3 Definitions of Quality in Manufacturing and Why They Matter

“Quality” is one of the most important concepts in manufacturing. But that doesn’t mean there’s agreement on what quality is. Ask 100 manufacturers and you’ll get 100 different definitions.

Quality standards like the ISO family, IATF 16949, and GxP are essential for producing quality products. There are few better tools for controlling costs, streamlining compliance, and guaranteeing safe, performant products.

They aren’t much help, however, where general definitions are concerned.

Even tried and true definitions (“Fitness for Use,” “Conformance to Requirements,” “When a thing does what it’s supposed to do”) can be maddeningly vague.

New technologies have spurred a resurgence of interest in quality in manufacturing–enough to earn the name Quality 4.0. Given this return to quality, now is a good time to revisit some of the canonical definitions of quality. They still have a lot to teach us about quality initiatives in the present, and where they might go in the future.

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In short, definitions of quality are vague because manufactured products are too varied to fit under a single, catchall definition.

In order to hold water as a concept, “quality” has to account for tremendous differences in manufactured products. Features of quality for, say, cutting-edge biologics, will differ from those for automobile parts. Quality standards for PCBs aren’t necessarily relevant for a food and beverage manufacturer.

Quality experts have long acknowledged this need for broad applicability in their definitions of quality. The experts at ASQ thus describe quality as “a subjective term for which each person or sector has its own definition.”

Still, there are commonalities that unite definitions across industry and product.

The ASQ argues that all quality products must meet two criteria:

1.) Quality products satisfy their stated or implied needs.

2.) Quality products are free of deficiencies.

Let’s unpack these points.

First, quality products “satisfy their stated or implied needs,” meaning they do what they say they’ll do. Second, quality products are free of deficiencies, meaning there’s no flaw in the design or production of the good that would prevent it from doing what it should do.

The beauty of this definition is that it applies to all manufactured products, from the most advanced pharmaceuticals to car parts. It applies equally well to industries where any new product requires months of regulatory approval and testing and industries where new products can roll off the lines at a moment’s notice.

In short, this definition establishes features of quality (goals, validation, and iterative improvement) that inform how manufacturers set and attain their standards.

Other definitions of quality tend to be similarly broad. Here are three of the most important.

Joseph M. Juran, a foundational thinker in manufacturing and quality management, offered this definition in the mid-1950s. It has been relevant since. For Juran, quality is achieved when a finished product is suitable for use by its intended audience. Like quality, “fitness” itself can be vague. Nevertheless, Juran outlined five factors that, in balance, determine whether or not a product is fit for use:

1.) Who will use the product

2.) How they will use it

3.) The possibility and probability of danger caused by the product

4.) The economic resources of the user and producer

5.) How the user perceives quality in different products

This is an elegant definition of quality because it captures how much quality is a careful negotiation of objects, people, and perceptions. Here, quality is always relative to a consumer—their needs, resources, and safety. In Juran’s definition, the quality of something depends on how someone will use it.

Philip Crosby, an influential contributor to management and quality theory, took a slightly different angle on quality. He defined “quality” not in terms of fitness, but in terms of requirements: those established for the product, and those of the consumer.

For Crosby, companies determine the requirements for a product based on their target consumer. When they design a product, manufacturers will establish its technical specs. These serve as a guideline for “requirements” on the product side.

At the same time, manufacturers do their best to scope the requirements of the consumer. A product that conforms to technical specifications but fails to fulfill the consumer’s need isn’t conforming to requirements. As a best practice, manufacturers will try to understand and eliminate the source of nonconformances whenever they arise.

3.) More than Making a Good Product

A much-cited essay in the Harvard Business Review argued that “Quality is more than making a good product.”

By this, the authors suggest that quality must be defined by customer needs in product design (what are the products and services they want?), as well as by how well the product satisfies those needs.

For these management scientists, quality isn’t something achieved on the shop floor. Rather, quality is the coordination of an entire company across a product’s lifecycle.

Starting to see a pattern? Across all accepted definitions, quality is relative to a consumer, a product, and an outcome.

Why Definitions of Quality Matter Now

The question remains: why now? Why dig up definitions of quality over half a century old?

The answer is: they’ve never been more relevant.

Quality 4.0 is often discussed in terms of technologies like AI, big data, IoT, computer vision. In reality, it’s much more than that.

Quality 4.0, at its core, names a shift in all aspects of quality–from culture to benchmarking to production to compliance–in the digital era. Still, quality returns to designing and producing goods that work for the end user.

But what does this look like in practice?

For one, new technologies have raised the ceiling in terms of repeatability, efficiency, and consistency in quality. Now, more than ever, manufacturers can leverage a dizzying range of tools throughout their QMS.

As new levels of quality become possible, understanding precisely what is meant by quality can help inform future efforts.

Second, the shift toward agile manufacturing has brought new attention to end-to-end product development. As all of these definitions of quality argue, quality starts with product design and continues through use by the consumer.

It should be no surprise that the ASQ suggests manufacturers add agile methodology to their lean programs. The overlaps between quality management and agile are many.

With the tools of Quality 4.0 at their disposal, manufacturers have an opportunity to serve their customers better than ever before.

And quality, at heart, means serving the consumer.

Back in 1987, Harvard’s David Garvin described eight ways people judge quality. It’s still one of the most practical frameworks in manufacturing.
Quality isn’t one number, it’s how the product performs, lasts, looks, and even how people feel about using it.

Not every dimension fits every product. A medical device team might care most about conformance and reliability; a consumer brand might focus on aesthetics and perception. The point is to see quality from more than one angle, because that’s how customers see it.

Quality isn’t just about finding defects. It’s about building processes that keep them from happening in the first place. That’s where Quality Control (QC) and Quality Assurance (QA) split paths.
They sound similar, but they cover different ground in a quality system.

QC makes sure bad parts don’t reach the customer. QA builds the conditions so bad parts never happen. Both matter, and both rely on feedback from each other to keep improving the system.

Quality isn’t an idea on a poster. It’s something you can measure.
On the floor, teams track a few key numbers that show how stable the process is, where problems start, and how customers experience the result.

1. First Pass Yield (FPY)
How often a product passes inspection the first time with no rework.
Example: Out of 1,000 units, 960 pass on the first go, FPY = 96%.
A healthy FPY points to a stable process. When it drops, the cause is usually variation, unclear work steps, or equipment drift.

2. Defect Rate
The share of parts that don’t meet spec out of total produced.
Example: 20 defective parts out of 2,000 equals a 1 % defect rate.
It’s a direct read on product quality and an easy way to compare performance between lines or shifts.

3. Customer Complaints
How often customers report quality issues.
Example: Three complaints for every 10,000 units shipped.
Even if in-process checks look good, complaint data shows how well the product holds up in use.

4. Cost of Poor Quality (CoPQ)
Money lost to scrap, rework, returns, and warranty work.
Example: A labeling mistake that triggers a recall could cost $25,000 in parts and labor.
CoPQ translates quality issues into financial impact and helps justify process improvements.

5. Audit Findings / Non-Conformances
Problems found during internal or external audits.
Example: A missing calibration record for a torque wrench spotted during a supplier audit.
These highlight system or documentation gaps that affect long-term control.

When these KPIs are defined and measured consistently, teams get a clear baseline for improvement instead of opinions.
Digital tools can pull data directly from the work being done with measurements, inspections, photos, operator entries, so issues show up in real time, not after the batch is finished.

Old quality systems were built for paperwork and after-the-fact reviews.
Modern plants need tools that move at the same speed as production.

With Tulip, quality sits inside the work itself, not off to the side. Operators record, check, and verify as they go, and the data ties straight into the process.

No-Code Apps That Build Quality Into the Job

Static work instructions and memory don’t cut it anymore.
Teams can build their own digital workflows with Tulip’s no-code tools that:

• Walk operators through each step

• Check inputs like torque, count, or dimensions before allowing the next task

• Block common mistakes using simple logic rules

This keeps execution steady, cuts rework, and helps new hires get up to speed without long shadowing periods.

Traceability Without Chasing Paper

Every part and action leaves a trail automatically. Tulip apps:

• Link data to the right operator, machine, and batch

• Capture timestamps, photos, and electronic signatures

• Store a searchable audit trail ready when QA or regulators ask for it

You spend less time hunting through binders and more time fixing what matters.

Built-In Feedback Loops

Quality improves when teams see problems early.
Live dashboards flag trends, defect spikes, or slipping metrics like FPY or cycle time.
Alerts go out automatically, and engineers can update workflows on the fly i.e. no code, no IT queue. Issues get addressed while the shift is still running, not weeks later in a review meeting.

This is what connected quality looks like: data gathered as work happens, traceability built in, and teams able to adjust in real time.

Paper records and scattered spreadsheets used to dominate both areas. Data got lost, checks were skipped, and it was hard to trace what actually happened on the line.
Digital tools change that by connecting quality work directly to the process.

With Tulip, teams can:

• Build QA workflows with step-by-step digital guides for operators.

• Capture QC data like measurements, photos, checks, in real time at the station.

• Link every record to the right batch, operator, and machine for full traceability.

• Audit instantly with version-controlled digital logs.

When data flows automatically, quality moves from reaction to prevention. Problems show up faster, fixes stick longer, and you’ve got proof built into the work itself.