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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.
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.
For many organizations, the cost of poor quality starts with outdated data collection practices.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:
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:
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
What to Expect
Learn all about SPC for manufacturing.
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.
An in-control process has many benefits:
Remember, being in control does not mean that the process is within specification. A process can be extremely stable while consistently producing bad product.
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.
Even an out-of-control process can reveal useful information. By using SPC to measure out-of-control processes, you can do the following:
Control charts, sometimes called process behavior charts, are tools to determine whether a process is stable or unstable.
Discover the most popular quality control charts and how to use them.
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.
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.
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.
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.
With InfinityQS, implementing SPC software has never been easier—or more affordable.
Charts the actual reading and the absolute difference between two consecutive plot points.
Plots the average of individual values in a subgroup.
Plots the average and the sample standard deviation of individual values in a subgroup.
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.”
There are two types of process variation:
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.
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.
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.
A 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.
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:
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.
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.
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.
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 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 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.
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.
Coty
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.
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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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?
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?
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?
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?
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?
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?
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.
Statistical process control can help manufacturers achieve continuous process improvement—when it is implemented properly. Watch out for the following obstacles, which can sideline your SPC efforts.
If management (or others within the company) believe that company circumstances are so unique that statistical process control cannot be applied to processes, they are likely to argue that even considering SPC would be a waste of time. This obstacle tends to crop up for manufacturers that experience the following:
To overcome this obstacle: Explain that if a process creates output, then SPC can be applied. The first step is to start collecting data to show how the process behaves. After metrics are defined and data are collected and plotted, it is easy to see that the process does have measurable characteristics. Educating employees in short-run process control methods is a great way to show them that they are not alone. While one likes to feel special, the truth is that most companies that feel too special for statistical process control are the ones that can benefit the most from using SPC.
SPC isn’t a cure-all. If no action is taken pursuant to the knowledge gained from SPC analysis, then implementing SPC software for manufacturing or setting up dozens of control charts is not going to improve anything. A control chart can’t eliminate variation and won’t solve all your quality problems.
SPC is the foundation of an effective process-improvement methodology, but there are numerous other tools that should be used. Management teams that expect to solve all their quality problems simply by implementing SPC but doing nothing with the data typically abandon the initiative when it doesn’t miraculously solve every problem.
To overcome this obstacle: SPC education must include an understanding of what SPC does. SPC brings to light common cause and special cause variations, but other tools are needed to reduce or eliminate variation. Train employees to use other process-improvement tools to help reduce variation and create a Corrective Action or Process Improvement team to work on projects.
Before SPC implementation, many manufacturers collect product data and compare them to specification limits. If the product is within the boundaries set by the customer, the manufacturer assumes that the process is performing fine…in-control. This use of data and limits is called product control, not process control.
When SPC is implemented, you use control limits that are based on process behavior to truly control the process. However, some companies keep specification limits on their control charts, base control limits on something other than true process variation, or set control limits to a standard other than +3 sigma. If control limits do not accurately represent the process, they are useless and can cause more harm than good.
To overcome this obstacle: Ensure that employees understand that control limits are the voice of the process and show how the process is performing, whereas specification limits are the voice of the customer and are independent of process stability. Specification limits do not belong on a control chart. Control charts always use control limits, which are set at 3 sigma units on either side of the central line and are based on data. Drill into all employees that control limits are never based on any calculation using the specification limits.
When a process is in state of statistical control, with primarily common cause variation present, any adjustment to the process is tampering and will only increase the variation. Operators often adjust machines that don’t need adjustment; good operators have a natural tendency to tinker with a process to try and make it perform at its best. Management can aggravate tampering by insisting that operators adjust a process when process data aren’t where management wants them.
These impulsive reactions create uncontrollable gyrations in the process. When the process deteriorates, management tends to blame the operator, resulting in distrust and damaged morale that can ruin an SPC initiative—and do irreversible harm to employee/management relations.
To overcome this obstacle: All employees, especially management, must be trained to understand variation and the dangers of tampering. Each data point on a control chart is independent of the previous one. Processes must be allowed to operate in their natural state if you are to understand the common cause variation. There is a saying in the SPC community, “Don’t do something, just stand there.” Training must include how tampering creates bias and nullifies control charts.
Employees who are expected to implement SPC without adequate training and resources will undoubtedly cause the initiative to fail. In many cases, management attempts to save money by scrimping on training, but the money saved will be outweighed by the wasted cost of an unsuccessful SPC program.
In some cases, employees get adequate training, but supervisors and management do not—and so do not support the initiative. If management is uncomfortable with SPC concepts, they will either avoid necessary actions (because they are uneasy with the changes) or recommend process changes based on a misunderstanding of process control. Either way, the SPC initiative suffers.
To overcome this obstacle: Management must provide the necessary resources to conduct thorough training for every employee and every level of the organization—including all levels of management. This training must be repeated at regular intervals, as new employees must be trained, and experienced employees need refresher courses.
Management must be involved with the SPC initiative so that employees know that management believes in and understands SPC. Management must set realistic goals for process improvement and base their analysis on solid metrics. Executive management should also involve front-line management in the selection of the areas to which to apply SPC. Doing so will increase the likelihood that front-line management will take ownership of the system and help it to gain acceptance with employees.
All managers must understand how decision-making should change after SPC is implemented. Remember Shewhart’s Fourth Foundation of Control Charts: Control charts are effective only to the extent that the organization can use, in an effective manner, the knowledge gained. Management must empower employees to make decisions gained from SPC analysis.
Data that lacks integrity has a devastating effect on analysis and decision-making. Using “bad” data can be worse than having no data. Data can be biased in many ways: Operators might be “rounding off” values before recording data. Subgrouping might not be rational. A measuring instrument might not be suited for the task or might be damaged or out of calibration.
To overcome this obstacle: Before the SPC initiative, set rules for data collection and analysis. Criteria should include the least number of significant digits for the measurement system, how much error (including gauge Repeatability and Reproducibility, bias, and linearity studies) is acceptable, calibration frequency for measurement instruments, rules for determination of outliers, and which actions to take with outliers. Sampling practices must be evaluated to prove rationality, and the sampling frequency must be sufficient to detect shifts in the process.
All the tools you need to get the job done.
When it comes to real-time Statistical Process Control (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.
InfinityQS® takes you farther. Our sophisticated analysis tools give you the ability to view data across product codes, lines, or sites—all on one report. And that’s just the beginning. Regardless of your manufacturing process—high volume/low mix, or low volume/high mix—InfinityQS has the right analysis tools for your unique situation.
This is real-time, real-life SPC.
Get the flexibility to meet your needs now and into the future, both in terms of functionally and implementation. You can choose from an on-premises solution or a cloud-based platform. Do it yourself or allow our experts to help you maximize the return on your SPC investment.
InfinityQS serves the real-time quality needs of all industries. We have designed our SPC solutions with flexibility to meet the widest possible range of scenarios and to support a big-picture view of your entire operation.
Your real-time SPC solution shouldn’t slow down your production line. Our solutions are plant-floor friendly and provide fast setups, fast data collection, and even faster data analysis. Plus, we help you expose process improvement opportunities you never knew existed, so you can save even more time and resources.
You have enough on your plate. Your real-time SPC solution should reduce burdens—not add to them. InfinityQS quality solutions help reveal the most important information for you automatically, so you can act immediately.
At InfinityQS, all of our salespeople, engineers, and quality experts hold a Six Sigma Green Belt certification. And we employ statisticians and Six Sigma Black Belts. We are committed to providing our clients real-world experience in quality technologies, manufacturing, statistics, and process control. With nearly 30 years of expertise in the real-time SPC market, InfinityQS understands your needs and how to solve your greatest challenges.