SPC Glossary: C

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Quality Management System Glossary: C

Calibration

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.

Capability

The total range of inherent variation in a stable process; determined by using data from control charts.

Cause

An identified reason for the presence of a defect or problem.

Cause-and-Effect Diagram

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.

C-Chart

See Count Chart.

Centerline

A line on a graph that represents the overall average (mean) operating level of the process.

Central Limit Theorem

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.

Central Tendency

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.

Characteristic

A factor, element, or measure that defines and differentiates a process, function, product, service, or other entity.

Chart

A tool for organizing, summarizing, and depicting data in graphic form.

Check Sheet

A simple data recording device. The check sheet is custom-designed by the user, which allows him or her to readily interpret the results.

Classification of Defects

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.

Common Cause

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.

Consumer’s Risk

Pertains to sampling and the potential risk that bad products will be accepted and shipped to the consumer.

Continuous Flow Process

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.

Continuous Improvement (CI)

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.

Continuous Sampling

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.

Control Chart

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.

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).

Control Plan (CP)

Written descriptions of the systems for controlling part and process quality by addressing the key characteristics and engineering requirements.

Corrective Action

A solution meant to reduce or eliminate an identified problem.

Correlation (Statistical)

A measure of the relationship between two data sets of variables.

Costs of Poor Quality (COPQ)

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).

Cost of Quality (COQ)

A means to quantify the total cost of quality-related efforts and deficiencies. Considered by some to be synonymous with COPQ.

Count Chart

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.

Count Data

See Attribute Data.

Count-Per-Unit Chart

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.

Cp

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.

Cpk Index

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.

Cumulative Sum Control Chart (CUSUM)

A type of control chart used to monitor small shifts in the process mean. It uses the cumulative sum of deviations from a target.

SPC Glossary: A-B

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Quality Management System Glossary: A-B

A

Acceptance Number

The maximum number of defects or defectives allowed in a sample from a lot (batch) of product to consider that lot acceptable.

Acceptance Quality Limit (AQL)

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.

Acceptance Sampling

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.

  1. Attribute sampling is a statistical method by which the lot is accepted or rejected based on one sample. The sample is recorded as a pass or fail depending on the number of defects or defectives found within that sample when compared to the Acceptance Number. Attribute inspections are typically subjective (visual) interpretations of the product.
  2. Variable sampling is similar to attribute sampling. However, rather than recording the number of defects, each piece within the sample from the lot of product is measured and those values are assessed against a specification limit. The result of that assessment may indicate the lot passes or fails. Sample sizes for variable sampling are usually smaller than attribute sampling because measurements are more accurate than subjective interpretation.

Acceptance Sampling Plan

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).

Accuracy

The difference of agreement between an observed value and an accepted reference value.

Analysis of Means (ANOM)

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.

Analysis of Variance (ANOVA)

Also known as Variance AnalysisANOVA VarianceANOVA 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.

AS9100

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.

Assignable Cause

Also known as Assignable Cause Variation.

An identifiable, specific cause of variation in a given process or measurement. Also see Special Cause.

Attribute Data

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.

Average

See Mean.

Averages Chart

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.

Average Outgoing Quality (AOQ)

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.

Average Outgoing Quality Limit (AOQL)

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.

Average Run Lengths (ARL)

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).

B

Blemish

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 DefectImperfection, and Nonconformity.

Bias

The offset of a measured value from the true population value.

Binomial Distribution

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.

Box and Whisker Plot

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.

SPC Glossary

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Quality Management System Glossary

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.

Measurable Benefits with Real-Time SPC

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Real-Life SPC

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:

  • Deep understanding of how manufacturing works in dozens of industries
  • Solving the challenges of today’s technical and economic landscape
  • Supporting the needs of operations, quality, IT, and executive teams

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.

Improve Data Collection, Analysis, and Reporting

We support data that many other vendors don’t, including non-normal distributions, short runs, and startup activities.

  • Our Unified Data Repository stores and processes data so that you can directly compare quality across multiple—
    • product codes
    • production lines
    • production sites
  • Analyze complex, real-time or historical data within one chart or report.
  • You don’t need to export and manually manipulate data to support complex analyses.

Real-Life Client Results

  • 14.4% average reduction in data-collection time
  • 17.1% average reduction in reporting time

“Resolving issues we didn’t even know we had.”

“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.”

Reduce Scrap, Waste, and Risk

Easily analyze quality data to optimize processes, minimize waste, and uncover significant savings. 

  • Reduce production scrap and waste.
  • Improve process capability.
  • Simplify communications by using automatic notifications.

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

66% annual dollar savings from reduced scrap alone.

One InfinityQS customer saw a 66% annual dollar savings from reduced scrap alone.

Optimize Manufacturing Operations

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.

  • Give your operations team an SPC solution that is easy to learn and use—and that won’t slow down production.
  • Help your quality team anticipate and prevent quality issues.
  • Use InfinityQS SPC solutions to minimize IT burden.
  • Provide your management team immediate insight into what’s happening across the company.
  • Get a real-time SPC solution that’s easy to use and affordable to try and buy.

 

Real-Life Client Results 

  • 14.1% average reduction in overtime
  • 14.3% average reduction in man-hour rework

14.1% average reduction in overtime

InfinityQS solutions help you turn quality from a problem to a profit center.

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What to Expect

  • Free 20-minute call with a product expert
  • Explore which solutions best suit your needs
  • No-pressure conversation
  • Get a live, personalized demo

How To Use Quality Metrics To Improve Quality Management in Manufacturing

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Quality Metrics Are a Manufacturing By-product That Could Be a Major Asset.

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.

Manufacturing quality metrics on their own are just the tip of the iceberg. What can your data tell you?

Who Uses Quality Metrics—and How? Learn Why Quality Metrics Matter in Manufacturing

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.

  • Complete data means you have everything you need to answer questions and solve problems.
  • Consistent data is collected and presented in the same way, no matter where it’s collected. Consistency enables comparative analyses—and accurate conclusions.
  • Accessible data is easy for the people to retrieve, whenever and however they need it—including remotely. The cloud enables companies to centralize their quality control metrics to make them widely available.

Examples of Quality Control Metrics in Action

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:

  • Quality assurance, so final products pass strict inspection standards
  • Process optimizations that reduce scrap and waste
  • Productivity, guided by data-driven decisions that decrease downtime, speed changeovers, or increase line throughput

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 control practices and oversight—even when they’re not physically present
  • Quality and process standardization by taking empirical best practices and applying them across the organization
  • Record keeping and other compliance activities

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:

  • Improve decision making
  • Promote the consistency needed to fulfill customer and brand expectations
  • Reduce cost via substantial, organization-wide process improvements
  • Increase profits by maximizing throughput, uptime, and quality

Quality control data can answer all these manufacturing needs—using the right quality analytics tools.

 

Uncover Quality Metrics To Maximize Performance

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.

Top Quality Metrics for Manufacturers

Manufacturing companies of every size—in every industry—can benefit from monitoring the following common quality metrics:

  • Defects: How many defects occur per million parts, or per million opportunities (if you have subassemblies and thus multiple opportunities for failure)?
  • Customer complaints/returns: How often do customers complain, or reject products over a designated period? Resolution activities and warranty costs are important to measure.
  • Scrap: How much material is left on the plant floor instead of becoming part of the finished product? And where does scrap originate—from vendors, setup, or other internal processes?
  • Yield: Yield is a classic measure of process or plant effectiveness. Consider calculating first-pass yield metrics, in addition to total yield, to find out how often products are manufactured correctly the first time.
  • Overall Equipment Effectiveness (OEE): How productively are you using equipment? OEE measures equipment performance by uptime (availability), output (performance), and how often products are in spec (quality).
  • Throughput: How many products can you make in a certain timeframe—per machine, line, and plant?
  • Supplier quality: How often do raw materials meet your quality specifications and requirements? How much do non-conforming materials cost the business?
  • Delivery: How often are products delivered on time, in perfect condition, and invoiced correctly?
  • Internal timing: How long does a customer have to wait—from order to delivery? How much time does it take to switch lines, introduce a new product to market, or execute change orders?
  • Capacity utilization: How does your output compare to capacity?
  • Schedule realization: How often do you reach production targets?
  • Audit metrics: Are audits completed on time? How often? And how many non-compliance notices do you receive?
  • Maintenance metrics: How often is scheduled maintenance completed on time? How many maintenance activities are planned versus emergencies? How much downtime do you experience because of maintenance—and what does that cost?

Measuring the Cost of Quality

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.

 

Asking the Right Questions

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 a New Side of Quality

See Enact in action—and how easy it is to activate your quality data using charts, dashboards, and tiles.

Leverage Quality Metrics & Optimize Manufacturing

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.”

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Quality Management Principles To Build Your Discipline

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If You Want a Company Culture That Supports Quality, Start with Established Quality Management Principles.

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.

SPC-based quality software can embed quality principles—and empower users—at every level using actionable quality insights.

What Are Quality Management Principles?

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.

Measuring Quality Compliance

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.

Why Do Quality Management Principles Matter?

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.

Start with the Basics: ISO Quality Management Principles

The ISO 9000 family of standards are based on seven quality management principles:

  1. Customer Focus: How manufacturers use quality management to meet or exceed customer expectations. Manufacturers can achieve customer focus by deeply understanding customers’ needs—and by measuring and monitoring customer satisfaction.
  2. Leadership: Leaders create working conditions that support quality and align quality to organizational strategies, policies, and processes. They can also make sure quality initiatives are properly resourced.
  3. People Engagement: People need appropriate training to support quality, but also recognition and empowerment to take initiative toward quality improvement. Engaged workers understand how their individual contributions affect quality performance, and are empowered to speak up, collaborate, and contribute to continuous improvement.
  4. Process Approach: Consistent and predictable results are a key measure of quality. This ISO 9001 principle requires manufacturers to understand and manage interrelated processes in ways that optimize performance. To do this, organizations map out interdependencies and design smooth and reliable manufacturing processes, from start to finish.
  5. Continuous Improvement: This quality management principle is designed to help manufacturers react to changes in the internal and external environment and create new opportunities. Continuous improvement affects process performance, organizational capabilities, and customer satisfaction. It also requires proactive audits, planning, and analyses.
  6. Evidence-based Decision Making: Here, ISO instills data, analysis, and evaluation as manufacturers’ best resources for success. Manufacturers need to understand the cause-and-effect relationships between various inputs and processes—and be able to objectively model consequences. To do this, manufacturers need access to accurate, reliable, and timely information, and the data needs to be available to the right people.
  7. Relationship Management: Manufacturing companies need a network of suppliers, partners, investors, and workers to produce quality products. Those relationships need to be proactively managed so that everyone stays aligned on goals, values, and quality expectations. ISO suggest that companies measure the performance of relevant parties and provide feedback to enhance quality.

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.

 

Common Quality Standards: What Are They? And What Do They Mean?

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.

From Principled to Practiced

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.

Use a Quality Platform To Put Principles into Practice

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.

Overcome Obstacles to Compliance

  • Do you have access to all the data you need if a regulator, client, or internal auditor makes a request? How can you be sure? And how long will it take for you to dig it up?Digital quality platforms remove many of the challenges related to compiling and reporting on compliance data—all your data resides in one centralized location, and it’s always within reach.Compliance metrics are more complete, accurate, and accessible when they’re digital. You can see exactly who entered data, when, and how data affected other compliance measures.

Our Director of Technical Services explains.

Help Users Focus on Their Jobs

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.

Prioritize Improvement Opportunities

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.

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  • Explore which solutions best suit your needs
  • No-pressure conversation
  • Get a live, personalized demo

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How To Sell Your Quality Management Plan

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When You Measure the Impact of Quality Management, You Can Make a Powerful Business Argument for Continuous Improvement.

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.

It’s time to modernize. Start building the business case for a technology-driven quality management plan.

How To Get Buy in? Start with Your Stakeholders.

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.

  • Poor communication: Modern quality management tools pool all your quality control data into one centralized server—increasing visibility, communication, and collaboration across the company. In contrast, companies using manual processes may rely on face-to-face exchanges or a mishmash of communication methods—email, team meetings, and floor notices—to explain changes, expectations, and results. Quality management tools improve communication, even as you transition to digital solutions. Plus, they keep everyone apprised and aligned as quality needs and initiatives evolve.
  • Ineffective documentation: Where are your manual processes stored—in someone’s head? And how do you keep track of process change and revision history? Technology-driven quality management processes are easier to implement, manage, and standardize across your company. Once a process and parameters are set, the system ensures that everyone—at every site—follows them precisely. When it comes to quality control documentation, modern quality management systems store all control metrics, and alert you when collections are missed or incomplete. That way, you know quality management practices are working.
  • Insufficient training: Well-planned projects can fall apart if people aren’t properly trained to implement them. In a manual quality world, workers might deviate from the script after training or implement quality practices differently at different sites. Digital quality management technology simplifies training by creating one system—and one way for quality management to be delivered across every process, product, and plant.

Efficient Data Collection

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.

Manufacturing Productivity

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 & Consistency

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.

Profitability

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.

Reputation & Brand Value

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.

Building Benefits for Quality Teams

And what about those gains? Once change has been implemented, everyone starts to experience the benefits of modern manufacturing technology.

Plant-floor Operators

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 & Quality Professionals

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.

  • Data collection, calculation, and reporting are automated
  • Data and reports are accessible any time, from anywhere, and can be analyzed in real time or historically
  • Because data is complete and consistent, it’s easier to compare quality across processes, equipment, and shifts
  • Data can be retrieved quickly to answer audits or customer requests

Learn how quality managers found opportunities in their quality data.

Corporate & Executive Leaders

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.

  • Executives can act with confidence since data feeding their decisions is complete, accurate, and reliable
  • Leaders can access all quality data in a unified, central repository—gaining a big picture view, and the ability to drill into line-level performance
  • Advanced analytics tools “grade” and prioritize quality issues to help identify quick wins—and biggest risks
  • Advanced reporting tools “slice and dice” data, and connect it to actionable strategies

Learn how a metal-forming manufacturer was able to improve quality and dramatically improve its bottom line.

The Costs of Quality

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:

  • Reduce overfill and waste
  • Increase yield
  • Maximize uptime and machine utilization
  • Improve operator productivity and performance
  • Lower workforce overhead
  • Retain and attract new customers

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:

  • Operators work more efficiently when they’re supported by quality intelligence
  • The right people get the right information—when and where they need it—to understand and resolve problems
  • Data triggers actions on the floor, which eliminates guesswork, biases, and inconsistency
  • Performance issues become visible in dashboards and charts, providing managers with visual representations of problems—and their potential impact
  • Root cause is easier to investigate using standardized and centralized data
  • Opportunities are easier to prioritize with advanced analytics
  • Solutions are easier to replicate and standardize across the business

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.

 

Easy to start. Easy to expand.

nEnact empowers you to quickly realize the benefits of digital data collection and analysis. Start today with:

  • Five Enact licenses: add more as needed
  • Quick Setup wizard: your guide to configuring data collections
  • Video tutorials and easy-to-use help: available in our Guided Learning Center
  • Flexible expansion: reconfigure your licenses, add licenses, integrate with other manufacturing systems, and move to automated data collection—at any time

Implementing Your Quality Management Plan

  • Remember: quality management is an ongoing process—not a one-and-done project. The goal is continuous improvement. Start to think about quality management as a phased process versus a final destination.The SPC-based software you need to support your quality management plan can generally be deployed in three stages: proof of concept, pilot, and rollout.

    Phase 1: Proof of Concept

    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:

    • Start small; use a single process (line or machine).
    • Document the “before” status so you can demonstrate a measurable impact.
    • Prepare master data, such as parts numbers and specification limits. Data may reside in multiple locations, so a proof of concept gives you a jumpstart on centralizing critical quality data.
    • Involve multiple experts, including operators, managers, and engineers. Ask them to enter data, interact with it, and provide feedback.

    Phase 2: Pilot

    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:

    • Pick an expansion path that’s similar to your proof of concept; similar lines or sites will be able to leverage configuration work you’ve already done—and can be spun up faster.
    • Focus on standardization to maximize the advantages of a centralized quality system, sync your metrics, analyses, naming conventions, and work practices.
    • Dig deeper. Now that you have more data feeding into the repository, it’s time to explore advanced features and capabilities.
    • Create documentation to prepare the rest of the organization, such as training materials. You can also start to estimate the resource requirements needed to support a complete rollout (e.g., time and staffing).

    Phase 3: Production Rollout

    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:

    • See the biggest opportunities for process and product quality improvement
    • Prioritize opportunities—and designate the resources needed to address them
    • Develop policies and processes that affect bottom-line performance
    • Use quality improvement to address strategic priorities
    • Empower workers—and create a culture around quality

“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

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  • Explore which solutions best suit your needs
  • No-pressure conversation
  • Get a live, personalized demo

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Elevating the Importance of Quality Control in Manufacturing

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Quality Control Has Always Been Important in Manufacturing. But It Hasn’t Been Influential—Until Now.

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.

When quality control is elevated across the organization, you can optimize every process.

Why Is Quality Control Important in Manufacturing?

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.

Efficient Data Collection

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.

Manufacturing Productivity

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 & Consistency

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.

Profitability

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.

Reputation & Brand Value

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.

The Cloud Makes Meaningful Quality Insights Possible

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.

  • You get total data visibility for all stakeholders—regardless of their location
  • You can access real-time data through any device—PC, tablet, and smartphone
  • You don’t need to worry about managing technology deployments, updates, or upgrades

Support & Extend Your Existing Systems

Integration is a key tenet of the Enact quality platform. This integration takes multiple forms:

  • Supply chain visibility: With Enact, you have unlimited visibility into your supply chain. Simply have suppliers enter their data into Enact’s web-based interface. There’s nothing to install or deploy at the supplier site—all they need is an internet connection and browser.
  • United systems: Because Enact is purpose-built for manufacturing, it’s designed to integrate with all the equipment and systems on your plant floor. Simply connect the Enact platform to your equipment, gauges, and sensors to enable automated data collection.
  • Seamless integration: Enact also enables seamless integration with your existing ERP, MOM, MES, and QMS systems. Not only can you use the data those systems collect, but with a simple click, you can upload quality data to those systems for a more consistent, enterprise-wide view of how quality affects and is affected by your inputs, processes, and outputs.
  • Expanded data value: Easily share quality Key Performance Indicators (KPIs) and lot data from Enact with other plant systems. Enact simplifies this data exchange process, saving you time and effort.

Change How You Apply Quality Control Methods in Manufacturing

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.

During Data Collection

Organizations that do quality control: 

  • Gather quality information, but not in a format that’s easy to access or analyze. Metrics may be stored on paper or only be available onsite.
  • Use different naming conventions for quality inputs, such as ingredient names or measurements. Or they might allow operators to create their own collection protocols, which introduces discrepancies.
  • Check measurements and results only periodically.
  • Manually calculate performance metrics. Human calculations are error prone compared to computers, and different calculation methods may arrive at slightly different results.
  • Address quality issues with home-grown solutions that aren’t replicable or that rely upon a particular person.

Organizations that master quality control:

  • Collect and store all quality data in a standard format—and in a centralized location. Their data is comparable across the business and for a variety of use cases—from the plant floor to the board room.
  • Integrate devices so leaders and operators have a more comprehensive view of quality—and how processes interconnect to support quality.
  • Automate calculations so they are timely and correct.

Acting on Data

Organizations that do quality control: 

  • Are tied to their physical locations. They may require visual inspections or counts to detect quality issues. That creates challenges for supervisors who oversee more than one line or who work in large plants.
  • Are forced to make critical decisions based on incomplete data. Often, these decisions may be limited to go/no-go since data has to be retrieved for further investigation.

Organizations that master quality control:

  • Keep a constant pulse on quality. Data is accessible in real time, from anywhere with a reliable internet connection.
  • Can identify root cause faster because data points are integrated and connected—and data is standardized and up to date.
  • Easily spot ways to improve quality at both the site and the enterprise level.
  • Get alarms triggered by statistics—not by mistake or because of hunches or “gut feelings.”
  • Have automated notifications sent to the right people in the organization—for immediate remediation or investigation.

Analyzing Data

Organizations that do quality control: 

  • Only look at historical performance data when there’s an issue or an external request (e.g., from a customer or auditor).
  • Are overwhelmed by the amount of data they collect. They prioritize data collection, compliance, and storage tasks over analysis because there’s just too much to dig through.
  • Try to use control charts to answer all quality control questions. They spend a lot of time reformatting and manipulating data to meet different users’ needs or customer data requests.
  • Struggle to troubleshoot issues in the moment, or test “what if” scenarios without a lot of manual work or risk.

Organizations that master quality control:

  • Have data sorted and sifted automatically, and pertinent reports are accessible within a few clicks.
  • See quality control data in real time, and can retrieve historical information from the same platform—without juggling spreadsheets.
  • Get reports customized for different use cases in the organization. Users get exactly what they need, when they need it—in a format they can understand and use right away.

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.

Easy to start. Easy to expand.

Enact empowers you to quickly realize the benefits of digital data collection and analysis. Start today with:

  • Five Enact licenses: add more as needed
  • Quick Setup wizard: your guide to configuring data collections
  • Video tutorials and easy-to-use help: available in our Guided Learning Center
  • Flexible expansion: reconfigure your licenses, add licenses, integrate with other manufacturing systems, and move to automated data collection—at any time

What’s the Role of Quality Management in Manufacturing?

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.

Tactical Applications of Quality Control

  • Digital tools speed up data collection and eliminate human error. Timed collections, acceptance sampling, and control charts create efficiencies in the plant.
  • Data becomes more accurate, consistent, and accessible.
  • Operators can use quality data to increase uptime and keep their lines moving.
  • Real-time visibility into operations leads to better troubleshooting and faster issue resolution. Managers can identify root cause faster—and take proactive measures to protect quality.
  • Automated calculations, alerts, and notifications help operators focus on the most important issues on the floor.

Strategic Applications of Quality Control

  • Standardized and configurable reporting helps everyone keep an eye on quality—which helps build a culture of quality. When everyone can see quality, everyone can affect quality.
  • Plant-level wins, such as reducing scrap or increasing yields, can be easily replicated across the organization. When it’s analyzed, centralized quality data leads to large-scale, long-term, enterprise-wide improvements.
  • Quality becomes more consistent across the entire organization, improving customer satisfaction and brand equity.
  • Executives can plan and prioritize quality initiatives, map out investments, and calculate the ROI from quality control efforts.
  • Quality becomes an influential lever toward key strategic goals, such as maximizing resources or boosting ROI.

Getting Started: What To Expect from a Manufacturing Quality Platform

When choosing a quality platform, work with your provider to pin down exactly what you want from the platform:

  • What are your goals for a quality solution? Do you hope to gain better insight across multiple facilities? Are you looking to improve compliance, or gain visibility into your supply chain?
  • What roles will access the platform, and what are their aspirations and concerns? Consider everyone who will use the solution—including floor operators, quality management, and executives.
  • How will you measure the effectiveness of your solution? Which KPIs matter most to you?
  • How will you measure ROI?

Set Up for Success

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.

How To Start Using Your Quality Platform: Stages for Deployment & Rollout

  • Stage 1: Proof of Concept: A focused deployment (on a single line or process) serves as the foundation for your Enact rollout—a blueprint for all other operations in your organization.
  • Stage 2: Expand Your Impact: Replicate your blueprint to other lines or across a facility. This provides an opportunity to expand Enact to other products or parts and incorporate additional functionality—such as gauges and Automated Data Collection (ADC).
  • Stage 3: Production Rollout: Apply your blueprint to other parts of the facility, lines, processes—and across other facilities in your organization.

Speak to a Distribution Industry Expert

What to Expect

  • Free 20-minute call with a product expert
  • Explore which solutions best suit your needs
  • No-pressure conversation
  • Get a live, personalized demo

Take the first step from quality to excellence

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How To Choose a Manufacturing Quality Platform

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Definition: What Is a Quality Platform?

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:

  • Proactively and intelligently reduce cost, scrap, and risk
  • Optimize profit and productivity
  • Streamline audits and analyses
  • Direct quality improvement resources where they’re needed most

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.

A quality platform brings the benefits of SPC and quality improvement to teams across your enterprise.

How Can My Team Align To Choose the Best Cloud-based SPC Software?

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.

How Is a Quality Platform Different from Your Existing Systems?

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.

  • Manufacturing Execution System (MES): This digital system connects and monitors your manufacturing equipment, providing real-time data across the product lifecycle. From your MES, you collect and analyze data about multiple inputs to create a record that captures the processes and outcomes involved in your plants. The data that your MES captures can be used in a variety of aims, including operations management, product tracking, performance analysis, resource allocation, and quality management. This system’s goal is to optimize your operations.
  • Enterprise Resource Planning (ERP): This business management software provides a real-time, integrated view of your core business processes. Use your ERP to track raw materials, production capacity, and orders, among other resources. Functional areas vary widely and include financial and management accounting, HR, manufacturing, order processing, and supply-chain management. This system’s goal is to optimize resource utilization across departments and divisions.
  • Manufacturing Operations Management (MOM): These systems are designed to manage a variety of manufacturing processes, including interactions between people and systems, IT security services, asset and production model tracking, and data aggregation. The goal of a MOM system is to optimize efficiency across business divisions.
  • Quality Management System (QMS): This system documents the processes and procedures for monitoring and maintaining your quality objectives. Although many organizations collect quality data manually, the most effective and efficient systems are software-based—and can partially or fully automate data collection. The goal of a QMS is to optimize quality management across the divisions of the broader enterprise.

How Does a Quality Platform Fit into Your Manufacturing Ecosystem?

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?

  • Data standardization is vital if you hope to get a true and accurate analysis of quality—a single version of truth, as it were. From naming conventions to measurement units to methodologies, you can’t compare apples to oranges and expect to get accurate, actionable insights.
  • A centralized repository is essential when standardizing data for Statistical Process Control (SPC) analysis and business analytics. You can be certain that data is collected, labeled, and stored consistently—regardless of its source.
  • The best option for data centralization is a relational database, which enables you to use and leverage data in multiple ways.

The Cloud Makes Meaningful Quality Insights Possible

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.

  • You get total data visibility for all stakeholders—regardless of their location
  • You can access real-time data through any device—PC, tablet, and smartphone
  • You don’t need to worry about managing technology deployments, updates, or upgrades

Support & Extend Your Existing Systems

Integration is a key tenet of the Enact quality platform. This integration takes multiple forms:

  • Supply chain visibility: With Enact, you have unlimited visibility into your supply chain. Simply have suppliers enter their data into Enact’s web-based interface. There’s nothing to install or deploy at the supplier site—all they need is an internet connection and browser.
  • United systems: Because Enact is purpose-built for manufacturing, it’s designed to integrate with all the equipment and systems on your plant floor. Simply connect the Enact platform to your equipment, gauges, and sensors to enable automated data collection.
  • Seamless integration: Enact also enables seamless integration with your existing ERP, MOM, MES, and QMS systems. Not only can you use the data those systems collect, but with a simple click, you can upload quality data to those systems for a more consistent, enterprise-wide view of how quality affects and is affected by your inputs, processes, and outputs.
  • Expanded data value: Easily share quality Key Performance Indicators (KPIs) and lot data from Enact with other plant systems. Enact simplifies this data exchange process, saving you time and effort.

Quality Insights Empower Manufacturing Optimization

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:

  • Your operators need timely notifications to help them standardize data collection and spot process deviations as quickly as possible.
  • Your quality managers need the ability to verify that products across each line in a facility meet quality standards.
  • Plant managers need insight into workflow efficiency and productivity.
  • Executives need an enterprise-wide view of profitability and proficiency—across multiple locations and regions.

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.

  • Data collection: Whether you use manual, automated, or semi-automated collection techniques is up to you. A robust quality platform will support automated capture (from calipers, scales, and so on) and simplify manual operator entry (through touchscreens and barcode scanners, for example).
  • Operator empowerment: With so much happening on the manufacturing floor, operators need all the help they can get focusing on specific areas and real-time events. Look for a quality platform that provides a way to filter data according to relevance—while enabling timed alerts and notifications. A dashboard that is customizable according to role or priority, is an invaluable tool.
  • Analytics: The key to actionable insights is in-depth analytics. Many manufacturers find value in SPC and KPIs. With your centralized data powering analysis of both real-time events and historical trends, you can easily surface the most valuable information—then use it to make smart, strategic decisions.
  • Transformation: Now that you have a focused, prioritized view of the data that matters to you—and the ability to analyze and synthesize it in a variety of ways—you are set to turn quality intelligence into business intelligence. What are the big-picture questions across your enterprise? Perhaps you want to know which facilities are running most efficiently—and how to duplicate that success at other locations. Or maybe you need to decide where to dedicate resources to see the greatest or fastest ROI.

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.

Easy to start. Easy to expand.

Enact empowers you to quickly realize the benefits of digital data collection and analysis. Start today with:

  • Five Enact licenses: add more as needed
  • Quick Setup wizard: your guide to configuring data collections
  • Video tutorials and easy-to-use help: available in our Guided Learning Center
  • Flexible expansion: reconfigure your licenses, add licenses, integrate with other manufacturing systems, and move to automated data collection—at any time

Getting Started: What To Expect from a Manufacturing Quality Platform

When choosing a quality platform, work with your provider to pin down exactly what you want from the platform:

  • What are your goals for a quality solution? Do you hope to gain better insight across multiple facilities? Are you looking to improve compliance, or gain visibility into your supply chain?
  • What roles will access the platform, and what are their aspirations and concerns? Consider everyone who will use the solution—including floor operators, quality management, and executives.
  • How will you measure the effectiveness of your solution? Which KPIs matter most to you?
  • How will you measure ROI?

Set Up for Success

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.

How To Start Using Your Quality Platform: Stages for Deployment & Rollout

  • Stage 1: Proof of Concept: A focused deployment (on a single line or process) serves as the foundation for your Enact rollout—a blueprint for all other operations in your organization.
  • Stage 2: Expand Your Impact: Replicate your blueprint to other lines or across a facility. This provides an opportunity to expand Enact to other products or parts and incorporate additional functionality—such as gauges and Automated Data Collection (ADC).
  • Stage 3: Production Rollout: Apply your blueprint to other parts of the facility, lines, processes—and across other facilities in your organization.

Speak to a Distribution Industry Expert

What to Expect

  • Free 20-minute call with a product expert
  • Explore which solutions best suit your needs
  • No-pressure conversation
  • Get a live, personalized demo

Take the first step from quality to excellence

Contact Us