Your quick reference to statistical process control for manufacturing quality management systems.
Don’t miss out! Book a demo of our specialized SPC software and unlock immediate improvements in your processes.
The comparison of a measurement instrument or system of unverified accuracy to a measurement instrument or system of known accuracy to detect any variation from the required performance specification.
The total range of inherent variation in a stable process; determined by using data from control charts.
An identified reason for the presence of a defect or problem.
Also called a fishbone diagram or an Ishikawa diagram (after its developer).
A quality control tool used to analyze potential causes of problems in a product or process.
See Count Chart.
A line on a graph that represents the overall average (mean) operating level of the process.
Also known as Central Limit Theorem Formula.
An important statistical theorem that states that subgroup averages tend to be normally distributed even if the output overall is not. This concept allows control charts to be widely used for process control even if the underlying process is not normally distributed.
Also known as Measures of Central Tendency.
The tendency of data gathered from a process to cluster toward a middle value somewhere between the high and low values of measurement.
A factor, element, or measure that defines and differentiates a process, function, product, service, or other entity.
A tool for organizing, summarizing, and depicting data in graphic form.
A simple data recording device. The check sheet is custom-designed by the user, which allows him or her to readily interpret the results.
The listing of possible defects of a unit, classified according to their level of severity. Commonly used classifications include: A, B, C, or D; critical, major, minor, or incidental; and critical, major, or minor. A separate acceptance sampling plan is generally applied to each class of defects.
Cause of variation that is inherent in a process over time. A common cause affects every outcome of the process and everyone working in the process. Also see Special Cause.
Pertains to sampling and the potential risk that bad products will be accepted and shipped to the consumer.
A method of manufacturing that aims to move a single unit in each step of a process, rather than treating units as batches for each step.
Also known as Continuous Quality Improvement and Continual Improvement.
The ongoing improvement of products, services, or processes through incremental (over time) and/or breakthrough (all at once) improvements.
Used when the product is manufactured in a continuous flow and is not able to be grouped into lots (batches). Two parameters are considered: Frequency (f) and Clearing Number (i). This is a progressive type of plan in which the Clearing Number is X (example = 60) and the frequency is 1/X (example = 1/20). The manufacturer inspects 100 percent of the product until (i)=60 is reached. If defect-free, the Frequency (example = 1/20) applies and now every (f)=20th sample is inspected. If at least one defect is found in the first (i)=60, 100-percent inspection continues until the Clearing Number is reached.
A graph used to study how a process changes over time. Frequently shows a central line to help detect a trend of plotted values toward either upper or lower Control Limit.
Also known as Process Control Limit and Natural Process Limit.
The boundaries of a process within specified confidence levels expressed as the Upper Control Limit (UCL) and the Lower Control Limit (LCL).
Written descriptions of the systems for controlling part and process quality by addressing the key characteristics and engineering requirements.
A solution meant to reduce or eliminate an identified problem.
A measure of the relationship between two data sets of variables.
The costs that would disappear if systems, processes, and products were perfect. These costs are organized into four categories: internal failure costs (costs associated with defects found before the customer receives the product or service); external failure costs (costs associated with defects found after the customer receives the product or service); appraisal costs (costs incurred to determine the degree of conformance to quality requirements); and prevention costs (costs incurred to keep failure and appraisal costs to a minimum).
A means to quantify the total cost of quality-related efforts and deficiencies. Considered by some to be synonymous with COPQ.
A Control Chart for evaluating the stability of a process in terms of the count of events of a given classification occurring in a sample. Commonly referred to as a c-chart.
See Attribute Data.
Also known as a u-chart.
A type of control chart used to monitor count-type data where the sample size is greater than one, typically the average number of nonconformities per unit.
A measure of dispersion, sometimes described as the engineering tolerance divided by the natural tolerance. The ratio of tolerance to 6 sigma (i.e., the Upper Specification Limit [USL], minus the Lower Specification Limit [LSL], divided by 6 sigma.
Also known as Process Capability Index.
Equals the lesser of the Upper Specification Limit minus the mean divided by 3 sigma or the mean minus the Lower Specification Limit divided by 3 sigma. The greater the Cpk value, the better.
A type of control chart used to monitor small shifts in the process mean. It uses the cumulative sum of deviations from a target.
The maximum number of defects or defectives allowed in a sample from a lot (batch) of product to consider that lot acceptable.
Also known as Acceptable Quality Limit, Acceptance Quality Level, Acceptable Quality Level, or AQL level.
The AQL is the lowest tolerable average (mean) of a process in percentage or ratio that is still considered acceptable.
A method of inspection in which statistical sampling of a lot (batch) of product is used to determine whether that lot of product is acceptable. Acceptance sampling comprises two types: attribute sampling and variable sampling.
The specific criteria by which a product is to be examined for acceptance utilizing Acceptance Sampling methods. The size of the lot (batch) of product combined with the Acceptance Quality Limit, as well as other considerations (depending on the plan being used and the characteristics being inspected), determine the sample size as well as the acceptance number. Some of the most commonly used standards today are ANSI/ASQ z1.4 (Attributes), ANSI/ASQ z1.9 (Variables), Lot Tolerance Percent Defective (LTPD), and Zero Acceptance Number (as described by Nicholas Squeglia in Zero Acceptance Number Sampling Plans, ASQC Quality Press).
The difference of agreement between an observed value and an accepted reference value.
A statistical procedure for troubleshooting industrial processes and analyzing the results of experimental designs with factors at fixed levels. When you need to compare multiple group means, you can use the ANOM as an alternative to the one-way analysis of variance F.
Also known as Variance Analysis, ANOVA Variance, ANOVA Analysis.
A basic statistical technique for determining the proportion of influence that a factor, or set of factors, has on total variation. ANOVA tests for differences between means; it’s similar to many other tests and experiments in that its purpose is to determine whether the response variable (i.e., your dependent variable) is changed by manipulating the independent variable.
Also known as AS9100 Standard and AS9100 Quality.
A widely adopted and standardized quality management system for the aerospace industry. It is known as EN9100 in Europe and JISQ9100 in Japan.
Also known as Assignable Cause Variation.
An identifiable, specific cause of variation in a given process or measurement. Also see Special Cause.
Also known as Go/No-Go information.
Qualitative data that can be counted for recording and analysis. Control charts based on attribute data include: percent chart, number of affected units chart, count chart, count-per-unit chart, quality score chart, and demerit chart. Also see Go/No-Go.
See Mean.
Also known as Averages Control Chart.
A control chart in which the subgroup average, X-bar, is used to evaluate the stability of the process level. Also see X-Bar Chart.
Also known as Average Outgoing Quality Formula.
The expected average quality level of an outgoing product for a given value of incoming product quality. Depends on the incoming quality, the probability that the lot will be accepted, and the sample and lot sizes.
Represents the maximum percent defective in the outgoing product. AOQL is the maximum average outgoing quality over all possible levels of incoming quality for a given acceptance sampling plan and disposal specification.
The number of points, on average, that will be plotted on a control chart before an out-of-control condition is indicated (e.g., a point plotting outside the control limits).
An imperfection severe enough to be noticed but that should not cause any real impairment with respect to the intended normal, or reasonably foreseeable, use. Also see Defect, Imperfection, and Nonconformity.
The offset of a measured value from the true population value.
Also known as Binomial Distribution Formula.
A discrete probability distribution used for counting the number of successes and failures or conforming and nonconforming units. This distribution underlies the p-chart and the np-chart.
A plot used in exploratory data analysis to picture the centering and variation of the data based on quartiles. After the data are ordered, the 25th, 50th, and 75th percentiles are identified. The box contains the data between the 25th and 75th percentiles.
Every manufacturing quality management professional who uses statistical process control (SPC) runs into questions occasionally. That’s why we’ve compiled this SPC glossary to serve as a quick reference when you’re looking for an answer, need to explain a concept to a colleague—or just can’t remember that term that’s on the tip of your tongue.
Feel free to bookmark this reference so you always have the definition you’re looking for—and be sure to visit our other SPC reference resources.
WHAT IS STATISTICAL PROCESS CONTROL? Learn the definition of SPC and what this industry-standard methodology is used for.
SPC 101 Dig in deeper to understand why and how SPC is used in manufacturing quality control.
DEFINITIVE GUIDE TO SPC CHARTS Learn why and how to use different control charts, see examples, and explore use cases.
Get big benefits from solutions designed for the way manufacturers work.
At InfinityQS®, we design and support practical solutions. Our expert industrial statisticians bring Six Sigma Black Belt certification and experience in the areas that matter most:
Our customers report measurable improvements and a robust ROI. It’s just one reason InfinityQS has a 97% client retention rate and a 94% client satisfaction rating across thousands of clients and tens of thousands of installations.
We support data that many other vendors don’t, including non-normal distributions, short runs, and startup activities.
Real-Life Client Results 14.4% average reduction in data-collection time 17.1% average reduction in reporting time
“No other system would allow us to integrate real-time process data from disparate systems into MES or launch automated alerts and actions to give our engineers intelligence and feedback. InfinityQS has proven vital in resolving issues we didn’t even know we had.”
Easily analyze quality data to optimize processes, minimize waste, and uncover significant savings.
Real-Life Client Results 12.7% average reduction in weekly scrap14.1% average reduction in warranty claims 12.9% average reduction in defect costs 10.7% average reduction in escapes
One InfinityQS customer saw a 66% annual dollar savings from reduced scrap alone.
We offer both on-premises and cloud-hosted SPC solutions. Get the most from your SPC investment by leveraging InfinityQS training, engineering, and help systems to tailor your deployment to meet your unique needs.
Real-Life Client Results 14.1% average reduction in overtime 14.3% average reduction in man-hour rework
InfinityQS solutions help you turn quality from a problem to a profit center.
What to Expect
A manufacturing quality platform makes it easier for manufacturers to apply quality control data.
Manufacturers produce a lot of data—sometimes more than they can collect, and often more than they can use. Having data isn’t a manufacturing problem. Making sense of it is. Manufacturing companies need help identifying their most useful metrics—right now, and for the long run.
A comprehensive approach to quality management answers the data overload problem and brings clarity to the most complex data (and quality) challenges. A quality management discipline standardizes data practices, so leaders have access to data when they need it—and confidence in the quality of that data.
With the right information at hand—pooled together without a herculean effort—managers can spend more time asking questions and exploring possibilities. Leaders can find ways to apply Statistical Process Control (SPC) to optimize quality and manufacturing processes overall. And they can proactively respond to manufacturing opportunities instead of always reacting to challenges.
Learn to use quality metrics to shift your operations into a more proactive quality management mode.
Quality metrics demonstrate how the organization is performing on waste reduction, quality control, and responsiveness, as well as other measures that matter to anyone making, using, or investing in your products.
Just like performance, quality metrics can change minute by minute (or second by second). That’s why it’s important for quality management practices to enable real-time visibility. With instant views of quality metrics, production staff and plant managers can make the right decisions in the moment. Likewise, analysts and executives can use the same data to steer the organization toward positive strategic outcomes.
Quality metrics are actionable at every level—if they’re complete, consistent, and accessible.
Does everybody in the organization really need access to real-time, SPC-based metrics?
Yes—although everyone doesn’t need the same control charts. Some users may not need control charts at all. A quality platform consolidates all your quality data into one place, then automatically presents the most pertinent information to users based on role and responsibility.
Here are examples of how SPC-based quality control metrics apply to different roles in your manufacturing organization:
Operational staff, including plant managers and production leaders, use SPC-based data to monitor quality and manage potential issues on the line in real time. They use data and quality analytics tools to manage:
With SPC-driven data, operators can investigate quality issues on the spot—and test new quality initiatives.
Quality control and process improvement professionals use real-time and historical quality metrics to monitor trends. By looking at key quality metrics across lines, products, and locations, they can uncover optimal manufacturing processes for the entire organization. They use quality metrics to improve:
Quality platforms standardize data collection and reporting—making quality data more complete, accurate, and accessible. That makes it more auditable and actionable.
Manufacturing executives rely on accurate quality metrics to guide organizational strategies. When SPC-based quality metrics are accessible, the executive team can:
Quality control data can answer all these manufacturing needs—using the right quality analytics tools.
What would happen if you only read 2% of your emails?
That’s essentially how many manufacturers approach their quality data: they dig into exception data and ignore the vast majority of quality metrics. By doing so, they miss opportunities to optimize manufacturing across the company.
But here’s some good news: Manufacturers already collect the data they need to improve performance. Once it’s standardized and centralized, quality priorities come into focus—and data can be used to proactively improve quality, customer satisfaction, and profitability.
Manufacturing companies of every size—in every industry—can benefit from monitoring the following common quality metrics:
Cost of Quality (COQ) is possibly the most important metric because it captures two perspectives: the cost of poor quality and the cost to invest in good quality.
Here’s how: First, COQ accounts for internal failures—such as scrap and rework—and for defects that reach the customer and have to be resolved through warranties, corrections, and adverse event reporting. COQ also tracks proactive audit costs, like product inspections and quality tests, and preventive measures to protect quality—such as SPC, quality planning, and training.
Companies that embrace quality as a discipline spend less on quality—across the entire organization. In fact, quality becomes a key driver for cost avoidance and other strategies that improve profitability.
If the answers to quality improvement are in the data, then you need to know the right questions to ask. If you’re not sure where to start, try asking your business and operations leaders these four questions:
1. How do you identify your biggest opportunities for process and product quality improvement? A quality platform centralizes quality data from across your company—making it easier to analyze. Built-in analytics do most of the data aggregation, slicing, and dicing automatically, exposing—in bold colors and charts—where to take action.
2. Once you identify opportunities, how do you prioritize resources to address them? Based on goals that you establish, a quality platform grades process performance across products, processes, and sites. With report card-like grading systems, quality opportunities are easier to see—and prioritize.
3. How do you know people are collecting the data they are supposed to collect? How confident are you that data collections are happening on time, every time? On the right form? In the same format? And are you notified when data collections are missed?
Modern quality solutions eliminate these worries—and improve the accuracy of your quality data. Technology standardizes collection methods across the company and calculates results in a standardized manner. An intelligent quality management solution alerts operators when collections are due and notifies mangers when collections are missed. When operators are empowered to stop wasting time babysitting data collection, they can spend more time understanding and applying that data to process improvement.
4. How do you know what your biggest challenges are? When you have so much data collected, opportunities hide in the blind spots—especially if managers and operators have to dig through control charts or reformat spreadsheets to make them useful. It’s no wonder that quality data is often only evaluated monthly or quarterly; it takes that long just to compile and format it.
A purpose-built manufacturing quality platform automates important calculations and unites them in a dashboard view—in real time. Meaningful information rises to the surface, and leaders can click into supporting details to uncover root cause—or opportunities—so they can start developing resolutions immediately.
See Enact in action—and how easy it is to activate your quality data using charts, dashboards, and tiles.
Modern quality management tools make it easier to extract value from your quality metrics. By uniting your quality data, leaders can view their entire organization in a whole new way.
With more accurate and complete quality data, manufacturers can answer complex questions—and meet different users’ quality needs.
Dashboards are configurable for different roles within an organization. The same data, when explored differently, can solve urgent issues on the plant floor—or pinpoint company-wide best practices.
Take a tour of Enact operator dashboards.
Other visual displays, such as bubble charts, help leaders compare quality metrics across sites. From there, managers can dig into supporting information to explain performance variations (such as on-time data collections or yield) to uncover improvement opportunities.
Learn how it’s possible to prioritize improvement opportunities.
Data Stream Grading is an advanced analytics tool that rolls up quality metrics across an organization—and lets you drill down into specific details. Data Stream Grading measures performance yield against your potential, then assigns it a grade. From there, it’s easy to see quick wins and high-impact projects.
See a Data Stream Grading “report card.”
Modern SPC tools can help hold you accountable to the highest quality management standards.
Caring about quality is nice. But it doesn’t improve products, performance, or profit. To do that, you need to establish standards—quality principles—across your organization.
Strong quality principles are supported by data—and provide targets that everyone in the company can pursue.
How do you set quality principles? Industry groups and international quality standards, such as ISO 9001 and ISO 22000, pave the way. Standards and accrediting organizations offer foundational quality management principles, as well as a baseline for quality management.
But they’re not the end state, or even the limit on what you can achieve. When you master quality management, these principles become more than just items on a checklist—they become ingrained in your organization’s culture. Quality sits at the center of all daily activities, as well as big-picture decisions, conversations, and plans.
When quality becomes a part of everything you do—and how you do it—compliance with industry standards is simpler. Learn how to make the principles of quality management an essential part of your workplace culture.
Many quality standards and compliance requirements are established externally. Sometimes customers set the bar, but most often industry bodies and action groups establish requirements. The International Organization for Standardization (ISO), for example, issues quality management principles to help manufacturers work more efficiently and reduce product failures.
Standards established by the ISO and others became the “norm,” and often dictate best practices. Their quality management principles influence how things are done—and what customers expect.
Setting standards is a great first step. But without measurement, it’s impossible to make progress. Statistical Process Control (SPC) methodology, which many manufacturers already use to control quality, is an important tool for measuring quality compliance. In fact, some certifications—such as SQF from the Safe Quality Food Institute—require the use of SPC to comply with safety and quality standards.
Manufacturers meet external quality standards to achieve certification—and to validate to customers and prospects that they’re operating in the most consistent and productive manner. Standards such as ISO 9001 cover more than just the plant floor—they address how quality permeates leadership, engagement, relationship management, decision making, and more. That’s why quality management principles are an important piece of building a culture around quality.
The ISO 9000 family of standards are based on seven quality management principles:
Every industry has a set of quality management principles—basic concepts or standards of quality—to comply with. In organizations that master quality, these principles are embedded in daily language and decision making and set the bar for quality.
The ISO establishes quality standards and principles that apply to manufacturers worldwide—regardless of product or output. Manufacturers are expected to follow ISO standards in addition to product-specific or geographic requirements. Food handling, for example, is held to different standards than car parts or computer chips.
The guidance from industry groups can be very specific, even granular. Here are some of the most common quality standards that are applied in manufacturing:
ISO 9001 Some of the ISO’s best-known standards fall under ISO 9001. It applies to manufacturing operations broadly, regardless of company size, location, or industry.
ISO 9001 builds on the seven quality management principles to build efficiencies, meet statutory and regulatory requirements, and put customers first. To achieve ISO 9001 certification, companies must document how they apply, track, and manage ISO’s quality management principles.
ISO 22000 ISO 22000 provides safety standards for the global food supply chain. These standards benefit consumers, of course, but also protect food and beverage manufacturers that work with global growers, suppliers, transport companies, and retailers.
Through Hazard Analysis and Critical Control Points (HACCP), ISO prescribes proactive measures to lower contamination risk and protect food. The seven principles of HACCP are designed to stop hazardous materials from entering the production process—as opposed to identifying them during final inspection.
Good Manufacturing Practice Good Manufacturing Practice (GMP) provides standards for quality governance in highly regulated industries—such as pharmaceutical and medical device manufacturing, cosmetics, and food and beverage manufacturing. Regulations cover manufacturing process, facilities, and personnel—all to ensure consumer safety. GMP requires equipment and product testing, employee competencies, and thorough documentation.
In the U.S., the Food and Drug Administration enforces GMP standards and regulations; Health Canada, the European Commission, and the World Health Organization regulate GMP worldwide.
Safe Quality Food The Food Industry Association created the Safe Quality Food (SQF) Program, a rigorous “farm-to-fork” certification to control food safety risks. It ensures that suppliers have produced, prepared, and handled food according to international and local food safety regulations—and to the highest possible standards.
The SQF Program is broken down into levels and codes, many of which build upon the HACCP rules established by the ISO. They cover food safety fundamentals, safety and quality, and ethical sourcing. Auditing is a core component of SQF, as is third-party assessment.
IATF 16949 IATF 16949 is the international standard for automotive quality management systems, which was established jointly by the International Automotive Task Force (IATF) and the ISO. It applies to any manufacturing organization that makes components, assemblies, or parts for the automotive industry.
IATF 16949 encompasses the QMPs of ISO 9001, but it is process oriented, too. To earn certification, manufacturers must demonstrate how their quality management processes support continuous improvement, prevent defects, and reduce variation and waste in the supply chain.
See how a modern SPC solution can support industry-required quality management principles. Put quality data to practical use—and see dramatic improvements in your manufacturing organization.
When they’re applied correctly, quality management principles aren’t just checklists. Sure, there are lots of processes and control measures to check along the way, but the benefits to the organization are systemic.
A manufacturing quality platform unites your quality metrics and exposes context and purpose. With modern SPC software and analytics tools, you can spot quality challenges and opportunities more clearly.
Whether you’re pursuing a certification, preparing for an audit, or trying to continuously improve operations, a manufacturing quality platform helps you comply—and surpass—the most stringent quality standards. Modern tools support compliance, operational improvement, and decision making—and make it easier to get a handle on the bigger quality picture.
Our Director of Technical Services explains.
Are your operators and quality professionals drowning in data? Or do they ignore it because it’s overwhelming?
A quality platform filters data for users automatically, and presents them with only the information they need—when they need it. You set the parameters, and the software lets you know when quality checks are due or processes are out of spec. That way, users can focus on their jobs—instead of babysitting compliance activities. Quality becomes embedded into your manufacturing processes, and proving it doesn’t take center stage.
Watch this video to learn how quality intelligence can be tailored for each user.
Unifying quality and compliance data is important. But then what? How do you make sense of the data? Or apply it to continuous improvement efforts?
Quality platforms simplify analyses—across multiple production lines, products, and locations. Managers can access data from anywhere and compare the information in a standardized format—no spreadsheet manipulation required.
Being able to dig into data—rolling it up organization-wide or drilling down to a particular worker or line—gives manufacturing leaders a distinct advantage. They can pinpoint what’s working—and what’s not—and create replicable best practices across the organization. They can also quantify the value of quality improvements to help prioritize initiatives.
Learn how centralized and standardized data enables clear prioritization.
Your quality management plan can deliver bottom-line benefits.
If it ain’t broke, don’t fix it—right? Wrong. When you invest in quality, you lower costs.
Every manufacturer knows that scheduled and preventive maintenance are required to keep operations moving at top capacity. And upgrades are expected to stay ahead of competitive threats, especially when it comes to customer-facing channels and back-office capabilities.
And yet, many quality professionals still use clipboards, pencils, and paper to manage quality on the plant floor. Even top-of-the-line, new equipment is monitored manually.
Plant workers are ready for modern quality management tools, and for more efficient ways to do their jobs. Technology isn’t a barrier for operators. In fact, technology is a natural part of their daily lives—until they get to work. It’s time to equip your workers as modern manufacturing professionals.
With the right quality management tools, plant operators can work more collaboratively with managers, quality professionals, and C-suite executives. Together, they can uncover the greatest opportunities to transform quality—and meet strategic objectives—across the company.
Investments in quality far outweigh the costs of mistakes, inefficiency, and waste. And modern quality management tools build accountability, engagement, and quality into everyday work tasks.
When you give workers the best—and right—tools for the job, the benefits add up fast.
Learn how to quantify the advantages of a strong, technology-driven quality management plan—and build a business case for modernization.
Does change sound painful? Time consuming? Too difficult to justify?
Remember, quality management is a discipline—it means practicing and improving quality management every single day. That requires investing in quality.
Luckily, digital quality management tools can eliminate some of the most challenging aspects of change.
When quality control data is monitored continuously—in real time—issues get detected and resolved more quickly. At the plant level, digital tools speed up data collection and improve the overall accuracy of the information gathered. With digital data collection, handwritten errors, incomplete information, and inconsistent entries aren’t added to the data set. Digital tools also automate analytics and alerts, ensuring that the right people are notified to act—as issues occur.
At higher levels in the organization, leaders can compare SPC data across shifts, processes, lines, and plants. Reports and dashboards are created automatically, so it’s easier to discover actions that optimize quality. Analysis is more effective and efficient, and best practices can be applied across the organization.
Manufacturers want their production lines to run as smoothly as possible and as close to capacity as possible. They use quality control practices to prevent inconsistencies or other failures (such as equipment or raw material defects) that delay operations.
Since most quality managers focus only on their line or shift, productivity gains are limited. But with an enterprise view of quality control data, organizations can compare performance across products, lines, sites, and other variables and arrive at best practices. Then, they can be replicated for greater organizational gains.
Quality control practices help manufacturers meet customer expectations and compliance standards. Quality measures that ensure product consistency, such as net weight and yield, are tracked multiple times a shift. But if those figures sit on a clipboard or get filed away after each shift, they don’t contribute to continuous improvement.
Improving quality and consistency is important for the plant floor—and even better for the enterprise. When quality data is collected digitally and stored in a centralized location, leaders are able to extract more insight and value from the numbers. It’s faster and easier for executives to spot emerging trends and make strategic decisions about quality processes, goals, and investments. With an enterprise view of quality control data, manufacturers can maintain product consistency across lines, shifts, and locations.
To improve profitability, manufactures need to reduce scrap, waste, rework, and recalls. But to fix problems, operators have to be able to see problems. Quality managers use SPC to spot out-of-spec issues that lead to costly mistakes. With elevated, SPC-based quality control practices, you can stop problems before it’s too late to salvage time and materials.
At the line and plant level, quality control improves efficiencies, and can save hundreds or thousands of dollars each shift. Extend those capabilities across the organization, and manufacturers could save millions of dollars. Consolidated, comprehensive, accessible data gives manufacturing leaders more power over the bottom lines.
Most manufacturing companies build their brands around quality. But how do they measure it? How do they prove it?
Customer satisfaction, certifications, and successful audits help tell your quality story—when (or because) it’s supported by data.
When SPC data is collected, stored, and reported digitally, it’s easier for manufacturers to validate product quality. In mere minutes, you can verify that checks were completed correctly and on time, and you can respond to customer inquiries—in detail—about specific days, shifts, lines, or lot numbers. Precise tracking helps managers pinpoint root cause, and take immediate action to protect the brand.
These benefits also roll up to the corporate level. Using enterprise-wide information about quality control and performance, leaders can make informed strategic decisions and investments that continuously improve quality—and brand positioning.
And what about those gains? Once change has been implemented, everyone starts to experience the benefits of modern manufacturing technology.
Operators need data and control charts to make decisions (e.g., Do I adjust a machine or not?). But there’s a lot of data to slog through to figure out what matters. It’s time consuming, tedious, and error prone. Operators risk missing an important trend, or jumping in to “fix” things before they need remediation. Modern quality management tools help plant-floor operators focus on their specific areas—not everything all at once. • The data they need is automatically calculated—and charted into formats they can use • Relevant data collections are presented up front to reduce distractions • Notifications and alerts let operators know when rule violations or other issues need their attention • Quality issues are automatically documented, graded, and prioritized
Learn how a carton manufacturing plant empowered its operators to eliminate defects.
Plant managers and quality professionals need data to develop and measure process improvements. But if they rely on manual processes, they may spend more time managing data than managing quality.
Manual data collection is time consuming—and may result in critical analysis gaps. Entries could be missing, inaccurate, or incomplete. And without the ability to efficiently filter and organize information, quality data quickly loses its meaning and value.
Digital quality management tools take away painstaking data management tasks, enabling managers and quality professionals to see the insights in their data faster—and more clearly.
Learn how quality managers found opportunities in their quality data.
Manufacturing leaders need to ensure product quality and consistency across the entire company—not just for a single line or shift. And they need to find high-impact improvements that move the company toward its strategic goals.
If data is managed manually—or differently by site or product—it can be challenging for leaders to enact meaningful change. There’s too much data—with too much variation—for any of it to be actionable. Opportunities are lost in the weeds, issues are difficult to prioritize, and it’s impossible to replicate best practices.
Modern Statistical Process Control (SPC) software centralizes and unifies quality data, giving leaders better “raw material” to build decisions with. Quality intelligence tools also analyze the data automatically—and present it graphically to executives for quick decision making.
Learn how a metal-forming manufacturer was able to improve quality and dramatically improve its bottom line.
A quality management discipline requires investment—but it’s not a line-item expense. Quality management practices cut across departmental boundaries to improve operations—and lower manufacturing costs—overall.
With a focused quality management plan, you could:
Quality management has a direct link to the bottom line. So how do you start generating ROI from your quality management plan?
With SPC-driven quality management tools and plans:
Can you see opportunities for improvement? You may suspect that your processes are underperforming, but with modern, digital quality management tools you can actually see the difference quality makes.
nEnact empowers you to quickly realize the benefits of digital data collection and analysis. Start today with:
Choose a single line or machine to serve as the foundation for your rollout—and a blueprint for your organization.
A focused deployment on a small scale shows operators how to use digital tools to capture data—making tedious manual data collection a thing of the past. Managers and executives will learn how digital analysis and reporting work and can explore the impact.
To plan a successful proof of concept:
During the pilot phase, you’ll apply lessons learned during the proof of concept to expand across the facility. As you add more products and parts to the quality platform, you can include additional functionality—and reporting will become richer. Then you will start to see the significant time savings provided by automated data collection, reporting, and analyses.
During the pilot:
Lather, rinse, and repeat! After a successful proof of concept and pilot, it’s time to apply your blueprint to other parts of the company. You can expand your modernized quality management process to other lines, facilities, and processes—and throughout the organization. As you continue developing your quality management approach, data and value will grow.
With accurate, consistent, and complete data from across your manufacturing organization, the business case for quality becomes stronger and stronger. Once fully deployed, you can:
“At the beginning of our proof of concept, the plant manager wasn’t into it. By the end, he was in love with it—and is now Enact’s biggest advocate. We’re actually using the system to effectively communicate between quality and production.”
— Jegadish Gunasagaran, QA Associate Bakery on Main
Go from “doing quality” to mastering it—and get more value for your quality management investment.
Quality practices have to be carried out for compliance reasons and to meet customer agreements. But traditionally, they’ve returned little value to the organization beyond “checking the box.”
Sure, site-level analyses translate to incremental improvements, but they don’t transform organizational performance. Quality control has been essential in manufacturing—but it hasn’t necessarily been influential.
That doesn’t have to be the case. Quality control measures can be goldmines for product improvement—and for reaching high-level strategic goals, such as lowering costs and mitigating risk. The insight you need to make more accurate and impactful decisions is being collected—probably right now—through your quality control processes.
These activities accumulate massive amounts of data—and some of your most important operational metrics. Quality control data encompasses nearly every aspect of your business, from suppliers and raw materials to equipment, people, processes, and final product inspections. All that data can be used to inform higher-level decision making—to elevate the impact of quality control in manufacturing.
To extract more value from quality control data, manufacturers need easy access to Statistical Process Control (SPC) data. Quality control tools need to be standardized, comprehensive, and actionable—for users at every level of the business.
You collect quality control data anyway, right? Why not use your information more effectively? Learn how to get more value out of the quality control practices you already have in place.
Make it better. Make it faster. Make it cheaper.
Manufacturers are under constant pressure to reduce waste and eliminate inefficiencies across processes—and to deliver the highest quality product at the lowest possible price. Along the way, you also need to balance customer expectations, regulatory requirements, and business goals.
Quality control practices are an essential way to track everyone’s requirements, as well as to assign standards and acceptable manufacturing ranges. Once established, quality control teams measure performance against these standards. And measure. And measure. And measure.
Then what? Based on SPC triggers, quality managers address core quality issues such as variability, volatility, and unpredictability. But their purview is limited, sometimes to a single product, line, or shift. And their goal is to fix an issue—as quickly as possible—and move on. Like a fire department for quality concerns. When managers return to the data, it’s usually to verify that corrective actions worked, and that production returned to the statistically acceptable status quo.
Quality control is essential to manufacturing. Without the right corrective actions, production lines could stand still. And without exceptional quality, manufacturers risk everything—reputation, sales prospects, and profitability.
And yet, quality control practices can do more. Using data they already collect, manufacturers can advance their organizations—not just defend and protect them. With enterprise software and quality control tools, data can reveal magnitudes of opportunity. Integrated SPC software makes quality control important—not just in manufacturing, but to the organization overall.
Consider how these benefits of quality control in manufacturing build value across the entire business.
With today’s advancements in digital technology, the best place to store this database—and your quality platform—is in the cloud. Doing so significantly reduces deployment and maintenance costs while increasing agility and supporting scalability.
Integration is a key tenet of the Enact quality platform. This integration takes multiple forms:
There’s a huge difference between “doing quality” and “mastering quality” in manufacturing.
Companies that do quality control focus heavily on data collection and action. They spend the bulk of their time checking boxes: Was data collected? When? By whom? They only look for insights if corrective action is required. And in those instances, analysis usually ends once the urgent issue is resolved.
Mastering quality control uses the same data—but applies it differently. Masters spend more time and energy proactively analyzing data, and compare metrics from across the organization to uncover best practices and opportunities. They take action after they analyze.
An analytical approach to quality management empowers manufacturers to be proactive and strategic with their quality control efforts. They uncover opportunities that support big picture strategic goals. They use quality control as a strategic lever to achieve corporate goals.
At the practical, day-to-day level, here’s what it looks like to shift from tactical quality control to strategic quality management.
Organizations that do quality control:
Organizations that master quality control:
When SPC tools are fast, accessible, and easy to use, manufacturers can analyze issues before they take action. With enterprise-wide SPC, you can finally have the tools you need to proactively address quality.
Enact empowers you to quickly realize the benefits of digital data collection and analysis. Start today with:
Your quality management program must support tactical, everyday quality requirements—and bigger-picture strategic goals. How can quality control programs balance both these needs and add value to the organization?
An enterprise-wide, digital SPC solution meets a diverse set of user requirements. The right solution helps you measure quality in real time, prevent costly problems, and reduce risk across the enterprise.
Here’s how a digital enterprise SPC solution can help you balance tactical and strategic quality control needs.
When choosing a quality platform, work with your provider to pin down exactly what you want from the platform:
The best way to launch a successful Enact deployment is via a targeted, small-scale deployment. By starting small—one filling or packaging operation, for example—you can experience Enact and build a foundational understanding of how your quality platform can work.
You begin by setting up an Enact subscription. Then, the in-app Quick Startup enables you to set up a simple data collection right away. You can immediatetly start collecting data and see how Enact works in your manufacturing production environment. When you encounter questions or need help, the Enact Guided Learning Center provides helpful videos and online tutorials.
You get to experience Enact’s dashboards, discuss feedback from your team, and evaluate the results of your initial setup. In other words, you get to see firsthand what Enact can do for you—and how it can help you extract meaningful business insights from your manufacturing quality data.
Key considerations and best practices for smart, data-based decision making.
What do we mean when we talk about a quality platform? Your manufacturing organization needs to prioritize decisions that will bring the greatest business benefit and help you optimize production operations. A manufacturing quality platform enables you to digitize critical quality and process data so that you can gain strategic insights into your operations. To gain that insight, you must have access to your quality and process data in digital form. A well-designed quality platform can fuel a digital transformation for your entire manufacturing ecosystem.
The knowledge and visibility contained in your quality data powers strategic decision making—not just across one product or line, but across your entire enterprise. By taking quality data out of disjointed, discrete systems—and by automating how you collect and work with that data—you can uncover opportunities to:
What does this mean for you? For one, proactive quality insights reduce the number of nail-biting incidents that crop up. It can alert you to variances before they become defects—or worse, escapes. It can help you determine which areas of a line, facility, or region need the most attention. And it can help you establish best practices and propagate them across all your facilities.
It can be challenging to align your teams and gain the buy-in necessary for selecting the best cloud-based quality platform for your company. That’s why we created a step-by-step buyer’s guide—to ensure that your teams have all the information they need during this important process.
You run your manufacturing enterprise using a wide range of systems, each encompassing a slightly different business focus. The interconnectivity among these systems forms your unique enterprise application architecture.
The success of your manufacturing ecosystem depends on the health—and successful integration—of these systems.
How can a quality platform fit in with your MES, ERP, MOM, and QMS? As a purpose-built manufacturing quality platform, Enact® by InfinityQS® goes beyond the confines of traditional plant floor quality management. It enables you to strategically collect, visualize, centralize, standardize, and analyze data—whatever the source—and provide essential integrations with your other business systems to facilitate meaningful analysis and actionable insights.
Using Enact, you can automate the collection of quality data from other systems, then standardize and centralize it into a unified data repository. Why is this step so important?
What capabilities should you demand in a quality platform? Remember, your quality and process data is your direct source for product quality and business insights. And that means different things to different people in your organization:
A quality platform provides a focused view of information each of these roles, when and where they need it. It delivers focused data insights efficiently and effectively, helping you optimize workflows, sort through today’s data “noise,” and save valuable time and resources.
Enact includes features—such as bubble charts and data stream grading—that make it easy to distill data across systems and locations, enabling the collaboration and prioritization that lead to dramatically effective results.