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April 13, 2026

Quality management is not a matter of faith

Kirjoittaja:

Ella Lindroos

Quality management is not a matter of faith — even though it’s easy to frame it that way. Some believe in the principles of quality management, others don’t.

TQM — Total Quality Management — has always been grounded in knowledge and research.

Quality management is most certainly not just a belief system. The literature in the field is extensive, and there is no shortage of individuals who have earned the title of guru.

Quality management is based on knowledge and research

W. Edwards Deming is undisputedly the founding father of the field. His 14 management principles and his strong influence on the PDCA cycle (Plan–Do–Check–Act) as a management tool still form the backbone of many quality management systems. Key work: Out of the Crisis (1982).

Joseph M. Juran brought a strategic perspective: quality is management’s responsibility, not just the technical department’s. He developed the Juran Trilogy (Quality Planning, Quality Control, Quality Improvement). Key work: Juran’s Quality Handbook.

Philip Crosby popularised the idea that “quality is free” — preventing defects costs less than correcting them. Key work: Quality Is Free (1979).

Kaoru Ishikawa developed the cause-and-effect diagram (the Ishikawa or fishbone diagram) and introduced the concept of quality circles in Japan. Key work: What Is Total Quality Control? The Japanese Way (1985).

Genichi Taguchi introduced statistical methods to product design and robustness — quality is designed in, not inspected in afterwards. Key work: Introduction to Quality Engineering (1986).

James Womack & Daniel Jones are not traditional quality gurus, but their Lean thinking (Lean Thinking, 1996) is now an inseparable part of practical quality management.

Kaplan & Norton are also not traditional quality gurus, but the Balanced Scorecard remains one of the most widely used measurement frameworks in the world. It supports quality thinking and management well, because it helps connect quality to strategic objectives through a structured set of measures. Key work: The Balanced Scorecard: Translating Strategy into Action (1996).

A focus on methodology and evidence-based decision-making

Alongside traditional TQM, a wide range of methods has emerged to translate the goal of a better future into practical action.

Central to these methods has been understanding the needs of stakeholders and creating value for them. In the core principles of quality management, quality is never solely about the quality of a product or service — it always encompasses the quality of operations and leadership as well.

Evidence-based decision-making has been a fundamental principle of quality management from the very beginning. Data-driven leadership is not an invention of this century.

What is an invention of this century, however, is the improvement in the volume and quality of available data.

Organisations can now be led according to quality management principles more effectively than ever before, partly because both predictive and descriptive data — about current state and future direction — is more readily available than it has ever been.

No activity should be irresponsible from any stakeholder’s perspective

The principles of sustainable development underpin many quality management frameworks, including ISO standards and the various versions of the EFQM model.

These frameworks are often broad in scope because they start from the fundamental assumption that quality must cover all operations — and that no activity should be irresponsible from the perspective of any key stakeholder.

Proactivity, ensuring continuity, and risk-based thinking are central perspectives in quality management — and a leadership team that genuinely embraces quality management must be able to demonstrate these in everyday practice.

In everyday practice, quality management means continuous improvement

In everyday practice, quality management is continuous improvement. Breaking it down into its components reveals it in a new light.

What does “everyday level” mean for me, for our leadership team, for the team, or for an individual team member?

Are we talking about the same cycle? Do our plans and execution align naturally with the calendar, or is everyday life unpredictable chaos?

Do we share the same unit of measure and cadence?

Has quality leadership been defined — or has leadership ever been evaluated at all?

Are we aware of the maturity level of our management system, and how it could be developed to the next quality level?

A common pitfall in quality management is how the PDCA model is applied

Continuous improvement is probably one of the most misused or misunderstood phrases in the field. Maintaining genuine PDCA (Plan–Do–Check–Act) requires structures and effort to work well both in terms of timing and content.

A frequently recurring mistake is making corrective moves too quickly, so that a planned change is never properly standardised or tested long enough to generate meaningful learning and improvement from the data and experience gathered.

Another common misconception about continuous improvement is that organisations drift into cycles that are far too long.

When these two issues combine, the result is an everyday reality where leadership may be making rapid changes continuously, staff and customers cannot absorb them fast enough, the annual or strategy-period PDCA is fragmented, and decisions based on collected data feel uncertain.

It is like a clockwork mechanism where only some of the gears are properly aligned.

Quality management today is a mindset that permeates your entire organisation

Total Quality Management (TQM) is not a production line inspection process — it is a mindset that permeates the entire organisation.

Today, it means continuously delivering customer value in a data-driven way, with the involvement of people and the systematic improvement of processes.

It also encompasses digitalisation, agile methods, risk-based thinking, and responsibility for the future.

8 frameworks that support quality management

Lean focuses on eliminating waste and maximising value flow. It is a natural extension of Deming’s principles: standardise processes → improve flow → increase customer value.

Six Sigma uses statistical methods to reduce variation. The DMAIC cycle (Define, Measure, Analyse, Improve, Control) is a particularly popular problem-solving model in both manufacturing and service industries.

Lean Six Sigma combines both — and is currently one of the most widely used practical frameworks.

Service Design approaches quality through the lens of the customer experience. It does not ask “is our process efficient?” but rather “how does this feel from the customer’s perspective?” Together, Lean, Six Sigma and Service Design form a powerful combination: efficient, consistent, and experienced as good.

Agile and DevOps (Scrum, Kanban, SAFe) bring continuous improvement to software development and IT services. Retrospectives, rapid iterations and continuous integration are, at their core, PDCA in a modern form.

The OKR model (Objectives & Key Results) supports transparency and measurability of goals, which strengthens ongoing quality monitoring across the entire organisation.

ISO 9001:2015 is the world’s most widely used quality management standard. The 2015 version placed greater emphasis on risk-based thinking, understanding context, and leadership commitment — not just documentation. The new version, ISO 9001:2026, will be published in September 2026 and, in addition to the above, places further emphasis on opportunities identified through risk, as well as the implications of climate change.

The EFQM Excellence Model is a European framework for holistic management that evaluates organisational performance across leadership, strategy and people through to customer outcomes and societal impact. The RADAR logic serves as its assessment tool — and its structure echoes that of PDCA. The updated version places greater emphasis on sustainability, innovation and technology.

What value does quality management deliver to your organisation?

Here are four benefits that quality management delivers to organisations of all sizes:

Financial benefits — reduction in the cost of errors, fewer complaints, improved process efficiency, and greater staff productivity. Philip Crosby’s classic argument still holds: prevention is cheaper than correction.

Customer and reputational benefits — increased customer satisfaction and loyalty, stronger competitive advantage, and brand credibility. ISO certification also opens doors to many international procurement processes.

Organisational benefits — better data-driven decision-making, clearer accountability, employee engagement, commitment and wellbeing, and easier change management when processes are documented and measured.

Sustainability benefits — clear processes and metrics support the monitoring and reporting of ESG objectives (environment, social responsibility, governance).

Will artificial intelligence change the principles of quality management?

The core principles of quality management — customer focus, continuous improvement, evidence-based decision-making and process management — I do not believe are losing their relevance in the age of artificial intelligence.

On the contrary: AI makes these principles more practical to implement than ever before. I would even venture to suggest that Deming himself would have been rather excited about AI.

In Deming’s time, “evidence-based decision-making” meant statistical sampling and hand-drawn control charts.

Today, machine learning analyses thousands of process variables in real time, detects deviations before they grow into defects, and predicts customer dissatisfaction before the customer themselves can articulate it.

Only the tool has changed. The principle remains the same.

Quality is always made by people

From a continuous improvement perspective, AI may be the most significant development since Lean.

Retrospectives and audits have traditionally been retrospective — AI-driven quality management shifts the focus to prevention.

In Scrum, this might mean, for example, that a team receives a data-driven analysis of its bottlenecks to support the retrospective, rather than relying solely on memory and gut feeling.

From a staff engagement perspective, AI frees people from routine measurement tasks for creative problem-solving — which has always been the soul of quality work.

Quality is made by people.

The difference between quality management and quality control

Below is a table outlining the typical differences between quality management and quality control.

Quality management is about principles and setting direction; quality control is about the structures according to which your organisation operates. Both are needed — intent without structure is empty words, and structure without intent gathers dust on a shelf.