Statistical Process Control 101

Why Use SPC in Manufacturing?

Today’s consumers expect the best quality products at the lowest price. Why do manufacturers use SPC? Because statistical process control can help you meet both these demands.

By using statistical process control, manufacturers can move from a detection approach to a prevention approach, reducing or eliminating the need to rely on sorting or inspection. SPC can increase productivity, reduce waste, and reduce the risk of shipping nonconforming products.

Statistical Process Control Reduces Scrap

For many years, the term quality control meant inspecting to remove nonconforming products. Products are produced, then inspected to determine whether they are fit to be shipped to the customer. Products that aren’t acceptable are either scrapped or reworked. Sorting products is not only expensive—you’re basically paying one employee to make the product and another to make sure that the product is right—it’s also not very accurate. Studies have shown that 100% inspection is approximately 80% effective.

Statistical process control helps manufacturers escape this inefficient cycle. SPC leads to a system of preventing nonconforming product during the production process instead of waiting until products are complete to determine whether they are acceptable. This reduces waste, increases productivity, makes product quality more consistent, and reduces the risk of shipping non-conforming products.

When statistical process control is properly implemented, manufacturers foster an environment in which operators are empowered to make decisions about processes. In this way, processes—and product quality—can be continuously improved.

SPC is a powerful tool—but success depends on regular and proper application. Management must support its implementation through trust and education of employees and a commitment to supply the necessary resources.

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Histogram

What is a Histogram?

A histogram is a graphical frequency distribution of raw data values. Histograms reveal the distribution of data values, compare them with specification limits, and generate useful metrics and statistics that describe the data set in detail.

When analyzing data, histograms are often used with statistical process control (SPC) control charts. That is, the same data used to create control charts can be used to create histograms. While control charts display data in time sequence, histograms do not. Instead, histograms show individual data values summarized and compared to engineering specifications.

Using Histograms

Histograms communicate the central tendency and spread of a data set. When compared with specification limits, histograms reveal how close the average of the data is to the engineering target. In the histogram below, the average (represented by the black vertical line) falls to the right of the target value. This indicates that the process is not centered. This fact is also indicated by the yellow bars, which show that most of the data values fall to the right of the target.

Histograms also allow users to compare individual data values to both upper and lower engineering specification limits. This allows quality professionals to access a variety of different statistics and improvement metrics.

This histogram represents the measurements of a feature, “Location C,” from a specific part, revealing the following important information:

  • The mean value is larger than the target value.
  • Data values are “bunching up” against the upper specification limit.
  • The yellow bars do not seem to match the normal curve fit.
  • No data value falls below the lower specification limit.
  • If unchanged, the process is expected to generate a loss of about 4.8% above the upper specification limit.

Automate and Simplify Control Chart Analysis

See how easy it is to access actionable information from your SPC control charts.

See the Histogram in Action

Histograms can be combined with other SPC tools and control charts to reveal important quality information and opportunities for improvement at a plant—and even across sites.

Modern SPC software solutions make these complex analyses possible. When data is centralized and standardized in a unified data repository, SPC software provides instant access to quality information, ensuring immediate attention to your greatest quality challenges.

See how InfinityQS® reveals valuable quality information and makes SPC easy.

 

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Statistical Process Control 101

SPC Control Charts

All control charts have three common elements:

  • Plot points: Plot points usually represent individual measurements, averages, standard deviations, or ranges.
  • Centerline: The centerline is usually (but not always) the average of the points plotted on the chart.
  • Control limits: Control limits represent the amount of variability in the process.

The Four Foundations of Shewhart’s Control Charts

There are four foundational guidelines to Shewhart statistical process control charts:

  1. Shewhart statistical process control charts always use control limits that are set to 3 sigma units on either side of the central line. These 3-sigma limits are always based on the chart’s data and define when action should be taken on the process. Control limits are never based on any calculation using the specification limits. Specification limits are the customer requirements and define how to treat the product, not the process.
  2. Always use an average dispersion statistic or a median dispersion statistic when computing 3-sigma control limits. Using the average or median of several dispersion statistics increases the robustness of the chart.
  3. The conceptual foundation of Shewhart’s charts is the notion of rational sampling and subgrouping.
  4. Control charts are effective only to the extent that the organization can effectively use the knowledge gained to take action.
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Statistical Process Control 101

Process Capability

Process capability is calculated from existing data but can be used as a prediction of future performance. However, the process capability analysis results must come from an in-control process if they are to be used to predict the process’s behavior in the future. The most commonly used measures of capability are Cp, Cpk, Pp, and Ppk.

Remember, in-control means that the process is showing primarily common cause variation and so is both stable and predictable. Also remember, a process can be within control limits and still be outside product specification limits.

What is Process Capability? Hearing Two Voices

As mentioned earlier, a 6-sigma range (+3 standard deviations) is the voice of the process and show only the expected variability of the process itself. Specification limits are the voice of the customer and represent what the customer expects. The graphic below shows a process that is consistently making product that is out of specification.

Process Capability

SPC Cp Index

The SPC Cp index shows how well the 6-sigma range fits into the specification range. This measurement is determined by dividing the specification limit (voice of the customer) by the control limit (voice of the process).

Formula for Cp calculation

To calculate Cp, use the following equation:

Process Capability

For example: A Cp value of 1 means that the process variation equals the specification limit range. The process is thus capable (but just barely) of meeting the customer’s criteria.

The Cp index does not consider how well (or how badly) the process is centered relative to the specification limit target. Therefore, the Cp is a measure of optimum capability (i.e., what the process is capable of if it were centered, not what the process might be doing).

SPC Cpk Index

Like the Cp index, the Cpk calculation shows the relationship of the 6-sigma spread to the specification limits, however, the Cpk considers the centering of the process. There are two Cpk values calculated, one that considers the upper specification limit and another that considers the lower specification limit. The reported Cpk, however, represents the lowest value of the capability against the upper or lower specification. Cpk shows where within the specification limits the process is currently producing, not what the process is capable of producing. To determine whether the process is capable, you must measure Cp.

Cp Versus Cpk

When the process is centered, the Cp and Cpk calculate to be the same number. However, as the process output deviates from the target value, so do the Cp and Cpk ratios. Some observations to consider looking at the graphic below:

  • The Cp value remains the same because the width of the distribution is constant.
  • Cpk = 0 when the process mean equals one of the specification limits.
  • Cpk < 0 when the process mean exceeds one of the specification limits.
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Quality Checks

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

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

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.

ExcellenceLoop

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.

QualityChecks_Check

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

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

Speak to a Manufacturing Industry 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