Quality Checks

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Use Data to Build Your Quality Manufacturing Strategy

At the most fundamental level, quality manufacturing hinges on quality checks. Data collected through quality checks is used to measure product and process quality, pinpoint places where operations can improve, and inform future strategies.

In order for quality checks to deliver meaningful insights, they have to be accurate, timely, complete, and appropriate. Leveraging statistical process control (SPC), manufacturers can hone in on the right metrics to watch and ensure data collection is consistent and precise—and that it’s providing needed information to make good business decisions.

InfinityQS SPC-based quality management solutions enable data collection options that help ensure key quality actions are attended to, whether that’s data collection, analysis, or investigation. With InfinityQS, operators are empowered to perform necessary quality checks and can take immediate steps to protect product quality.

Paper-based manufacturing quality checks are a costly option. Modernizing data collection saves more than you think.

How to Control Quality in Manufacturing? It Starts with Data

To make good decisions, you need good data. How do you collect the right information to assess and improve your quality manufacturing processes?

Compared to digital solutions, paper-based SPC data collection is expensive, inefficient, and even risky. Paper-based data collection is fraught with the potential for error and requires significant human resources to complete lower-value tasks, like managing supplies and filing or retrieving documents.

Gleaning usable information from a stack of papers—or dozens of spreadsheets—is costly too. Using paper, critical information is inaccessible, leading to missed opportunities to reduce risk, waste, or defects because the analytical process is too cumbersome and time-consuming.

QualityChecks_Check

Software Simplifies Data Collection

SPC-based quality software simplifies data collection and speeds up important quality checks, supporting streamlined data intake, reporting, and analysis with little to no IT involvement. In addition, modern quality solutions integrate into your existing quality workflows and manufacturing processes.

Because quality control in manufacturing is constantly evolving, modern quality management solutions allow for a mix of manual, semi-automated, and fully automated data collection.

  • Manual—Data collection can easily shift from pencil and paper to keyboards, touchscreens, or barcodes and become part of the “bigger picture.”
  • Semi-automated—Operators can use connected scales, calipers, gauges, and custom devices to collect quality information when they’re on the plant floor.
  • Fully Automated—Data from enterprise resource planning systems, automated test equipment, programmable logic controllers (PLCs) and “smart” sensors can feed directly into your InfinityQS quality improvement system without operator intervention.

No matter how or when data is collected, InfinityQS stores everything in a central data repository, along with the time, date, and shift. Once data is entered, anyone who’s responsible for quality—from operators to executives—can access, analyze, and act on the information.

Quality Alerts: Stay Focused on Quality, Not the Clock

Software brings standardization and consistency to quality checks, especially for time-based collections. Built-in alerts and scheduled reminders ensure that every timed check occurs on schedule, regardless of plant, shift, or machine. InfinityQS supports timed quality checks with:

  • countdown clocks that monitor the schedule
  • automatic notifications when checks are due
  • manager notifications when a check is missed
  • automatic assignment of rechecks or operator validation when data falls out of specification

Disciplined Data Collection, Flexible Management

When collection requirements or processes change, it’s easy to adapt digital collection processes and cascade that information throughout the organization. Managers simply adjust the data-collection requirements in the software, and they can include relevant information and instructions in the quality check notifications.

Testing requirements and standard operating procedures (SOPs) can be managed in the same way. Once the requirements and notification schedules are established, the software will trigger compliance workflows and automatically save data from the plant floor to a central repository.

Collect the Right Data, Right Now

Do you have the information you need to make strategic decisions and improve quality? InfinityQS software makes it easier to collect, access, and analyze the right information for your quality improvement journey.

Move Faster While Improving Product Quality in Manufacturing

When InfinityQS helped a semiconductor manufacturer transition from manual to software-based data collection, the company went from multiple, duplicate collection steps to one. Streamlining quality checks enabled the company to:

  • Save time and simplify operator processes
  • Improve the accuracy and timeliness of quality data
  • Quickly identify and correct errors
  • Improve operator morale
  • Identify opportunities to learn and improve

A manufacturing engineer at the company said the whole manufacturing atmosphere changed “from the drudgery of manually recording data and sending it into a black hole to a feeling of ownership as people instantly saw their names and the data they entered.”

“As the projects and operators advance, we only expect to move faster and faster—with the same integrity,” he said.

Read the Case Study

Proving Quality Check Compliance: Hit the “Easy Button” on Audits

Industry audits are high-stakes events. SPC software can ease some of the stress by making data collection, storage, and retrieval a cinch.

  • InfinityQS makes it quick and easy to prove that quality checks were completed correctly and on time.
  • Reports can be customized (e.g., by shift, day, or event) and produced in minutes, since all of your quality data are held in a centralized and standardized repository.
  • Information can be accessed from anywhere, anytime.

A purpose-built quality management solution can reduce audit prep and reporting time from days or weeks to mere minutes.

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Box-and-Whisker Plots

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What is a Box-and-Whisker Plot?

A box-and-whisker plot is a well-known statistical process control (SPC) comparative analysis tool that can help you eliminate process variation. Use box-and-whisker plots to compare product and process performance, even on different lines or in different plants.

Like a histogram, box-and-whisker plots reveal the distribution of data values. Instead of a histogram’s frequency distribution, box-and-whisker plots represent the distribution with percentiles.

Box-and-Whisker Plots Explained

Vertical lines on box-and-whisker plots represent percentiles. The leftmost point on each horizontal line (or “whisker”) represents the minimum value while the rightmost dot represents the maximum value. The line in the center of each box represents the 50th percentile. The box itself spans from the 25th to the 75th percentile. The vertical lines on the whiskers represent the 5th and 95th percentiles.

Use Box-and-Whisker Plots for Rapid Insights

In a screen display, box-and-whisker plots are more compact than histograms, enabling several plots to share the same screen space. This means you can easily compare multiple plots. You can quickly compare central tendency and variability for each data set represented by box-and-whisker plots.

Compare Against Specification Limits

To extract even more valuable information, box-and-whisker plots can be compared against specification limits. This allows the viewer to understand which data sets are generating the most (and least) out-of-specification issues. As a result, box-and-whisker plots are ideal tools for prioritizing quality activities and Six Sigma projects.

Compare Multiple Process Streams

Box-and-whisker plots are particularly useful for viewing the performance of multiple process streams in the same interface. This capability is useful in any industry that needs to compare performance between production lines, product codes, and shifts.

For example, in food packaging organizations, minimum fill weights are regulated. Underfilling can result in fines or sanctions; overfilling can significantly increase costs.

Box-and-whisker plots quickly reveal vitally important information such as:

  • Whether products are being filled to minimum required levels
  • The presence and amount of overfill
  • Which products run best on which production lines
  • Which production lines run the same product weights higher (or lower) than others
  • Whether different shifts fill consistently
  • Average fill volumes
  • Variability in fill volumes
  • Where out-of-specification issues occur the most

Take Another Look at In-spec Data

With the box-and-whisker chart, you can easily discover best practices and opportunities for improvement. Plus, you can examine in-spec data to find savings you might never have expected.

See the Box-and-Whisker Plot in Action

InfinityQS® SPC software solutions provide fast and easy access to box-and-whisker charts, without a great amount of time-consuming manual data entry or analysis. See how easy it can be to spot trouble—or opportunities—by surfacing this information from within SPC software.

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Cost of Quality

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Rethink Your Investment in Quality Initiatives

For many manufacturing organizations, the cost of quality management is viewed as an expense, necessary for maintaining compliance but not operationally beneficial.

That’s because status-quo and poor quality management programs are typically reactive. Data are collected and stored in spreadsheets or on paper forms, and quality issues often aren’t discovered until they create customer or shipment problems. Putting out quality “fires” leaves the quality team little opportunity to take advantage of the profit potential inside that quality data.

Quality is much more than a checkbox or a line-item expense. When you build quality manufacturing into your organizational culture, you turn quality data into a rich, strategic information source that helps you reduce costs, improve productivity, and expand market share to secure the future of your company.

The cost of quality isn’t measured in the price of data collection but in the greater value that quality data brings your manufacturing organization.

Quality manufacturing starts when you re-imagine where your quality data can take you.

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Make the Case: Weigh the Cost of Poor Quality

What is the cost of poor quality? Many manufacturing organizations evaluate the cost of quality by considering only the upfront price of a software solution, better measurement gauges, or enhanced inspection strategies. But poor quality is the source of significant unseen costs across the organization, from the plant floor to the facility level and extending across the enterprise. To make the case for adopting a quality manufacturing culture, consider the broader costs you’re incurring with an outmoded quality management practice.

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On the Plant Floor: Collecting Manufacturing Quality Data

For many organizations, the cost of poor quality starts with outdated data collection practices. Paper data collection is not cheap. If your plant floor operators and quality management teams are still using paper checklists to manually record data collections, ask about the costs of factors such as:

  • Time—Operators focus excessive time and energy on manually collecting and recording data.
  • Inaccurate collections—Manual data collections are prone to inaccuracies and may be missed altogether, increasing compliance risks.
  • Missed information—Data collected on paper is hard to review, increasing the likelihood of missing critical process variances or other issues.

At the Facility Level: Analyzing Manufacturing Quality Insights

Quality professionals and plant managers in many organizations spend hours—even days—every week compiling collected data into spreadsheets, then manipulating that data across multiple sheets. Even with all that effort, they may never have a clear way to compare the information that’s coming in from different machines, shifts, or sites. Consider the cost and effort associated with trying to improve:

  • Priorities—Which processes or equipment need improvement?
  • Savings—Which improvements will yield the greatest cost reductions?
  • Consistency—How can you ensure high performance across all lines and sites?

Across the Enterprise: Reporting Manufacturing Quality Insights

Leadership, Six Sigma, and executive teams need the ability to easily access aggregated data to perform fast, clear analyses of the root causes of production costs. When leadership teams have to sort information from multiple sources or request reports from IT, decision-making can slow to a glacial pace. Consider how costly and time-intensive it can be to:

  • Analyze—Manual reporting is more complex with disparate, out-of-date data structures.
  • Collaborate—Teams struggle to make data-driven decisions without a shared view of information.
  • Prioritize—It’s difficult to identify high-impact improvements without the ability to compare performance across the enterprise.

How Much Can You Save with a Quality Manufacturing Focus?

Root-cause analysis of any costly issue often reveals that the losses multiply across multiple levels of the organization. See how one large-form manufacturer reduced its scrap from 45% to 0% and dramatically reduced costs—all by focusing on quality.

Quality Data Is Your Most Powerful Cost-Reduction Resource

Manufacturing organizations of every size—from global enterprises to midsize regional producers—and across every industry often feel they have to choose between high quality and operational efficiency.

Nothing could be further from the truth.

Poor quality is at the heart of the costliest issues in manufacturing. Eliminate your quality issues, and you’re already on your way to reducing costs and improving productivity. The quality data you’re already collecting hold the keys to addressing core cost issues head on.

Reduce the cost of waste, scrap, and rework

Relying on a final inspection for quality control can be a case of too little, too late. If a process falls out of spec anywhere along the production line, that finished product heads straight for the waste bin, racking up untold costs in rework and materials. InfinityQS quality control solutions help you monitor product and process quality in real time at every critical operation so that plant floor operators can adjust and eliminate variations before they cause costly waste.

In addition, InfinityQS solutions centralize and standardize collected quality data to provide meaningful operational insights. When you roll up that aggregated data, you can get a big-picture view of all your operations—and apply waste-reducing best practices consistently across products, processes, and plants.

Turn customer complaints into customer loyalty

When you are able to identify and correct product and process variations early in production, sub-par products never reach your final inspection and customers. If customers do have a question or an issue, InfinityQS solutions provide instant access to reporting that helps you respond to customer queries quickly. That responsiveness can help you build a stronger bond, more repeat orders, and  better customer relationships. The result? Happy customers who value your high quality, reliable products.

Build brand equity and gain a competitive advantage

A hallmark of a trustworthy brand is consistency across all products. InfinityQS solutions provide targeted, extensive data collection and quality control analysis capabilities, automated alerts, and aggregated access to historical data. Together, these capabilities enable unparalleled product consistency to meet your customers’ expectations and elevate your brand as the premium producer in your industry.

Prevent costly recalls

Product recalls cost your manufacturing organization more than lost time and materials. The potential loss of customer confidence and brand reputation can be devastating. InfinityQS quality solutions enable you to reduce or eliminate defects and automate compliance, policy, and procedure enforcement. Proactive quality assurance reduces the need for reactive responses to recalls.

Simplify and streamline audits

Audits that take days or even weeks rack up costs in time, effort, and resources. InfinityQS quality solutions eliminate those costs by enabling you to respond to audit requests in minutes. When quality data are centralized and standardized, it’s easy to pull together quality, preventative control, and other data across one shift or multiple shifts on multiple days. Then, you can easily create customized reports in response to specific auditor or customer queries.

Reduce the cost of regulatory compliance

Manufacturers today deal with myriad, complex international regulations and compliance requirements. Ensuring those requirements are met can be complex and time-consuming. InfinityQS solutions simplify and streamline regulatory compliance with automated alerts to ensure compliance checks are performed and automated notifications that provide visibility into potential or actual failures.

Make the most of investments in systems and equipment

No manufacturer is able to abandon their investment in existing equipment, devices, systems, and infrastructure. Fortunately, InfinityQS quality management software solutions integrate with a wide range of legacy systems, supporting communications through their native protocols (e.g., ODBC connection, XML, TXT, and others). Familiar, user-friendly interfaces and operational components add to the flexibility and scalability of the InfinityQS platform and enable users to take advantage of self-service, on-demand reporting. The result is better use of your quality data—without costly demands on your IT team.

Real Savings, Real Transformation

Ready to change your perception of the cost of quality? Take a peek at the features, analytics, dashboards, and reports in InfinityQS software and re-imagine how you can find savings in the quality data you already have.

 

Reduce Costs and Boost ROI when You Look at Quality in a New Light

The quality data you already collect is a rich, untapped source of strategic insights that can drive enterprise-wide growth and transformation. With a quality manufacturing approach, the cost of quality initiatives quickly becomes a powerful return on investment.

When quality is embedded in every process across your enterprise, your business is transformed in ways that reduce costs across every level of your manufacturing operations.

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Statistical Process Control 101

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Process Behavior and Control

The terms in control and out of control are typically used when referring to a stable or unstable process. A process is in control (stable) when the average and standard deviations are known and predictable. A process is out of control (unstable) when either the average or standard deviation is changing or unpredictable.

  • In control: Stable, predictable, consistent, unchanging
  • Out of control: Unstable, unpredictable, inconsistent, changing

In Control

An in-control process has many benefits:

  • Scrap and rework estimates can be made prior to production.
  • Machine settings can be adjusted to optimize throughput.
  • Engineers can incorporate statistical tolerance into their drawings, increasing component tolerances without compromising assembly performance.
  • Product designs can be statistically modeled to accurately predict fit and performance yields prior to prototype assembly.
  • Machine utilization can be optimized (e.g., high-precision machines and resources will not be wasted on manufacturing low-precision dimensions).
  • Process-improvement resources will be better spent.

Remember, being in control does not mean that the process is within specification. A process can be extremely stable while consistently producing bad product.

Out of Control

A process is usually judged to be out of control based on five commonly used control chart rules. These rules signal a change in either the process average or the variation.

  1. Points are beyond control limits.
  2. Eight or more consecutive points are either above or below the centerline.
  3. Four out of five consecutive points are in or beyond the 2-sigma zone (referred to as zone B in the graphic).
  4. Six points or more point in a row are steadily increasing or decreasing.
  5. Two out of three consecutive points are in the 3-sigma region (referred to as zone A in the graphic).
Process behavior chart

Even an out-of-control process can reveal useful information. By using SPC to measure out-of-control processes, you can do the following:

  • Detect both unwanted and desirable process changes.
  • Prove whether a process change resulted in an improvement.
  • Determine when to make a process change.
  • Verify measurement system improvements.

Control charts, sometimes called process behavior charts, are tools to determine whether a process is stable or unstable.

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Statistical Process Control 101

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Definitive Guide to SPC Control Charts

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What is an SPC Control Chart?

For manufacturers who use statistical process control (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.

Why Use SPC Control Charts for Quality Control?

SPC control charts are foundational quality control tools and figure prominently in Lean manufacturing and Six Sigma efforts. Control charts are used in a variety of ways, but on the shop floor, operators use them as a means of assessing, controlling, and ensuring the consistency of manufacturing processes. By controlling processes, operators can minimize significant process changes that can result in off-quality products.

The Right Control Chart for Every Situation

Your manufacturing situation is unique, so you need control charts that can manage the variety of products you make while reducing complexities that take up time in your work day.

With InfinityQS® software, you get access to more than a few traditional control charts. From standard control chart options for high-speed production to managing short runs and large numbers of part features, InfinityQS software offers a huge variety of configurable control charting options to help manage your biggest challenges.

Although many different types of SPC charts exist, selecting the most appropriate chart for your situation should not be overwhelming. Let InfinityQS help. Our highly configurable control charts will ensure that you have the best control chart for detecting the right type of variation, resulting in reduced defects and greater process consistency.

In addition, our software solutions automate and simplify chart use to help you get actionable information from your quality data.

SPC Charts Explained

In the pages of this online guide, you’ll find examples of the most popular SPC control charts and analytic displays and learn how they can help you better understand your processes and optimize performance.

For further guidance, download our free resource, A Practical Guide to Selecting the Right Control Chart.

Beyond the SPC Chart: See the Benefits of Modern SPC Software

Today’s manufacturing environments produce an ever-increasing amount of data. With support for automated and semi-automated data collection, using statistical process control through SPC-based Quality Intelligence software makes sense and can help reduce or eliminate the potential for human error.

Think you can’t afford to automate SPC charts?

With InfinityQS, implementing SPC software has never been easier—or more affordable.

SPC Control Charts

Select an SPC control chart to learn more about its use in traditional and special production situations.

Individual X and Moving Range (IX-MR) Chart

Charts the actual reading and the absolute difference between two consecutive plot points.

Xbar and Range Chart

Plots the average of individual values in a subgroup.

Xbar and s Chart

Plots the average and the sample standard deviation of individual values in a subgroup.

SPC Analytic Charts

Select an analytic chart to discover how it can provide insight for your SPC initiatives.
Histogram Histogram Plots distributions to provide a quick view of variations with a process. Learn More
Pareto Pareto Provides a different way to visualize distributions and analyze problem or cause frequency. Learn More
Box-and-Whisker Box-and-Whisker Shows the shape of a distribution, including central value and variability. Learn More
Capability & Performance Capability & Performance Capability (Cp) and performance (Cpk) charts illustrate a process’s ability to meet specifications. Learn More

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Statistical Process Control 101

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Understanding Process Variation

William Edwards Deming (1900-1993) was an important contributor to statistical process control and its use in manufacturing. According to the American Society for Quality (ASQ), his 14 key points on quality management are a core part of modern quality management programs.

Understanding process variation is an integral aspect of using Statistical Process Control (SPC) to improve your manufacturing processes. Dr. Deming’s first principle states, “The central problem in lack of quality is the failure of management to understand variation.” Only after management understands variation can a manufacturer succeed in implementing Dr. Deming’s second principle: “It is management’s responsibility to know whether the problems are in the system or in the behavior of the people.”

Types of Process Variation

There are two types of process variation:

  • Common cause variation is inherent to the system. This variation can be changed only by improving the equipment or changing the work procedures; the operator has little influence over it.
  • Assignable cause variation comes from sources outside of the system. This variation can occur because of operator error, use of improper tooling, equipment malfunction, raw material problems, or any other abnormal disruptive inputs.

The goal of SPC is to understand the difference between these two types of process variation—and to react only to assignable cause variation. Processes that show primarily common cause variation are, by definition, in control and running as well as possible.

Control versus capability

Note that keeping a process in control doesn’t mean that the product is acceptable; the system must also be capable of making acceptable products. Control and capability are different concepts.

SPC uses statistical tools—such as control charts—to identify process variations. Special cause variations—those outside the standard or expected variation—are identified and their causes need to be eliminated or at least understood.

Example of special cause variation

Suppose you drive to work each day. Your path has inherent or common variations, such as traffic lights. But suppose there is a railroad crossing that causes you to be 30 minutes late for work. That day’s commute would be special variation, and the railroad crossing would be the assignable cause.

As a result of understanding and reducing or eliminating assignable cause variations (perhaps there is a route with no railroad crossings), processes can be kept in control and continually improved. Adjusting an in-control process when there is no identified need is called tampering and only increases the variation of the system.

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Statistical Process Control 101

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Populations and Sampling

population consists of all the possible elements or items associated with a situation; for example, all trout that are living in a lake. A sample refers to a portion of those elements or items. It is cost prohibitive to evaluate every member of a population and, in the case of destructive testing, may be impossible. For these reasons, manufacturers rely on sampling their data to cost-effectively make inferences of the population without measuring each piece.

  • Effective sampling plans must be representative of the population being studied.
  • In most cases, sampling plans need to be random and unbiased.
  • Sampling frequency and subgroup size are also crucial to a successful sampling plan.

Rational Sampling and Subgrouping

Rational samples are taken with regard to the way the process output is measured (i.e., what, where, how, and when it is measured). Samples must be taken frequently enough to monitor any changes in the process. Samples should be selected with the goal of keeping the process stream intact. That is, in the context of manufacturing, a stream consists of a single part, process, and feature combination. Mixing any one of these parameters introduces ambiguity into the analysis. Odd sample sizes (3 and 5 are very common) are recommended because they have a natural median.

The correct sampling frequency depends on how fast the process is changing. To be representative of the population, samples must be taken often enough to catch any expected changes in the process, but with sufficient time between samples to display variation. Frequencies are usually defined in measurements of time (e.g., every 30 minutes, hourly, daily) but may also be defined using counts (e.g., every 100th product).

After the data have been sampled rationally, they must be subgrouped rationally as well. A rational subgroup contains parts that can be produced without any process adjustments – typically consecutively produced parts. Such a subgroup has little possibility of assignable cause variation within the subgroup. If only common cause variation exists within the samples, then any abnormal differences within or between the subgroups is attributable to assignable cause variation. Process streams should not be mixed within a subgroup. If the subgroup includes output of two or more process streams and each stream cannot be identified, then the sampling is not rational.

The subgroup size determines the sensitivity of a chart. As the sample size increases, the plotted statistic becomes more sensitive. That is, charts can detect smaller process shifts as the sample size increases.

Data must sometimes be grouped in subgroups of one. Subgroup size should be one when process adjustments or raw material changes must be made with each part or when only one value represents the monitored condition (e.g., daily yield, past week’s overtime). Subgroup size should also be one when sampling a known homogeneous batch.

In Advanced Topics in Statistical Process Control, Donald Wheeler suggests the following subgrouping principles:

  • Never knowingly subgroup unlike things together.
  • Minimize the variation within each subgroup.
  • Maximize the opportunity for variation between the subgroups.
  • Average across noise, not across signals.
  • Treat the chart in accordance with the use of the data.
  • Establish standard sampling procedures.

Random vs biased sampling

The purpose of a sample is to accurately represent the population. Statistical formulas that are used to estimate populations are based on the premise that the samples are random. In a random sample, every item in the population has an equal chance of being selected. A sample has bias when some of the items in a population have a greater chance of being sampled than others.

Example: Sampling pies

Suppose you are a taster in a pie factory. If a day’s production is one pie, then that pie is the population. To evaluate the population, you would need to eat the entire pie. However, you’d then be left with no pie to sell. A more effective option, assuming a uniform crust and homogeneous filling, would be to slice the pie into 12 equal sections and eat only one slice. By eating this sample slice, you can evaluate the quality of the entire pie and still be left with slices to sell.

If production increases to several pies per day, you may continue eating one slice from a pie and may not sample every pie. If you add a second shift or a second variety of pie, you would need to collect subgroups from these new sources of variation.

Imagine that you always take a sample slice from the same slice location for the pie samples. It may be possible that the location of that slice as the pie moves through the oven allows it to be perfectly cooked while the other side of the pie is slightly undercooked. This is another source of variation that needs to be considered with sampling. A true random sample would be one that is taken from different or random areas of each sampled pie.

5 Ws and 2 Hs of Sampling

Who will be collecting the data? Evaluate the abilities of the operator who collects the data. How much time does the operator have? Does the operator have adequate resources to collect the data?

What is to be measured? Focus on important characteristics. Remember that it costs money to sample, so you should focus on the characteristics that are critical to controlling the process or key features that measure product conformity.

Where or at what point in the process will the sample be taken? The sample should be taken at a point early enough in the process that allows the data to be used for process control.

When will the process be sampled? Samples must be taken often enough to reflect shifts in the process. A good rule of thumb is to sample two subgroups between process shifts.

Why is this sample being taken? Will the data be used for product control or process control? What question(s) are you trying to answer with the data?

How will the data be collected? Will samples be measured or evaluated manually, or will the data be retrieved from an automated measurement source?

How many samples will be taken? The sample quantity should be adequate for control without being too large.

Attributes (Defects/Defectives)

The discussion so far has centered on the benefits of measuring variables data. But in many situations, there is no measurement value, only a pass/fail rating or a defect count. Even so, attribute data can also be plotted on control charts and be vital to understanding process control. There are two distinct types of attribute data: defects and defectives.

Defects

Defects data, also known as counts data, are used to describe data collection situations in which the number of occurrences within a given unit is counted. An occurrence may be a defect, observation, or an event. A unit is an opportunity region to find defects, sometimes called the area of opportunity. A unit may be a batch of parts, a given surface area or distance, a window of time, or any domain of observation.

For example, suppose the number of weave flaws is counted on a bolt of fabric. The bolt represents a unit, and the weave flaws represent occurrences. There might be an unlimited number of types of flaws on a given bolt of fabric. Some flaws might be more severe than others. A flaw might or might not cause the bolt to be scrapped. Consecutively produced bolts might or might not be of uniform size.

Defectives

Defectives data, also known as go/no-go or pass/fail data, are used to describe data collection situations in which the unit either does or does not conform.

For example, light bulbs are tested in lots of 100. If a bulb lights up, it conforms and is accepted. If the bulb does not light, it is nonconforming. Or consider a filling operation. If a container is filled below the minimum weight, it is defective. Anything over the minimum weight is accepted. Either the fill volume meets the minimum requirements, or it does not.

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Quality Manufacturing

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Go Beyond the Quality Checklist

As a manufacturer, you do more than just make a product. You are creating a brand and a customer relationship that’s essential for today’s success and the future of your business. Whatever your industry, whether you’re a small manufacturer or a global brand, your company’s reputation and earning potential are always at stake.

Manufacturing quality is the key to differentiation and a competitive advantage. When you put quality at the heart of your operations and your manufacturing culture, you gain more than just a mark on a quality control checklist. You gain the ability to transform your manufacturing organization and position it to thrive now and into the future.

Quality manufacturing starts when you re-imagine where your quality data can take you.

Quality manufacturing starts when you re-imagine where your quality data can take you.

“Coty has realized quality manufacturing benefits across all parts of the value chain—from quality professionals that experience unprecedented database accuracy to executives seeing financial savings.”
Romina Colautti, Process Engineer

Coty

Build Quality Manufacturing into Your Culture

Quality manufacturing goes beyond compliance requirements on a quality checklist. It’s more than employing a proven statistical process control (SPC) methodology. It’s a cultural foundation that crosses activities in every aspect of your operations, informs and empowers decision-making, and delivers a powerful return on investment (ROI). It’s the enterprise-wide practice that turns quality data into actionable Quality Intelligence.

Browse the topics in this learning center to learn about the critical aspects of quality control in manufacturing that will elevate the quality manufacturing culture of your organization.

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Cost of Quality

When you change the way you think about your investment in quality initiatives in manufacturing, you launch a transformational process that enables positive, continuous improvement—and profitable growth for the future of your organization.

Do you see quality as a cost—or an opportunity? A constant chore—or a strategic advantage?

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Quality Team

In some organizations, the quality team is limited to a few individuals. In organizations that embrace quality manufacturing, the quality team extends across roles and locations, from suppliers to plant floor operators to plant managers to boardroom executives.

When everyone in the organization has a stake in quality, you gain the ability to continuously identify improvement opportunities, minimize risk and recalls, and exceed customer expectations.

How can you foster communication across roles?

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Quality Dashboards

When everyone plays an important role in quality manufacturing, it’s essential to ensure that everyone has the information they need to perform their quality assurance tasks.

Role-based quality dashboards provide an uncluttered interface for showing every user the specific information they need to do their job well—and take meaningful, proactive action to improve quality at every level.

How can you make SPC information more accessible to all your stakeholders?

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Quality Checks

On a busy plant floor, operators may be performing routine quality checks as if they were an annoying chore rather a necessary contribution of assuring quality. More important, they might miss timed checks or skip some quality checks altogether.

When you provide operators with tools such as automated data collection and role-based dashboards, they can more easily see the information they need, perform required checks, and take corrective action in real time.

How can you empower operators and ensure accountability?

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Quality in Real Time

Every manufacturing organization collects data. Lots of data. But often that data is siloed, archived, and never used. In quality manufacturing, valuable data are brought back to life to ensure quality in real time.

That means being able to not only collect data in real time but also see, analyze, and use it to proactively correct issues and improve outcomes—saving time, money, and resources in the process.

How can you improve response times to issues and audits?

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Quality Control Methods

Time-tested quality control methods such as inspection, in-process sampling, and control charts provide a solid foundation for SPC-based quality programs. However, these methods can be time-consuming and often stop at the plant floor.

Learn how to leverage the quality data you already collect today to make more impactful improvements across your whole enterprise.

How can you make SPC information easier to analyze?

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Quality Metrics

Your metrics—the data that you measure and record every day—are just the tip of the quality manufacturing iceberg. Give a second life to your data by centralizing and aggregating it so that you can see the bigger picture of your products, processes, and plants—and make meaningful improvements across your enterprise.

How do you turn quality metrics into quality intelligence?

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Bring Quality Initiatives to Life with Modern SPC

Building a quality manufacturing culture is faster and easier when you have the right tools and systems in place. InfinityQS provides the solutions to address your most critical quality concerns. Grounded in proven statistical process control (SPC) methodology and purpose-built for the way modern manufacturing works today.

Learn more about InfinityQS quality platforms.

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