Elevating the Importance of Quality Control in Manufacturing

Go from “doing quality” to mastering it—and get more value for your quality management investment.

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?

Elevate quality control 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.

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. 

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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.
Tactical Applications of Quality Control

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. 

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