How To Use Quality Metrics To Improve Quality Management in Manufacturing

A manufacturing quality platform makes it easier for manufacturers to apply quality control data.

Trusted by hundreds of manufacturing companies around the world:

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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