Sidney Dekker: The Safety Scientist Who Influences How I Think About Quality

Over the past decades, as I’ve grown and now led quality organizations in biotechnology, I’ve encountered many thinkers who’ve shaped my approach to investigation and risk management. But few have fundamentally altered my perspective like Sidney Dekker. His work didn’t just add to my toolkit—it forced me to question some of my most basic assumptions about human error, system failure, and what it means to create genuinely effective quality systems.

Dekker’s challenge to move beyond “safety theater” toward authentic learning resonates deeply with my own frustrations about quality systems that look impressive on paper but fail when tested by real-world complexity.

Why Dekker Matters for Quality Leaders

Professor Sidney Dekker brings a unique combination of academic rigor and operational experience to safety science. As both a commercial airline pilot and the Director of the Safety Science Innovation Lab at Griffith University, he understands the gap between how work is supposed to happen and how it actually gets done. This dual perspective—practitioner and scholar—gives his critiques of traditional safety approaches unusual credibility.

But what initially drew me to Dekker’s work wasn’t his credentials. It was his ability to articulate something I’d been experiencing but couldn’t quite name: the growing disconnect between our increasingly sophisticated compliance systems and our actual ability to prevent quality problems. His concept of “drift into failure” provided a framework for understanding why organizations with excellent procedures and well-trained personnel still experience systemic breakdowns.

The “New View” Revolution

Dekker’s most fundamental contribution is what he calls the “new view” of human error—a complete reframing of how we understand system failures. Having spent years investigating deviations and CAPAs, I can attest to how transformative this shift in perspective can be.

The Traditional Approach I Used to Take:

Human error causes problems
People are unreliable; systems need protection from human variability
Solutions focus on better training, clearer procedures, more controls

Dekker’s New View That Changed My Practice:

Human error is a symptom of deeper systemic issues
People are the primary source of system reliability, not the threat to it
Variability and adaptation are what make complex systems work

This isn’t just academic theory—it has practical implications for every investigation I lead. When I encounter “operator error” in a deviation investigation, Dekker’s framework pushes me to ask different questions: What made this action reasonable to the operator at the time? What system conditions shaped their decision-making? How did our procedures and training actually perform under real-world conditions?

This shift aligns perfectly with the causal reasoning approaches I’ve been developing on this blog. Instead of stopping at “failure to follow procedure,” we dig into the specific mechanisms that drove the event—exactly what Dekker’s view demands.

Drift Into Failure: Why Good Organizations Go Bad

Perhaps Dekker’s most powerful concept for quality leaders is “drift into failure”—the idea that organizations gradually migrate toward disaster through seemingly rational local decisions. This isn’t sudden catastrophic failure; it’s incremental erosion of safety margins through competitive pressure, resource constraints, and normalized deviance.

I’ve seen this pattern repeatedly. For example, a cleaning validation program starts with robust protocols, but over time, small shortcuts accumulate: sampling points that are “difficult to access” get moved, hold times get shortened when production pressure increases, acceptance criteria get “clarified” in ways that gradually expand limits.

Each individual decision seems reasonable in isolation. But collectively, they represent drift—a gradual migration away from the original safety margins toward conditions that enable failure. The contamination events and data integrity issues that plague our industry often represent the endpoint of these drift processes, not sudden breakdowns in otherwise reliable systems.

Beyond Root Cause: Understanding Contributing Conditions

Traditional root cause analysis seeks the single factor that “caused” an event, but complex system failures emerge from multiple interacting conditions. The take-the-best heuristic I’ve been exploring on this blog—focusing on the most causally powerful factor—builds directly on Dekker’s insight that we need to understand mechanisms, not hunt for someone to blame.

When I investigate a failure now, I’m not looking for THE root cause. I’m trying to understand how various factors combined to create conditions for failure. What pressures were operators experiencing? How did procedures perform under actual conditions? What information was available to decision-makers? What made their actions reasonable given their understanding of the situation?

This approach generates investigations that actually help prevent recurrence rather than just satisfying regulatory expectations for “complete” investigations.

Just Culture: Moving Beyond Blame

Dekker’s evolution of just culture thinking has been particularly influential in my leadership approach. His latest work moves beyond simple “blame-free” environments toward restorative justice principles—asking not “who broke the rule” but “who was hurt and how can we address underlying needs.”

This shift has practical implications for how I handle deviations and quality events. Instead of focusing on disciplinary action, I’m asking: What systemic conditions contributed to this outcome? What support do people need to succeed? How can we address the underlying vulnerabilities this event revealed?

This doesn’t mean eliminating accountability—it means creating accountability systems that actually improve performance rather than just satisfying our need to assign blame.

Safety Theater: The Problem with Compliance Performance

Dekker’s most recent work on “safety theater” hits particularly close to home in our regulated environment. He defines safety theater as the performance of compliance when under surveillance that retreats to actual work practices when supervision disappears.

I’ve watched organizations prepare for inspections by creating impressive documentation packages that bear little resemblance to how work actually gets done. Procedures get rewritten to sound more rigorous, training records get updated, and everyone rehearses the “right” answers for auditors. But once the inspection ends, work reverts to the adaptive practices that actually make operations function.

This theater emerges from our desire for perfect, controllable systems, but it paradoxically undermines genuine safety by creating inauthenticity. People learn to perform compliance rather than create genuine safety and quality outcomes.

The falsifiable quality systems I’ve been advocating on this blog represent one response to this problem—creating systems that can be tested and potentially proven wrong rather than just demonstrated as compliant.

Six Practical Takeaways for Quality Leaders

After years of applying Dekker’s insights in biotechnology manufacturing, here are the six most practical lessons for quality professionals:

1. Treat “Human Error” as the Beginning of Investigation, Not the End

When investigations conclude with “human error,” they’ve barely started. This should prompt deeper questions: Why did this action make sense? What system conditions shaped this decision? What can we learn about how our procedures and training actually perform under pressure?

2. Understand Work-as-Done, Not Just Work-as-Imagined

There’s always a gap between procedures (work-as-imagined) and actual practice (work-as-done). Understanding this gap and why it exists is more valuable than trying to force compliance with unrealistic procedures. Some of the most important quality improvements I’ve implemented came from understanding how operators actually solve problems under real conditions.

3. Measure Positive Capacities, Not Just Negative Events

Traditional quality metrics focus on what didn’t happen—no deviations, no complaints, no failures. I’ve started developing metrics around investigation quality, learning effectiveness, and adaptive capacity rather than just counting problems. How quickly do we identify and respond to emerging issues? How effectively do we share learning across sites? How well do our people handle unexpected situations?

4. Create Psychological Safety for Learning

Fear and punishment shut down the flow of safety-critical information. Organizations that want to learn from failures must create conditions where people can report problems, admit mistakes, and share concerns without fear of retribution. This is particularly challenging in our regulated environment, but it’s essential for moving beyond compliance theater toward genuine learning.

5. Focus on Contributing Conditions, Not Root Causes

Complex failures emerge from multiple interacting factors, not single root causes. The take-the-best approach I’ve been developing helps identify the most causally powerful factor while avoiding the trap of seeking THE cause. Understanding mechanisms is more valuable than finding someone to blame.

6. Embrace Adaptive Capacity Instead of Fighting Variability

People’s ability to adapt and respond to unexpected conditions is what makes complex systems work, not a threat to be controlled. Rather than trying to eliminate human variability through ever-more-prescriptive procedures, we should understand how that variability creates resilience and design systems that support rather than constrain adaptive problem-solving.

Connection to Investigation Excellence

Dekker’s work provides the theoretical foundation for many approaches I’ve been exploring on this blog. His emphasis on testable hypotheses rather than compliance theater directly supports falsifiable quality systems. His new view framework underlies the causal reasoning methods I’ve been developing. His focus on understanding normal work, not just failures, informs my approach to risk management.

Most importantly, his insistence on moving beyond negative reasoning (“what didn’t happen”) to positive causal statements (“what actually happened and why”) has transformed how I approach investigations. Instead of documenting failures to follow procedures, we’re understanding the specific mechanisms that drove events—and that makes all the difference in preventing recurrence.

Essential Reading for Quality Leaders

If you’re leading quality organizations in today’s complex regulatory environment, these Dekker works are essential:

Start Here:

The Field Guide to Understanding Human Error (3rd Edition) – Foundation for the new view approach
Just Culture: Restorative Justice and Accountability (4th Edition) – Practical approach to accountability without blame

For Investigation Excellence:

Behind Human Error (with Woods, Cook, et al.) – Comprehensive framework for moving beyond blame
Drift into Failure – Understanding how good organizations gradually deteriorate

For Current Challenges:

Safety Theater – Critique of compliance-focused approaches particularly relevant to regulated industries
Patient Safety: A Human Factors Approach – Healthcare applications that translate well to pharmaceutical quality

The Leadership Challenge

Dekker’s work challenges us as quality leaders to move beyond the comfortable certainty of compliance-focused approaches toward the more demanding work of creating genuine learning systems. This requires admitting that our procedures and training might not work as intended. It means supporting people when they make mistakes rather than just punishing them. It demands that we measure our success by how well we learn and adapt, not just how well we document compliance.

This isn’t easy work. It requires the kind of organizational humility that Amy Edmondson and other leadership researchers emphasize—the willingness to be proven wrong in service of getting better. But in my experience, organizations that embrace this challenge develop more robust quality systems and, ultimately, better outcomes for patients.

The question isn’t whether Sidney Dekker is right about everything—it’s whether we’re willing to test his ideas and learn from the results. That’s exactly the kind of falsifiable approach that both his work and effective quality systems demand.

Beyond Malfunction Mindset: Normal Work, Adaptive Quality, and the Future of Pharmaceutical Problem-Solving

Beyond the Shadow of Failure

Problem-solving is too often shaped by the assumption that the system is perfectly understood and fully specified. If something goes wrong—a deviation, a batch out-of-spec, or a contamination event—our approach is to dissect what “failed” and fix that flaw, believing this will restore order. This way of thinking, which I call the malfunction mindset, is as ingrained as it is incomplete. It assumes that successful outcomes are the default, that work always happens as written in SOPs, and that only failure deserves our scrutiny.

But here’s the paradox: most of the time, our highly complex manufacturing environments actually succeed—often under imperfect, shifting, and not fully understood conditions. If we only study what failed, and never question how our systems achieve their many daily successes, we miss the real nature of pharmaceutical quality: it is not the absence of failure, but the presence of robust, adaptive work. Taking this broader, more nuanced perspective is not just an academic exercise—it’s essential for building resilient operations that truly protect patients, products, and our organizations.

Drawing from my thinking through zemblanity (the predictable but often overlooked negative outcomes of well-intentioned quality fixes), the effectiveness paradox (why “nothing bad happened” isn’t proof your quality system works), and the persistent gap between work-as-imagined and work-as-done, this post explores why the malfunction mindset persists, how it distorts investigations, and what future-ready quality management should look like.

The Allure—and Limits—of the Failure Model

Why do we reflexively look for broken parts and single points of failure? It is, as Sidney Dekker has argued, both comforting and defensible. When something goes wrong, you can always point to a failed sensor, a missed checklist, or an operator error. This approach—introducing another level of documentation, another check, another layer of review—offers a sense of closure and regulatory safety. After all, as long as you can demonstrate that you “fixed” something tangible, you’ve fulfilled investigational due diligence.

Yet this fails to account for how quality is actually produced—or lost—in the real world. The malfunction model treats systems like complicated machines: fix the broken gear, oil the creaky hinge, and the machine runs smoothly again. But, as Dekker reminds us in Drift Into Failure, such linear thinking ignores the drift, adaptation, and emergent complexity that characterize real manufacturing environments. The truth is, in complex adaptive systems like pharmaceutical manufacturing, it often takes more than one “error” for failure to manifest. The system absorbs small deviations continuously, adapting and flexing until, sometimes, a boundary is crossed and a problem surfaces.

W. Edwards Deming’s wisdom rings truer than ever: “Most problems result from the system itself, not from individual faults.” A sustainable approach to quality is one that designs for success—and that means understanding the system-wide properties enabling robust performance, not just eliminating isolated malfunctions.

Procedural Fundamentalism: The Work-as-Imagined Trap

One of the least examined, yet most impactful, contributors to the malfunction mindset is procedural fundamentalism—the belief that the written procedure is both a complete specification and an accurate description of work. This feels rigorous and provides compliance comfort, but it is a profound misreading of how work actually happens in pharmaceutical manufacturing.

Work-as-imagined, as elucidated by Erik Hollnagel and others, represents an abstraction: it is how distant architects of SOPs visualize the “correct” execution of a process. Yet, real-world conditions—resource shortages, unexpected interruptions, mismatched raw materials, shifting priorities—force adaptation. Operators, supervisors, and Quality professionals do not simply “follow the recipe”: they interpret, improvise, and—crucially—adjust on the fly.

When we treat procedures as authoritative descriptions of reality, we create the proxy problem: our investigations compare real operations against an imagined baseline that never fully existed. Deviations become automatically framed as problem points, and success is redefined as rigid adherence, regardless of context or outcome.

Complexity, Performance Variability, and Real Success

So, how do pharmaceutical operations succeed so reliably despite the ever-present complexity and variability of daily work?

The answer lies in embracing performance variability as a feature of robust systems, not a flaw. In high-reliability environments—from aviation to medicine to pharmaceutical manufacturing—success is routinely achieved not by demanding strict compliance, but by cultivating adaptive capacity.

Consider environmental monitoring in a sterile suite: The procedure may specify precise times and locations, but a seasoned operator, noticing shifts in people flow or equipment usage, might proactively sample a high-risk area more frequently. This adaptation—not captured in work-as-imagined—actually strengthens data integrity. Yet, traditional metrics would treat this as a procedural deviation.

This is the paradox of the malfunction mindset: in seeking to eliminate all performance variability, we risk undermining precisely those adaptive behaviors that produce reliable quality under uncertainty.

Why the Malfunction Mindset Persists: Cognitive Comfort and Regulatory Reinforcement

Why do organizations continue to privilege the malfunction mindset, even as evidence accumulates of its limits? The answer is both psychological and cultural.

Component breakdown thinking is psychologically satisfying—it offers a clear problem, a specific cause, and a direct fix. For regulatory agencies, it is easy to measure and audit: did the deviation investigation determine the root cause, did the CAPA address it, does the documentation support this narrative? Anything that doesn’t fit this model is hard to defend in audits or inspections.

Yet this approach offers, at best, a partial diagnosis and, at worst, the illusion of control. It encourages organizations to catalog deviations while blindly accepting a much broader universe of unexamined daily adaptations that actually determine system robustness.

Complexity Science and the Art of Organizational Success

To move toward a more accurate—and ultimately more effective—model of quality, pharmaceutical leaders must integrate the insights of complexity science. Drawing from the work of Stuart Kauffman and others at the Santa Fe Institute, we understand that the highest-performing systems operate not at the edge of rigid order, but at the “edge of chaos,” where structure is balanced with adaptability.

In these systems, success and failure both arise from emergent properties—the patterns of interaction between people, procedures, equipment, and environment. The most meaningful interventions, therefore, address how the parts interact, not just how each part functions in isolation.

This explains why traditional root cause analysis, focused on the parts, often fails to produce lasting improvements; it cannot account for outcomes that emerge only from the collective dynamics of the system as a whole.

Investigating for Learning: The Take-the-Best Heuristic

A key innovation needed in pharmaceutical investigations is a shift to what Hollnagel calls Safety-II thinking: focusing on how things go right as well as why they occasionally go wrong.

Here, the take-the-best heuristic becomes crucial. Instead of compiling lists of all deviations, ask: Among all contributing factors, which one, if addressed, would have the most powerful positive impact on future outcomes, while preserving adaptive capacity? This approach ensures investigations generate actionable, meaningful learning, rather than feeding the endless paper chase of “compliance theater.”

Building Systems That Support Adaptive Capability

Taking complexity and adaptive performance seriously requires practical changes to how we design procedures, train, oversee, and measure quality.

Procedure Design: Make explicit the distinction between objectives and methods. Procedures should articulate clear quality goals, specify necessary constraints, but deliberately enable workers to choose methods within those boundaries when faced with new conditions.
Training: Move beyond procedural compliance. Develop adaptive expertise in your staff, so they can interpret and adjust sensibly—understanding not just “what” to do, but “why” it matters in the bigger system.
Oversight and Monitoring: Audit for adaptive capacity. Don’t just track “compliance” but also whether workers have the resources and knowledge to adapt safely and intelligently. Positive performance variability (smart adaptations) should be recognized and studied.
Quality System Design: Build systematic learning from both success and failure. Examine ordinary operations to discern how adaptive mechanisms work, and protect these capabilities rather than squashing them in the name of “control.”

Leadership and Systems Thinking

Realizing this vision depends on a transformation in leadership mindset—from one seeking control to one enabling adaptive capacity. Deming’s profound knowledge and the principles of complexity leadership remind us that what matters is not enforcing ever-stricter compliance, but cultivating an organizational context where smart adaptation and genuine learning become standard.

Leadership must:

Distinguish between complicated and complex: Apply detailed procedures to the former (e.g., calibration), but support flexible, principles-based management for the latter.
Tolerate appropriate uncertainty: Not every problem has a clear, single answer. Creating psychological safety is essential for learning and adaptation during ambiguity.
Develop learning organizations: Invest in deep understanding of operations, foster regular study of work-as-done, and celebrate insights from both expected and unexpected sources.

Practical Strategies for Implementation

Turning these insights into institutional practice involves a systematic, research-inspired approach:

Start procedure development with observation of real work before specifying methods. Small scale and mock exercises are critical.
Employ cognitive apprenticeship models in training, so that experience, reasoning under uncertainty, and systems thinking become core competencies.
Begin investigations with appreciative inquiry—map out how the system usually works, not just how it trips up.
Measure leading indicators (capacity, information flow, adaptability) not just lagging ones (failures, deviations).
Create closed feedback loops for corrective actions—insisting every intervention be evaluated for impact on both compliance and adaptive capacity.

Scientific Quality Management and Adaptive Systems: No Contradiction

The tension between rigorous scientific quality management (QbD, process validation, risk management frameworks) and support for adaptation is a false dilemma. Indeed, genuine scientific quality management starts with humility: the recognition that our understanding of complex systems is always partial, our controls imperfect, and our frameworks provisional.

A falsifiable quality framework embeds learning and adaptation at its core—treating deviations as opportunities to test and refine models, rather than simply checkboxes to complete.

The best organizations are not those that experience the fewest deviations, but those that learn fastest from both expected and unexpected events, and apply this knowledge to strengthen both system structure and adaptive capacity.

Embracing Normal Work: Closing the Gap

Normal pharmaceutical manufacturing is not the story of perfect procedural compliance; it’s the story of people, working together to achieve quality goals under diverse, unpredictable, and evolving conditions. This is both more challenging—and more rewarding—than any plan prescribed solely by SOPs.

To truly move the needle on pharmaceutical quality, organizations must:

Embrace performance variability as evidence of adaptive capacity, not just risk.
Investigate for learning, not blame; study success, not just failure.
Design systems to support both structure and flexible adaptation—never sacrificing one entirely for the other.
Cultivate leadership that values humility, systems thinking, and experimental learning, creating a culture comfortable with complexity.

This approach will not be easy. It means questioning decades of compliance custom, organizational habit, and intellectual ease. But the payoff is immense: more resilient operations, fewer catastrophic surprises, and, above all, improved safety and efficacy for the patients who depend on our products.

The challenge—and the opportunity—facing pharmaceutical quality management is to evolve beyond compliance theater and malfunction thinking into a new era of resilience and organizational learning. Success lies not in the illusory comfort of perfectly executed procedures, but in the everyday adaptations, intelligent improvisation, and system-level capabilities that make those successes possible.

The call to action is clear: Investigate not just to explain what failed, but to understand how, and why, things so often go right. Protect, nurture, and enhance the adaptive capacities of your organization. In doing so, pharmaceutical quality can finally become more than an after-the-fact audit; it will become the creative, resilient capability that patients, regulators, and organizations genuinely want to hire.

Take-the-Best Heuristic for Causal Investigation

The integration of Gigerenzer’s take-the-best heuristic with a causal reasoning framework creates a powerful approach to root cause analysis that addresses one of the most persistent problems in quality investigations: the tendency to generate exhaustive lists of contributing factors without identifying the causal mechanisms that actually drove the event.

Traditional root cause analysis often suffers from what we might call “factor proliferation”—the systematic identification of every possible contributing element without distinguishing between those that were causally necessary for the outcome and those that merely provide context. This comprehensive approach feels thorough but often obscures the most important causal relationships by giving equal weight to diagnostic and non-diagnostic factors.

The take-the-best heuristic offers an elegant solution by focusing investigative effort on identifying the single most causally powerful factor—the factor that, if changed, would have been most likely to prevent the event from occurring. This approach aligns perfectly with causal reasoning’s emphasis on identifying what was actually present and necessary for the outcome, rather than cataloging everything that might have been relevant.

From Counterfactuals to Causal Mechanisms

The most significant advantage of applying take-the-best to causal investigation is its natural resistance to the negative reasoning trap that dominates traditional root cause analysis. When investigators ask “What single factor was most causally responsible for this outcome?” they’re forced to identify positive causal mechanisms rather than falling back on counterfactuals like “failure to follow procedure” or “inadequate training.”

Consider a typical pharmaceutical deviation where a batch fails specification due to contamination. Traditional analysis might identify multiple contributing factors: inadequate cleaning validation, operator error, environmental monitoring gaps, supplier material variability, and equipment maintenance issues. Each factor receives roughly equal attention in the investigation report, leading to broad but shallow corrective actions.

A take-the-best causal approach would ask: “Which single factor, if it had been different, would most likely have prevented this contamination?” The investigation might reveal that the cleaning validation was adequate under normal conditions, but a specific equipment configuration created dead zones that weren’t addressed in the original validation. This equipment configuration becomes the take-the-best factor because changing it would have directly prevented the contamination, regardless of other contributing elements.

This focus on the most causally powerful factor doesn’t ignore other contributing elements—it prioritizes them based on their causal necessity rather than their mere presence during the event.

The Diagnostic Power of Singular Focus

One of Gigerenzer’s key insights about take-the-best is that focusing on the single most diagnostic factor can actually improve decision accuracy compared to complex multivariate approaches. In causal investigation, this translates to identifying the factor that had the greatest causal influence on the outcome—the factor that represents the strongest link in the causal chain.

This approach forces investigators to move beyond correlation and association toward genuine causal understanding. Instead of asking “What factors were present during this event?” the investigation asks “What factor was most necessary and sufficient for this specific outcome to occur?” This question naturally leads to the kind of specific, testable causal statements.

For example, rather than concluding that “multiple factors contributed to the deviation including inadequate procedures, training gaps, and environmental conditions,” a take-the-best causal analysis might conclude that “the deviation occurred because the procedure specified a 30-minute hold time that was insufficient for complete mixing under the actual environmental conditions present during manufacturing, leading to stratification that caused the observed variability.” This statement identifies the specific causal mechanism (insufficient hold time leading to incomplete mixing) while providing the time, place, and magnitude specificity that causal reasoning demands.

Preventing the Generic CAPA Trap

The take-the-best approach to causal investigation naturally prevents one of the most common failures in pharmaceutical quality: the generation of generic, unfocused corrective actions that address symptoms rather than causes. When investigators identify multiple contributing factors without clear causal prioritization, the resulting CAPAs often become diffuse efforts to “improve” everything without addressing the specific mechanisms that drove the event.

By focusing on the single most causally powerful factor, take-the-best investigations generate targeted corrective actions that address the specific mechanism identified as most necessary for the outcome. This creates more effective prevention strategies while avoiding the resource dilution that often accompanies broad-based improvement efforts.

The causal reasoning framework enhances this focus by requiring that the identified factor be described in terms of what actually happened rather than what failed to happen. Instead of “failure to follow cleaning procedures,” the investigation might identify “use of abbreviated cleaning cycle during shift change because operators prioritized production schedule over cleaning thoroughness.” This causal statement directly leads to specific corrective actions: modify shift change procedures, clarify prioritization guidance, or redesign cleaning cycles to be robust against time pressure.

Systematic Application

Implementing take-the-best causal investigation in pharmaceutical quality requires systematic attention to identifying and testing causal hypotheses rather than simply cataloging potential contributing factors. This process follows a structured approach:

Step 1: Event Reconstruction with Causal Focus – Document what actually happened during the event, emphasizing the sequence of causal mechanisms rather than deviations from expected procedure. Focus on understanding why actions made sense to the people involved at the time they occurred.

Step 2: Causal Hypothesis Generation – Develop specific hypotheses about which single factor was most necessary and sufficient for the observed outcome. These hypotheses should make testable predictions about system behavior under different conditions.

Step 3: Diagnostic Testing – Systematically test each causal hypothesis to determine which factor had the greatest influence on the outcome. This might involve data analysis, controlled experiments, or systematic comparison with similar events.

Step 4: Take-the-Best Selection – Identify the single factor that testing reveals to be most causally powerful—the factor that, if changed, would be most likely to prevent recurrence of the specific event.

Step 5: Mechanistic CAPA Development – Design corrective actions that specifically address the identified causal mechanism rather than implementing broad-based improvements across all potential contributing factors.

Integration with Falsifiable Quality Systems

The take-the-best approach to causal investigation creates naturally falsifiable hypotheses that can be tested and validated over time. When an investigation concludes that a specific factor was most causally responsible for an event, this conclusion makes testable predictions about system behavior that can be validated through subsequent experience.

For example, if a contamination investigation identifies equipment configuration as the take-the-best causal factor, this conclusion predicts that similar contamination events will be prevented by addressing equipment configuration issues, regardless of training improvements or procedural changes. This prediction can be tested systematically as the organization gains experience with similar situations.

This integration with falsifiable quality systems creates a learning loop where investigation conclusions are continuously refined based on their predictive accuracy. Investigations that correctly identify the most causally powerful factors will generate effective prevention strategies, while investigations that miss the key causal mechanisms will be revealed through continued problems despite implemented corrective actions.

The Leadership and Cultural Implications

Implementing take-the-best causal investigation requires leadership commitment to genuine learning rather than blame assignment. This approach often reveals system-level factors that leadership helped create or maintain, requiring the kind of organizational humility that the Energy Safety Canada framework emphasizes.

The cultural shift from comprehensive factor identification to focused causal analysis can be challenging for organizations accustomed to demonstrating thoroughness through exhaustive documentation. Leaders must support investigators in making causal judgments and prioritizing factors based on their diagnostic power rather than their visibility or political sensitivity.

This cultural change aligns with the broader shift toward scientific quality management that both the adaptive toolbox and falsifiable quality frameworks require. Organizations must develop comfort with making specific causal claims that can be tested and potentially proven wrong, rather than maintaining the false safety of comprehensive but non-specific factor lists.

The take-the-best approach to causal investigation represents a practical synthesis of rigorous scientific thinking and adaptive decision-making. By focusing on the single most causally powerful factor while maintaining the specific, testable language that causal reasoning demands, this approach generates investigations that are both scientifically valid and operationally useful—exactly what pharmaceutical quality management needs to move beyond the recurring problems that plague traditional root cause analysis.

Why ‘First-Time Right’ is a Dangerous Myth in Continuous Manufacturing

In manufacturing circles, “First-Time Right” (FTR) has become something of a sacred cow-a philosophy so universally accepted that questioning it feels almost heretical. Yet as continuous manufacturing processes increasingly replace traditional batch production, we need to critically examine whether this cherished doctrine serves us well or creates dangerous blind spots in our quality assurance frameworks.

The Seductive Promise of First-Time Right

Let’s start by acknowledging the compelling appeal of FTR. As commonly defined, First-Time Right is both a manufacturing principle and KPI that denotes the percentage of end-products leaving production without quality defects. The concept promises a manufacturing utopia: zero waste, minimal costs, maximum efficiency, and delighted customers receiving perfect products every time.

The math seems straightforward. If you produce 1,000 units and 920 are defect-free, your FTR is 92%. Continuous improvement efforts should steadily drive that percentage upward, reducing the resources wasted on imperfect units.

This principle finds its intellectual foundation in Six Sigma methodology, which can tend to give it an air of scientific inevitability. Yet even Six Sigma acknowledges that perfection remains elusive. This subtle but crucial nuance often gets lost when organizations embrace FTR as an absolute expectation rather than an aspiration.

First-Time Right in biologics drug substance manufacturing refers to the principle and performance metric of producing a biological drug substance that meets all predefined quality attributes and regulatory requirements on the first attempt, without the need for rework, reprocessing, or batch rejection. In this context, FTR emphasizes executing each step of the complex, multi-stage biologics manufacturing process correctly from the outset-starting with cell line development, through upstream (cell culture/fermentation) and downstream (purification, formulation) operations, to the final drug substance release.

Achieving FTR is especially challenging in biologics because these products are made from living systems and are highly sensitive to variations in raw materials, process parameters, and environmental conditions. Even minor deviations can lead to significant quality issues such as contamination, loss of potency, or batch failure, often requiring the entire batch to be discarded.

In biologics manufacturing, FTR is not just about minimizing waste and cost; it is critical for patient safety, regulatory compliance, and maintaining supply reliability. However, due to the inherent variability and complexity of biologics, FTR is best viewed as a continuous improvement goal rather than an absolute expectation. The focus is on designing and controlling processes to consistently deliver drug substances that meet all critical quality attributes-recognizing that, despite best efforts, some level of process variation and deviation is inevitable in biologics production

The Unique Complexities of Continuous Manufacturing

Traditional batch processing creates natural boundaries-discrete points where production pauses, quality can be assessed, and decisions about proceeding can be made. In contrast, continuous manufacturing operates without these convenient checkpoints, as raw materials are continuously fed into the manufacturing system, and finished products are continuously extracted, without interruption over the life of the production run.

This fundamental difference requires a complete rethinking of quality assurance approaches. In continuous environments:

Quality must be monitored and controlled in real-time, without stopping production
Deviations must be detected and addressed while the process continues running
The interconnected nature of production steps means issues can propagate rapidly through the system
Traceability becomes vastly more complex

Regulatory agencies recognize these unique challenges, acknowledging that understanding and managing risks is central to any decision to greenlight CM in a production-ready environment. When manufacturing processes never stop, quality assurance cannot rely on the same methodologies that worked for discrete batches.

The Dangerous Complacency of Perfect-First-Time Thinking

The most insidious danger of treating FTR as an achievable absolute is the complacency it breeds. When leadership becomes fixated on achieving perfect FTR scores, several dangerous patterns emerge:

Overconfidence in Automation

While automation can significantly improve quality, it is important to recognize the irreplaceable value of human oversight. Automated systems, no matter how advanced, are ultimately limited by their programming, design, and maintenance. Human operators bring critical thinking, intuition, and the ability to spot subtle anomalies that machines may overlook. A vigilant human presence can catch emerging defects or process deviations before they escalate, providing a layer of judgment and adaptability that automation alone cannot replicate. Relying solely on automation creates a dangerous blind spot-one where the absence of human insight can allow issues to go undetected until they become major problems. True quality excellence comes from the synergy of advanced technology and engaged, knowledgeable people working together.

Underinvestment in Deviation Management

If perfection is expected, why invest in systems to handle imperfections? Yet robust deviation management-the processes used to identify, document, investigate, and correct deviations becomes even more critical in continuous environments where problems can cascade rapidly. Organizations pursuing FTR often underinvest in the very systems that would help them identify and address the inevitable deviations.

False Sense of Process Robustness

Process robustness refers to the ability of a manufacturing process to tolerate the variability of raw materials, process equipment, operating conditions, environmental conditions and human factors. An obsession with FTR can mask underlying fragility in processes that appear to be performing well under normal conditions. When we pretend our processes are infallible, we stop asking critical questions about their resilience under stress.

Quality Culture Deterioration

When FTR becomes dogma, teams may become reluctant to report or escalate potential issues, fearing they’ll be seen as failures. This creates a culture of silence around deviations-precisely the opposite of what’s needed for effective quality management in continuous manufacturing. When perfection is the only acceptable outcome, people hide imperfections rather than address them.

Magical Thinking in Quality Management

The belief that we can eliminate all errors in complex manufacturing processes amounts to what organizational psychologists call “magical thinking” – the delusional belief that one can do the impossible. In manufacturing, this often manifests as pretending that doing more tasks with less resources will not hurt the work quality.

This is a pattern I’ve observed repeatedly in my investigations of quality failures. When leadership subscribes to the myth that perfection is not just desirable but achievable, they create the conditions for quality disasters. Teams stop preparing for how to handle deviations and start pretending deviations won’t occur.

The irony is that this approach actually undermines the very goal of FTR. By acknowledging the possibility of failure and building systems to detect and learn from it quickly, we actually increase the likelihood of getting things right.

Building a Healthier Quality Culture for Continuous Manufacturing

Rather than chasing the mirage of perfect FTR, organizations should focus on creating systems and cultures that:

Detect deviations rapidly: Continuous monitoring through advanced process control systems becomes essential for monitoring and regulating critical parameters throughout the production process. The question isn’t whether deviations will occur but how quickly you’ll know about them.
Investigate transparently: When issues occur, the focus should be on understanding root causes rather than assigning blame. The culture must prioritize learning over blame.
Implement robust corrective actions: Deviations should be thoroughly documented including details about when and where it occurred, who identified it, a detailed description of the nonconformance, initial actions taken, results of the investigation into the cause, actions taken to correct and prevent recurrence, and a final evaluation of the effectiveness of these actions.
Learn systematically: Each deviation represents a valuable opportunity to strengthen processes and prevent similar issues in the future. The organization that learns fastest wins, not the one that pretends to be perfect.

Breaking the Groupthink Cycle

The FTR myth thrives in environments characterized by groupthink, where challenging the prevailing wisdom is discouraged. When leaders obsess over FTR metrics while punishing those who report deviations, they create the perfect conditions for quality disasters.

This connects to a theme I’ve explored repeatedly on this blog: the dangers of losing institutional memory and critical thinking in quality organizations. When we forget that imperfection is inevitable, we stop building the systems and cultures needed to manage it effectively.

Embracing Humility, Vigilance, and Continuous Learning

True quality excellence comes not from pretending that errors don’t occur, but from embracing a more nuanced reality:

Perfection is a worthy aspiration but an impossible standard
Systems must be designed not just to prevent errors but to detect and address them
A healthy quality culture prizes transparency and learning over the appearance of perfection
Continuous improvement comes from acknowledging and understanding imperfections, not denying them

The path forward requires humility to recognize the limitations of our processes, vigilance to catch deviations quickly when they occur, and an unwavering commitment to learning and improving from each experience.

In the end, the most dangerous quality issues aren’t the ones we detect and address-they’re the ones our systems and culture allow to remain hidden because we’re too invested in the myth that they shouldn’t exist at all. First-Time Right should remain an aspiration that drives improvement, not a dogma that blinds us to reality.

From Perfect to Perpetually Improving

As continuous manufacturing becomes the norm rather than the exception, we need to move beyond the simplistic FTR myth toward a more sophisticated understanding of quality. Rather than asking, “Did we get it perfect the first time?” we should be asking:

How quickly do we detect when things go wrong?
How effectively do we contain and remediate issues?
How systematically do we learn from each deviation?
How resilient are our processes to the variations they inevitably encounter?

These questions acknowledge the reality of manufacturing-that imperfection is inevitable-while focusing our efforts on what truly matters: building systems and cultures capable of detecting, addressing, and learning from deviations to drive continuous improvement.

The companies that thrive in the continuous manufacturing future won’t be those with the most impressive FTR metrics on paper. They’ll be those with the humility to acknowledge imperfection, the systems to detect and address it quickly, and the learning cultures that turn each deviation into an opportunity for improvement.

The Golden Start to a Deviation Investigation

How you respond in the first 24 hours after discovering a deviation can make the difference between a minor quality issue and a major compliance problem. This critical window-what I call “The Golden Day”-represents your best opportunity to capture accurate information, contain potential risks, and set the stage for a successful investigation. When managed effectively, this initial day creates the foundation for identifying true root causes and implementing effective corrective actions that protect product quality and patient safety.

Why the First 24 Hours Matter: The Evidence

The initial response to a deviation is crucial for both regulatory compliance and effective problem-solving. Industry practice and regulatory expectations align on the importance of quick, systematic responses to deviations.

Regulatory expectations explicitly state that deviation investigation and root cause determination should be completed in a timely manner, and industry expectations usually align on deviations being completed within 30 days of discovery.
In the landmark U.S. v. Barr Laboratories case, “the Court declared that all failure investigations must be performed promptly, within thirty business days of the problem’s occurrence”
Best practices recommend assembling a cross-functional team immediately after deviation discovery and conduct initial risk assessment within 24 hours”
Initial actions taken in the first day directly impact the quality and effectiveness of the entire investigation process

When you capitalize on this golden window, you’re working with fresh memories, intact evidence, and the highest chance of observing actual conditions that contributed to the deviation.

Managing Events Systematically

Identifying the Problem: Clarity from the Start

Clear, precise problem definition forms the foundation of any effective investigation. Vague or incomplete problem statements lead to misdirected investigations and ultimately, inadequate corrective actions.

Document using specific, factual language that describes what occurred versus what was expected
Include all relevant details such as procedure and equipment numbers, product names and lot numbers
Apply the 5W2H method (What, When, Where, Who, Why if known, How much is involved, and How it was discovered)
Avoid speculation about causes in the initial description
Remember that the description should incorporate relevant records and photographs of discovered defects.

5W2H	Typical questions	Contains
Who?	Who are the people directly concerned with the problem? Who does this? Who should be involved but wasn’t? Was someone involved who shouldn’t be?	User IDs, Roles and Departments
What?	What happened?	Action, steps, description
When?	When did the problem occur?	Times, dates, place In process
Where?	Where did the problem occur?	Location
Why is it important?	Why did we do this? What are the requirements? What is the expected condition?	Justification, reason
How?	How did we discover. Where in the process was it?	Method, process, procedure
How Many? How Much?	How many things are involved? How often did the situation happen? How much did it impact?	Number, frequency

The quality of your deviation documentation begins with this initial identification. As I’ve emphasized in previous posts, the investigation/deviation report should tell a story that can be easily understood by all parties well after the event and the investigation. This narrative begins with clear identification on day one.

Elements	Problem Statement
Is used to…	Understand and target a problem. Providing a scope. Evaluate any risks. Make objective decisions
Answers the following… (5W2H)	What? (problem that occurred);When? (timing of what occurred); Where? (location of what occurred); Who? (persons involved/observers); Why? (why it matters, not why it occurred); How Much/Many? (volume or count); How Often? (First/only occurrence or multiple)
Contains…	Object (What was affected?); Defect (What went wrong?)
Provides direction for…	Escalation(s); Investigation

Going to the GEMBA: Being Where the Action Is

GEMBA-the actual place where work happens-is a cornerstone concept in quality management. When a deviation occurs, there is no substitute for being physically present at the location.

Observe the actual conditions and environment firsthand
Notice details that might not be captured in written reports
Understand the workflow and context surrounding the deviation
Gather physical evidence before it’s lost or conditions change
Create the opportunity for meaningful conversations with operators

Human error occurs because we are human beings. The extent of our knowledge, training, and skill has little to do with the mistakes we make. We tire, our minds wander and lose concentration, and we must navigate complex processes while satisfying competing goals and priorities – compliance, schedule adherence, efficiency, etc.

Foremost to understanding human performance is knowing that people do what makes sense to them given the available cues, tools, and focus of their attention at the time. Simply put, people come to work to do a good job – if it made sense for them to do what they did, it will make sense to others given similar conditions. The following factors significantly shape human performance and should be the focus of any human error investigation:

Physical Environment Environment, tools, procedures, process design	Organizational Culture Just- or blame-culture, attitude towards error
Management and Supervision Management of personnel, training, procedures	Stress Factors Personal, circumstantial, organizational

We do not want to see or experience human error – but when we do, it’s imperative to view it as a valuable opportunity to improve the system or process. This mindset is the heart of effective human error prevention.

Conducting an Effective GEMBA Walk for Deviations

When conducting your GEMBA walk specifically for deviation investigation:

Arrive with a clear purpose and structured approach
Observe before asking questions
Document observations with photos when appropriate
Look for environmental factors that might not appear in reports
Pay attention to equipment configuration and conditions
Note how operators interact with the process or equipment

A deviation gemba is a cross-functional team meeting that is assembled where a potential deviation event occurred. Going to the gemba and “freezing the scene” as close as possible to the time the event occurred will yield valuable clues about the environment that existed at the time – and fresher memories will provide higher quality interviews. This gemba has specific objectives:

Obtain a common understanding of the event: what happened, when and where it happened, who observed it, who was involved – all the facts surrounding the event. Is it a deviation?
Clearly describe actions taken, or that need to be taken, to contain impact from the event: product quarantine, physical or mechanical interventions, management or regulatory notifications, etc.
Interview involved operators: ask open-ended questions, like how the event unfolded or was discovered, from their perspective, or how the event could have been prevented, in their opinion – insights from personnel experienced with the process can prove invaluable during an investigation.

Deviation GEMBA Tips

Typically there is time between when notification of a deviation gemba goes out and when the team is scheduled to assemble. It is important to come prepared to help facilitate an efficient gemba:

Assemble procedures and other relevant documents and records. This will make references easier during the gemba.
Keep your team on-track – the gemba should end with the team having a common understanding of the event, actions taken to contain impact, and the agreed-upon next steps of the investigation.

You will gain plenty of investigational leads from your observations and interviews at the gemba – which documents to review, which personnel to interview, which equipment history to inspect, and more. The gemba is such an invaluable experience that, for many minor events, root cause and CAPA can be determined fairly easily from information gathered solely at the gemba.

Informal Rubric for Conducting a Good Deviation GEMBA

Describe the timeliness of the team gathering at the gemba.
Were all required roles and experts present?
Was someone leading or facilitating the gemba?
Describe any interviews the team performed during the gemba.
Did the team get sidetracked or off-topic during the gemba
Was the team prepared with relevant documentation or information?
Did the team determine batch impact and any reportability requirements?
Did the team satisfy the objectives of the gemba?
What did the team do well?
What could the team improve upon?

Speaking with Operators: The Power of Cognitive Interviewing

Interviewing personnel who were present when the deviation occurred requires special techniques to elicit accurate, complete information. Traditional questioning often fails to capture critical details.

Cognitive interviewing, as I outlined in my previous post on “Interviewing,” was originally created for law enforcement and later adopted during accident investigations by the National Transportation Safety Board (NTSB). This approach is based on two key principles:

Witnesses need time and encouragement to recall information
Retrieval cues enhance memory recall

How to Apply Cognitive Interviewing in Deviation Investigations

Mental Reinstatement: Encourage the interviewee to mentally recreate the environment and people involved
In-Depth Reporting: Encourage the reporting of all the details, even if it is minor or not directly related
Multiple Perspectives: Ask the interviewee to recall the event from others’ points of view
Several Orders: Ask the interviewee to recount the timeline in different ways. Beginning to end, end to beginning

Most importantly, conduct these interviews at the actual location where the deviation occurred. A key part of this is that retrieval cues access memory. This is why doing the interview on the scene (or Gemba) is so effective.

Component	What It Consists of
Mental Reinstatement	Encourage the interviewee to mentally recreate the environment and people involved.
In-Depth Reporting	Encourage the reporting of all the details.
Multiple Perspectives	Ask the interviewee to recall the event from others’ points of view.
Several Orders	Ask the interviewee to recount the timeline in different ways.

Approach the Interviewee Positively:
- Ask for the interview.
- State the purpose of the interview.
- Tell interviewee why he/she was selected.
- Avoid statements that imply blame.
- Focus on the need to capture knowledge
- Answer questions about the interview.
- Acknowledge and respond to concerns.
- Manage negative emotions.
Apply these Four Components:
- Use mental reinstatement.
- Report everything.
- Change the perspective.
- Change the order.
Apply these Two Principles:
- Witnesses need time and encouragement to recall information.
- Retrieval cues enhance memory recall.
Demonstrate these Skills:
- Recreate the original context and had them walk you through process.
- Tell the witness to actively generate information.
- Adopt the witness’s perspective.
- Listen actively, do not interrupt, and pause before asking follow-up questions.
- Ask open-ended questions.
- Encourage the witness to use imagery.
- Perform interview at the Gemba.
- Follow sequence of the four major components.
- Bring support materials.
- Establish a connection with the witness.
- Do Not tell them how they made the mistake.

Initial Impact Assessment: Understanding the Scope

Within the first 24 hours, a preliminary impact assessment is essential for determining the scope of the deviation and the appropriate response.

Apply a risk-based approach to categorize the deviation as critical, major, or minor
Evaluate all potentially affected products, materials, or batches
Consider potential effects on critical quality attributes
Assess possible regulatory implications
Determine if released products may be affected

This impact assessment is also the initial risk assessment, which will help guide the level of effort put into the deviation.

Factors to Consider in Initial Risk Assessment

Patient safety implications
Product quality impact
Compliance with registered specifications
Potential for impact on other batches or products
Regulatory reporting requirements
Level of investigation required

This initial assessment will guide subsequent decisions about quarantine, notification requirements, and the depth of investigation needed. Remember, this is a preliminary assessment that will be refined as the investigation progresses.

Immediate Actions: Containing the Issue

Once you’ve identified the deviation and assessed its potential impact, immediate actions must be taken to contain the issue and prevent further risk.

Quarantine potentially affected products or materials to prevent their release or further use
Notify key stakeholders, including quality assurance, production supervision, and relevant department heads
Implement temporary corrective or containment measures
Document the deviation in your quality management system
Secure relevant evidence and documentation
Consider whether to stop related processes

Industry best practices emphasize that you should Report the deviation in real-time. Notify QA within 24 hours and hold the GEMBA. Remember that “if you don’t document it, it didn’t happen” – thorough documentation of both the deviation and your immediate response is essential.

Affected vs Related Batches

Not every Impact is the same, so it can be helpful to have two concepts: Affected and Related.

Affected Batch: Product directly impacted by the event at the time of discovery, for instance, the batch being manufactured or tested when the deviation occurred.
Related Batch: Product manufactured or tested under the same conditions or parameters using the process in which the deviation occurred and determined as part of the deviation investigation process to have no impact on product quality.

Setting Up for a Successful Full Investigation

The final step in the golden day is establishing the foundation for the comprehensive investigation that will follow.

Assemble a cross-functional investigation team with relevant expertise
Define clear roles and responsibilities for team members
Establish a timeline for the investigation (remembering the 30-day guideline)
Identify additional data or evidence that needs to be collected
Plan for any necessary testing or analysis
Schedule follow-up interviews or observations

In my post on handling deviations, I emphasized that you must perform a time-sensitive and thorough investigation within 30 days. The groundwork laid during the golden day will make this timeline achievable while maintaining investigation quality.

Planning for Root Cause Analysis

During this setup phase, you should also begin planning which root cause analysis tools might be most appropriate for your investigation. Select tools based on the event complexity and the number of potential root causes and when “human error” appears to be involved, prepare to dig deeper as this is rarely the true root cause

Identifying Phase of your Investigation

If	Then you are at
The problem is not understood. Boundaries have not been set. There could be more than one problem	Problem Understanding
Data needs to be collected. There are questions about frequency or occurrence. You have not had interviews	Data Collection
Data has been collected but not analyszed	Data Analysis
The root cause needs to be determined from the analyzed data	Identify Root Cause

Root Cause Analysis Tools Chart body { font-family: Arial, sans-serif; line-height: 1.6; margin: 20px; } table { border-collapse: collapse; width: 100%; margin-bottom: 20px; } th, td { border: 1px solid #ddd; padding: 8px 12px; vertical-align: top; } th { background-color: #f2f2f2; font-weight: bold; text-align: left; } tr:nth-child(even) { background-color: #f9f9f9; } .purpose-cell { font-weight: bold; } h1 { text-align: center; color: #333; } ul { margin: 0; padding-left: 20px; }

Root Cause Analysis Tools Chart

Purpose	Tool	Description
Problem Understanding	Process Map	A picture of the separate steps of a process in sequential order, including: materials or services entering or leaving the process (inputs and outputs) decisions that must be made people who become involved time involved at each step, and/or process measurements.
	Critical Incident Technique (CIT)	A process used for collecting direct observations of human behavior that have critical significance, and meet methodically defined criteria.
	Comparative Analysis	A technique that focuses a problem-solving team on a problem. It compares one or more elements of a problem or process to evaluate elements that are similar or different (e.g. comparing a standard process to a failing process).
	Performance Matrix	A tool that describes the participation by various roles in completing tasks or deliverables for a project or business process. Note: It is especially useful in clarifying roles and responsibilities in cross-functional/departmental positions.
	5W2H Analysis	An approach that defines a problem and its underlying contributing factors by systematically asking questions related to who, what, when, where, why, how, and how much/often.
Data Collection	Surveys	A technique for gathering data from a targeted audience based on a standard set of criteria.
	Check Sheets	A technique to compile data or observations to detect and show trends/patterns.
	Cognitive Interview	An interview technique used by investigators to help the interviewee recall specific memories from a specific event.
	KNOT Chart	A data collection and classification tool to organize data based on what is Known Need to know Opinion, and Think we know.
Data Analysis	Pareto Chart	A technique that focuses efforts on problems offering the greatest potential for improvement.
	Histogram	A tool that summarizes data collected over a period of time, and graphically presents frequency distribution.
	Scatter Chart	A tool to study possible relationships between changes in two different sets of variables.
	Run Chart	A tool that captures study data for trends/patterns over time.
	Affinity Diagram	A technique for brainstorming and summarizing ideas into natural groupings to understand a problem.
Root Cause Analysis	Interrelationship Digraphs	A tool to identify, analyze, and classify cause and effect relationships among issues so that drivers become part of an effective solution.
	Why-Why	A technique that allows one to explore the cause-and-effect relationships of a particular problem by asking why; drilling down through the underlying contributing causes to identify root cause.
	Is/Is Not	A technique that guides the search for causes of a problem by isolating the who, what, when, where, and how of an event. It narrows the investigation to factors that have an impact and eliminates factors that do not have an impact. By comparing what the problem is with what the problem is not, we can see what is distinctive about a problem which leads to possible causes.
	Structured Brainstorming	A technique to identify, explore, and display the factors within each root cause category that may be affecting the problem/issue, and/or effect being studied through this structured idea-generating tool.
	Cause and Effect Diagram (Ishikawa/Fishbone)	A tool to display potential causes of an event based on root cause categories defined by structured brainstorming using this tool as a visual aid.
	Causal Factor Charting	A tool to analyze human factors and behaviors that contribute to errors, and identify behavior-influencing factors and gaps.
Other Tools	Prioritization Matrix	A tool to systematically compare choices through applying and weighting criteria.
	Control Chart	A tool to monitor process performance over time by studying its variation and source.
	Process Capability	A tool to determine whether a process is capable of meeting requirements or specifications.

Making the Most of Your Golden Day

The first 24 hours after discovering a deviation represent a unique opportunity that should not be wasted. By following the structured approach outlined in this post-identifying the problem clearly, going to the GEMBA, interviewing operators using cognitive techniques, conducting an initial impact assessment, taking immediate containment actions, and setting up for the full investigation-you maximize the value of this golden day.

Remember that excellent deviation management is directly linked to product quality, patient safety, and regulatory compliance. Each well-managed deviation is an opportunity to strengthen your quality system.

I encourage you to assess your current approach to the first 24 hours of deviation management. Are you capturing the full value of this golden day, or are you letting critical information slip away? Implement these strategies, train your team on proper deviation triage, and transform your deviation response from reactive to proactive.

Your deviation management effectiveness doesn’t begin when the investigation report is initiated-it begins the moment a deviation is discovered. Make that golden day count.