard wedges.

Institute for Healthcare Improvement (IHI) – The Institute for Healthcare Improvement offers resources and services to help healthcare organizations make dramatic and long-lasting improvements that enhance clinical outcomes and reduce costs.

National Association for Healthcare Quality (NAHQ) – NAHQ is dedicated to improving the quality of healthcare and to supporting the development of professionals in healthcare quality.

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Glossary

A

Alpha value

The risk of being wrong when completing a hypothesis test.

The chi-square table is computed in such as way that if the assumption of “normal data” is true for a given X-bar and sigma, the results of the chi-square test will be incorrectly rejected only 5% of the time. This calculation reflects the alpha value.

Assignable cause

An assignable cause is a source of variation that is intermittent, not predictable. It is sometimes called “special cause” variation. On a control chart, an assignable cause is signaled by points beyond the control limits or nonrandom patterns within the control limits.

Attributes data

Attributes data is data that can be classified and counted. There are two types of attributes data: counts of defects per item or group of items (nonconformities) and counts of defective items (nonconforming). For example, yes/no, good/bad, pass/fail, and go/no go.

Average

Another term for a mean, it is an indicator of the center of a set of data points. It is found by adding all the individual values and dividing by the number of values.

B

Bell curve

Another term for the shape formed by a normal distribution when drawn as a histogram.

Bias

Something that influences the selection of certain items when collecting a sample.

Bimodal distribution

A distribution that has two modes. Drawn as a histogram, this condition is reflected by two peaks or high points.

C

Capability

The capability of a process is how the process performs when compared to specification limits or requirements. It uses a series of indices: Cp, Cpk, Cr, and Cpm.

Capability analysis

A set of statistical calculations performed on a set of data to assess how the distribution formed by the data compares to specifications or requirements.

Capable process

A process is said to be capable if nearly 100% of its output falls within specification limits.

c-chart

An attributes control chart that is used to monitor the number of nonconformities, such as defects per subgroup. The subgroup size must remain constant for this type of chart.

Central location

Central location is the center of a set of data points. Mean, median, and mode are the statistics used to describe it.

Central tendency

Statistics such as the mean, median, and mode are said to be measures of central tendency.

Characteristic

A distinguishing feature of a process or its output on which variables or attributes data can be collected.

Chi-square

A goodness-of-fit-test statistic used to test the assumption that the distribution of a set of data is similar to the expected distribution, such as a normal distribution.

Coefficient of variance

A ratio that measures the significance of the standard deviation in relation to the mean.

Common cause

A source of variation that is inherent in a system and is predictable. A control chart identifies a system with only common causes of variation. Common causes of variation affect all individual values of a system, and can be eliminated only by a systemic change.

Control chart

A control chart is a graphical representation of a characteristic of a process, showing plotted values of some statistic, a central line, and one or two control limits. It is used to determine whether a process has been operating in statistical control and is an aid to maintaining statistical control.

Control limits

Lines on a control chart used as a basis for judging whether variation in data on a chart is due to special or common causes. These limits are calculated from data collected from the system, they are not specifications or limits set by customers or management.

Cp

A capability index that compares the width of a two-sided specification with the variation in the process. Estimated standard deviation is used to calculate the process variation. A Cp larger than 1 indicates that the process variation is narrower than the specification.

Cpk

Cpk is a capability index that tells how well a system can meet two-sided specification limits. Because it takes the target value into account, the system does not have to be centered on the target value for this index to be useful. It is calculated with estimated standard deviation. A Cpk greater than 1 indicates that the process can meet the specification.

Cpl

A capability index that compares the variation in the process to the lower specification. Estimated standard deviation is used to calculate the process variation. A Cpl greater than 1 indicates the process is capable of meeting the lower specification.

Cpm

Cpm is a capability index that shows how well the system can produce output within specifications while taking the target into account. Its calculation uses sigma calculated from the target value instead of the mean.

Cpu

A capability index that compares the variation in the process to the upper specification. Estimated standard deviation is used to calculate the process variation. A Cpu greater than 1 indicates the process is capable of meeting the upper specification.

Cr

Capability ratio compares the variation in a process with the width of a two-sided specification. Estimated standard deviation is used to calculate the process variation. It is the inverse of Cp.

D

Defect

An occurrence such as a blemish, scratch, burn, error, or omission that appears on an object. A defect does not necessarily make the object unusable or unacceptable.

Defective

A product or service flawed beyond use or acceptability.

Discrimination

This refers to a description of the capability of a measurement system.

Dispersion

Statistics such as the range and standard deviation (sigma) are said to be measures of dispersion.

Distribution

Distribution is a way of describing the output from a system of variation. The distribution’s location, shape, and spread may be evaluated by statistics such as the mean, median, sigma, and range.

E

Estimated sigma

This is an estimate of the standard deviation calculated by dividing the average range by the tabular constant d2 (R-bar/d2).

H

Histogram

A histogram is a bar chart that represents the frequency distribution of data. The height of each bar corresponds to the number of items in the class or cell. The width of each bar represents a measurement interval. The histogram shows basic information such as central location, shape, and spread of the data being examined.

I

In control

A process is said to be “in control” or “stable” if it is in statistical control. If a process is in statistical control, a control chart will have no subgroups falling outside the control limits, no runs, and no nonrandom patterns.

Individuals control chart

The individual portion of an X-MR control chart. The individual data points are plotted onto the chart and compared with control limits.

K

Kurtosis

Kurtosis is a statistic that is used to measure the “flatness” or “peakedness” of a set a of data. It represents a measure of the combined weight of the tails relative to the rest of a distribution. As the tails of a distribution become heavier, the kurtosis will increase. As the tails become lighter, the kurtosis value will decrease.

L

Lower control limit

A line on a control chart used as a basis for judging whether variation from the data on the chart is due to special or common causes. Any point beyond the lower control limit is an indication of a special cause occurring. This limit is calculated from data collected on the system, it is not a specification or limit set by customers or management. The symbol is LCL.

Lower specification limit

The lower limit of a specification. This limit is set as an aim for a system or process, it is usually set by the customer of the process, engineering, or management. The symbol for the lower specification is LSL – lower specification limit.

M

Maximum acceptable subgroup size

When a varying sample size is being used in a p or u-control chart, the maximum acceptable sample size is usually a sample size that is twenty-five percent larger than the average sample size. Any subgroup with a sample size larger than the maximum acceptable subgroup size has to have control limits calculated specifically for that subgroup.

Mean

Another term for average, it is an indicator of the central location of a set of data. It is found by adding all the individual values and dividing by the number of values.

Measurement system

A measurement system consists of the people, procedures, systems, and devices used to take measurements.

Median

The middle number in a set of data when it is ranked from lowest to highest, it is an indicator of central location in a data set.

Minimum acceptable subgroup size

When a varying sample size is being used in a p or u-control chart, the minimum acceptable sample size is usually a sample size that is twenty-five percent smaller than the average sample size. Any subgroup with a sample size smaller than the minimum acceptable subgroup size has to have control limits calculated specifically for that subgroup.

Mode

Is the number that occurs most frequently in a data set. It is usually an indicator of central location.

Moving range

The difference between consecutive subgroup values on an X-MR control chart. The moving range is used as a measure of variability.

Moving range chart

The moving range portion of an individuals and moving range control chart. The moving ranges are plotted on the chart and compared with control limits.

N

Negatively skewed distribution

A distribution of data where most of the data appears on the right hand side of the distribution and then tails off to the left. Also known as a skewed left distribution.

Nonconforming

Nonconforming data is a count of defective units. It is often described as go/no go, pass/fail, or yes/no, since there are only two possible outcomes to any given check. You can track either the number of defective units or the number of nondefective units.

Nonconformities

Nonconformities data is a count of defects per unit or group of units. It can refer to defects or occurrences that should not be present but are, or any characteristic that should be present but is not.

Nonnormal data

Data that does not form a normal distribution.

Nonnormal data distribution

Any data set that does not show a normal, bell-shaped distribution.

Nonrandom pattern

A pattern in data that is repeating, or is not due to normal variation.

Normal curve

This bell-shaped curve is used to illustrate the shape of a normal distribution.

Normal distribution

A data distribution that is bell shaped and symmetrical, the normal distribution is the basis for control chart and capability analysis.

Normal probability plot

A normal probability plot is a graphical method for showing a frequency distribution. The scaling is set up so that if the distribution is normal, a straight line will result.

np-chart

An attributes control chart that plots the number of items that are defective or possess a characteristic of interest. The subgroup size must remain constant for this type of chart to be used.

O

Observation

An observation is a single piece of data, usually a count or a measurement. It is also known as a reading.

Operational definition

When applied to data collection, it is a clear, concise, and detailed definition of a measure. It ensures that those collecting data do so consistently.

Outlier

An outlier is a point on a chart that does not fall into the pattern of the rest of the data.

Out-of-control

When applied to a control chart, out of control means that at least one special cause of variation is present.

Overcontrol

Over reaction to a set of data. For example, in a control chart, it would be reacting to a common cause as if it were a special cause.

P

Pareto chart

A Pareto chart is a bar chart for ranking aspects of a problem. Typically, a few aspects make up a significant portion of the problem while many trivial aspects exist.

p-chart

An attributes control chart that plots the number of items possessing a characteristic of interest. The subgroup size may vary.

Positively skewed distribution

A distribution of data where most of the data appears on the left hand side of the distribution and then tails off to the right. Also known as a skewed right distribution.

Pp

Pp is a capability index, similar to Cp, that is a measure of process performance. Pp tells how well a system can meet two-sided specification limits, assuming that the average is centered on the target value. It is calculated with the actual sigma (using the actual individual values) rather than the estimated sigma. A Pp larger than 1 indicates that the process variation is narrower than the specification.

Ppk

Similar to Cpk, Ppk is a capability index that indicates whether a process is capable of meeting two-sided specification limits. However, Ppk uses actual standard deviation to calculate the process variation, whereas Cpk uses an estimated standard deviation. The target value is taken into account with Ppk, so the system does not have be center on the target value to be useful. A Ppk greater than 1 indicates that the process can meet the specification.

Ppl

A capability index similar to Cpl in that it compares the variation in the process to the lower specification. However, Ppl uses standard deviation to calculate the process variation, whereas Cpl uses an estimated standard deviation. A Ppl greater than 1 indicates the process is capable of meeting the lower specification.

Ppu

A capability index similar to Cpu in that it compares the variation in the process to the upper specification. However, Ppu uses standard deviation to calculate the process variation, whereas Cpu uses an estimated standard deviation. A Ppu greater than 1 indicates the process is capable of meeting the upper specification.

Pr

A capability ratio similar to Cr in that it compares the variation in a process with the width of a two-sided specification. However, Pr uses standard deviation to calculate the process variation, whereas Cr uses an estimated standard deviation. It is the inverse of Pp.

Process

A process is the combination of people, equipment, materials, methods, and environment that produce output—a given product or service. The words process and system are often used interchangeably.

Process capability

Process capability is the 6 sigma range of common cause variation for statistically stable processes only. Sigma is usually estimated by R-bar/d2.

Process performance

The process performance is the 6 sigma range of inherent variation for statistically stable processes only, where sigma is usually estimated by the sample standard deviation.

R

Random distribution

A distribution that forms no particular shape.

Random sample

A sample that allows every item in a population to have an equal chance of being selected, with no bias.

Range

Range is an estimate of spread in a set of data points; the difference between the highest and lowest values in the data set.

Repeatability

Repeatability refers to variation in a series of measurements that have been taken with one gage measuring one characteristic of the same item by the same person.

Reproducibility

Reproducibility refers to variation in a series of measurements that have been taken with one gage measuring one characteristic of the same item by different people.

Run chart

A run chart is a simple line chart that plots one characteristic over time. It is used to plot individual observations and detect patterns in the data.

S

Sample

A sample is a collection of one or more observations used to analyze the performance of a process, as opposed to the total populations. It is intended to represent the characteristics of the population. Sample is a synonym for “subgroup” in process control applications.

Sample size

The number of pieces of data taken at one time. For example five boxes are checked for stiffness every hour, the sample size in this case is five. If the temperature of a room is taken every hour, only one number is collected every hour, so the sample size is one.

Sigma of the individuals

Sigma of the individuals is standard deviation calculated from the individual data values in a data set. It is also known as actual or calculated sigma.

Sigma

Sigma is the Greek symbol, sigma symbol, used to denote standard deviation. It is a measure of the variation or spread within a set of data.

Skewed distribution

A distribution that tails off to one side, either to the left or right.

Skewness

Skewness is a statistic that is used to measure the symmetry of the distribution for a set of data. A process that is skewed tails off to the left or to the right.

Special cause

Special cause variation is a source of variation that is intermittent, not predictable. Sometimes it is called “assignable cause” variation. On a control chart, a special cause is signaled by points beyond the control limits, runs, or nonrandom patterns within the control limits. A process that has special cause variation is said to be out-of-control, unstable, or unpredictable.

Specification limits

Specifications are boundaries, usually set by management, engineering, or customers, within which a system must operate. They are sometimes called engineering tolerances.

Spread

Spread is the range of data from the lowest value to the highest value.

Stable process

A system, analysed by a control chart, with no special causes of variation present, this system is also said to be in control. Variation within a stable system is due to common causes, and is predictable.

Standard deviation

A statistic that describes the variation or spread within a data set. It can be used to indicate the variation in a process and to compare with specifications.

Statistical control

Statistical control is a condition describing a process from which all special causes of variation have been removed and only common causes of variation remain. On a control chart, processes that are in statistical control show no subgroups outside the control limits, no runs, and no nonrandom patterns. This condition is also referred to as in control, stable, or predictable.

Subgroup

A subgroup is one or more occurrences or measurements taken at one time. Multiple subgroups are used to analyze the performance of a process. Subgroup is used as a synonym for “sample.”

Symmetrical distribution

A distribution that if cut in half, shows each side is the mirror of the other.

T

Target value

The exact value at which customers, engineering, or management want the system to operate.

Trial limits

On a control chart, trial limits are calculated when there is insufficient data to calculate control limits. These give a temporary guide until sufficient data has been collected.

U

u-chart

An attributes control chart that is used to monitor the number of nonconformities per unit, such as defects per item. The subgroup size may vary.

Undercontrol

Not reacting to a set of data when the data is showing an issue or problem. For example, in a control chart, it would be ignoring a special cause of variation.

Uniform distribution

A distribution, when drawn as a histogram, has each bar at a similar frequency.

Unstable system

A system that contains special and common causes of variation; this system is also said to be out of control. An unstable system is unpredictable.

Upper control limit

A line on a control chart used as a basis for judging whether variation from the data on the chart is due to special or common causes. Any point beyond the upper control limit is an indication of a special cause occurring. This limit is calculated from data collected on the system, it is not a specification or limit set by customers or management. Its symbol is UCL.

Upper specification limit

The upper limit of a specification. This limit is set as an aim for a system or process, it is usually set by the customer of the process, engineering, or management. The symbol for the upper specification is USL–upper specification limit.

V

Variability

Variability refers to the differences among individual outputs of a process. In control chart pairs, it refers to the differences between individual observations and is analyzed in range, sigma, and moving range charts.

Variables

Variables data is data that is acquired through measurements, such as length, time, diameter, strength, weight, temperature, density, thickness, pressure, and height. X-bar and range, X-bar and sigma, and individuals and moving range charts are used to analyze variables data.

Variation

Variation is the inevitable differences that occur among individual outputs of a process. Sources of variation may be grouped into two major categories: common causes and special causes.

X

X-bar

X-bar is the average or mean of values in a group of observations.

X-bar chart

The X-bar chart is a variables control chart that shows the subgroup averages. The subgroup size for this chart must be larger than one and consistent.

Z

Zlower

The symbol for the Z value for the lower specification limit. It represents the number of standard deviations between the average and the lower specification limit.

Zmin

The minimum of the Z values, either Zupper or Zlower. It is used to calculate the Cpk index in capability analysis.

Zupper

The symbol for the Z value for the upper specification limit. It represents the number of standard deviations between the average and the upper specification limit.

Z value

Used in capability analysis, it is the symbol for the number of standard deviations between the average and a specification limit for a normal distribution.

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A resource of data collection tools, including how to collect data, how much to collect, and how frequently to collect it.

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Data Analysis Tools

Tools for analyzing and interpreting data so that the areas to improve become apparent.

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Formulas and Tables

Formulas for calculating control limits, capability indices, and more, plus the tables referred to in these calculations.

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Glossary

This complete glossary of quality tools, symbols, and abbreviations includes definitions for more than 200 quality terms.

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Additional Resources

Links to other quality resources.

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PQ Systems: SPC and Gage Management Software for Manufacturers

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PQ Systems provides intuitive solutions to help manufacturers optimize process performance, improve product quality, and mitigate supply chain risk. Simple and intuitive, yet accurate and complete, our solutions will help you achieve quality improvement success. PQ Systems SPC and gage management quality improvement solutions are relied on across an array of industries, ranging from aerospace to healthcare.

Harness the power of data analysis to increase customer satisfaction, improve audit success, and meet quality compliance requirements with statistical process control (SPC) software. PQ Systems delivers a robust SPC application that will help you easily analyze your data, understand and improve your processes, and communicate important quality information. Whether it’s for use on a single line or in a global multi-site operation, we provide a scalable, easy-to-use software solution to help you optimize quality that is simple to deploy, implement, and operate. Features include:

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Stay organized and maintain gage inventory, measurement systems analysis (MSA) study results, calibrations, and certificates—all in one place. PQ Systems is a complete calibration management solution that provides easy access to all of your gage information including usage, calibration schedules, procedures, history, and more. This gage tracking software will help audit-proof your measurement system and give your team complete device traceability and precise calibration records.

Our gage management software is designed to save time and mitigate risk by organizing and managing your measurement system information. The audit trail maintains a robust and precise record of changes to gages and events to comply with industry standards. Features include:

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Honeybee Robotics Explores Mars with PQ Systems

Learn how Honeybee Robotics uses PQ Systems to support its gage management system and comply with ISO and AS auditors.

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What to Expect

  • Free 20-minute call with a product expert
  • Live demo tailored to your industry requirements
  • Discover what products best fit your needs
  • No games, gimmicks, or high-pressure sales pitch

Real-Time Statistical Process Control (SPC) Software for Manufacturing

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Control Process Inputs on the Production Floor

Take the guesswork out of quality control with statistical process control (SPC) software. Advantive’s award-winning SPC software delivers unparalleled visibility and intelligence to help plant managers understand and control variability in manufacturing. Developed by experienced statisticians, quality professionals, and Six Sigma-certified experts, our real-time SPC solutions include the measurement and monitoring tools you need to minimize production costs, optimize product quality, and significantly reduce risk, defects, and inconsistencies.

Quickly Detect Issues

Real-time, automated alerts and notifications help manufacturers facilitate rapid and precise corrections on the production floor.

Minimize Production Costs

Our SPC solutions uncover information that manufacturers can use to improve yields while reducing downtime and quality costs.

Optimize Product Quality

Make process improvements based on an unprecedented degree of insight into the behavior and quality of every part of your manufacturing lines.

Scalable SPC Software to Drive Better Quality Outcomes

With real-time quality data from across the enterprise, our proven SPC software for manufacturing provide simple, intuitive shop floor operator interfaces and powerful analysis capabilities for quality professionals and managers. By aligning operations, process control, supply and demand data, and front-office applications, Advantive’s solutions turn quality risk into a competitive advantage.

For manufacturers who use SPC or are engaged in continuous process improvement activities, SPC control charts are powerful tools for assessing and improving process quality. Control charts provide immediate, real-time indications of significant changes in manufacturing processes that warrant a root-cause analysis or other investigation. From standard control chart options for high-speed production to managing short runs and large numbers of part features, our SPC software offers a huge variety of configurable control charting options to help manage your biggest challenges.

We collect, standardize, aggregate, and store data in a single Unified Data Repository, so you can analyze and view it in hundreds of ways—without requiring expensive add-on software, export to third-party applications, or manual data processing. It’s simple to summarize, aggregate, and analyze all of your quality data using Advantive analysis tools.

Extensive analysis and reporting capabilities help quality professionals and Six Sigma teams identify improvement opportunities and replicate quality successes, leading to better process and product quality across the organization. Depending on your role and immediate goals, different information can take priority on any given day. That’s why Advantive products feature the ability to automatically trigger alarms and communicate them to precisely the right personnel at your plants. Conveniently, a single dashboard can be configured to reveal charts and information targeted to different unique users. This allows a single interface to be repurposed for a wide variety of needs and users while minimizing time spent configuring the system.

When it comes to real-time SPC, most solutions begin—and end—with control charts. Although control charts are excellent shop-floor tools, you’ll need other analysis tools to extract maximum information from your data. Our SPC solutions include sophisticated analysis tools to give you the ability to view data across product codes, lines, or sites—all in one report. Regardless of your manufacturing process—high volume/low mix, or low volume/high mix—our software has the right analysis tools for your unique situation.

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Interactive Guide

How to Improve Manufacturing Operations with Real-Time SPC Software

With the right Statistical Process Control (SPC) software, your data can provide actionable information to improve performance, reduce production costs, optimize product quality, and increase consistency across your manufacturing operations. Learn more by exploring our interactive online guide to SPC software and quality management.

The addition of InfinityQS to SanDisk’s existing system infrastructure gives us a significantly more responsive and effective production environment. InfinityQS has proven vital in resolving issues we didn’t even know we had.

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Didier Chavet

Regional CIO

DataNet integrated easily into our processes and helped improve product quality and reliability. Since implementing, we have seen a variance reduction of 39.5%!

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Chris Valier

Business Intelligence Manager

I would recommend PQ Systems to any company that needs to gain control over their gage measurement system and have the confidence to be in compliance when it comes to ISO and AS auditors.

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Steve Hardwick

Facility Manager

What Our Customers Are Saying

Find out how our customers are solving plant floor problems, minimizing production costs, and maximizing customer value with best-of-breed SPC software from Advantive.

Speak to an SPC Software Expert

What to Expect

  • Free 20-minute call with a product expert
  • Live demo tailored to your industry requirements
  • Discover what products best fit your needs
  • No games, gimmicks, or high-pressure sales pitch

Standardized Quality Management System (QMS) Built for Manufacturers

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Meet Customer and Regulatory Compliance Standards with Ease

Product quality can make or break your manufacturing company’s success. That’s why you need a reliable quality management system (QMS) in place. At Advantive, our QMS solution provides a formalized platform that accurately tracks product specifications, documents processes, manages industry regulations, and helps you consistently meet quality standards.

Reduce Waste

Eliminate paperwork and manual processes to define test requirements and record results.

Improve Quality

Automatically prompt operators on the floor to perform required tests at the right time.

Build Trust

Improve product quality and reduce waste, returns and customer complaints.

Powerful QMS Software to Improve Product Quality and Consistency

At Advantive, we focus on providing accessible technology that drives transformational outcomes for manufacturers. Our specialized quality management systems include the tools and functionality you need to collect, test, and inspect products, meet customer and regulatory requirements, and ultimately, produce the highest quality products.

Our quality management software is integrated throughout your entire manufacturing facility to provide complete operational visibility. As information flows from one department to another, decisions can be made automatically to improve quality, productivity, on-time delivery, efficiency, and more.

Manage quality test requirements and results locally, regionally, and corporate-wide with our flexible QMS software.

  • Define quality test requirements electronically for specific plants, plants within a specific region or your entire corporation.
  • Establish tests for specific jobs, customers, specifications, board types, paper, or other manufacturing characteristics.
  • Ensure all results are recorded promptly with a Transmission Link (XMT) to testing devices capable of providing test results electronically. Some testing devices test or measure every item produced. Others even photograph every defect and/or each and every item tested.

Improve internal compliance with QA test plans and ISO requirements. If tests are skipped or results are not recorded, notification is recorded accordingly. When jobs are completed, documentation of results (including COCs and COAs) can be printed and/or emailed to appropriate parties. Because accurate results are recorded directly on the factory floor, you have the ability to respond to customer complaints and product returns with factual data. Having access to test results in real time also enables quality managers and others to examine and evaluate test results prior to shipping inferior products.

Optimize and reduce raw material and finished goods inventory. Eliminate costly material outages. Analyze what-if scenarios and market trends using multi-plant and corporate forecasts. Plan a group of similar production facilities or regional plant groupings to analyze and optimize material usage. Utilize tactical and strategic planning tools to manage long-term business strategies and more.

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The Ultimate Guide to Selecting a Cloud-Based Quality Management Solution
 

By utilizing InfinityQS to implement SPC and Six Sigma best practices across our manufacturing processes, Ben & Jerry’s will continue to identify opportunities for cost savings and ensure the highest level of customer satisfaction. The result is the perfect pint for our customers.

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Nina King

Quality Supervisor

We have made numerous investments in technology to help support our quality initiatives, and InfinityQS SCS allows us to bring everything together. We can now assimilate data from different systems and device locations into one centralized hub for easy analysis and decision-making.

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Jack Kraemer

President and COO

I would recommend PQ Systems to any company that needs to gain control over their gage measurement system and have the confidence to be in compliance when it comes to ISO and AS auditors.

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Steve Hardwick

Facility Manager

What Our Customers Are Saying

Find out how our customers are using quality management software to improve product quality, decrease risk, and make strategic data-driven business decisions.

Speak to a QMS Expert

What to Expect

  • Free 20-minute call with a product expert
  • Live demo tailored to your industry requirements
  • Discover what products best fit your needs
  • No games, gimmicks, or high-pressure sales pitch