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:

  1. 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.
  2. Investigate transparently: When issues occur, the focus should be on understanding root causes rather than assigning blame. The culture must prioritize learning over blame.
  3. 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.
  4. 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.

You Gotta Have Heart: Combating Human Error

The persistent attribution of human error as a root cause deviations reveals far more about systemic weaknesses than individual failings. The label often masks deeper organizational, procedural, and cultural flaws. Like cracks in a foundation, recurring human errors signal where quality management systems (QMS) fail to account for the complexities of human cognition, communication, and operational realities.

The Myth of Human Error as a Root Cause

Regulatory agencies increasingly reject “human error” as an acceptable conclusion in deviation investigations. This shift recognizes that human actions occur within a web of systemic influences. A technician’s missed documentation step or a formulation error rarely stem from carelessness alone but emerge from:

The aviation industry’s “Tower of Babel” problem—where siloed teams develop isolated communication loops—parallels pharmaceutical manufacturing. The Quality Unit may prioritize regulatory compliance, while production focuses on throughput, creating disjointed interpretations of “quality.” These disconnects manifest as errors when cross-functional risks go unaddressed.

Cognitive Architecture and Error Propagation

Human cognition operates under predictable constraints. Attentional biases, memory limitations, and heuristic decision-making—while evolutionarily advantageous—create vulnerabilities in GMP environments. For example:

  • Attentional tunneling: An operator hyper-focused on solving a equipment jam may overlook a temperature excursion alert.
  • Procedural drift: Subtle deviations from written protocols accumulate over time as workers optimize for perceived efficiency.
  • Complacency cycles: Over-familiarity with routine tasks reduces vigilance, particularly during night shifts or prolonged operations.

These cognitive patterns aren’t failures but features of human neurobiology. Effective QMS design anticipates them through:

  1. Error-proofing: Automated checkpoints that detect deviations before critical process stages
  2. Cognitive load management: Procedures (including batch records) tailored to cognitive load principles with decision-support prompts
  3. Resilience engineering: Simulations that train teams to recognize and recover from near-misses

Strategies for Reframing Human Error Analysis

Conduct Cognitive Autopsies

Move beyond 5-Whys to adopt human factors analysis frameworks:

  • Human Error Assessment and Reduction Technique (HEART): Quantifies the likelihood of specific error types based on task characteristics
  • Critical Action and Decision (CAD) timelines: Maps decision points where system defenses failed

For example, a labeling mix-up might reveal:

  • Task factors: Nearly identical packaging for two products (29% contribution to error likelihood)
  • Environmental factors: Poor lighting in labeling area (18%)
  • Organizational factors: Inadequate change control when adding new SKUs (53%)

Redesign for Intuitive Use

The redesign of for intuitive use requires multilayered approaches based on understand how human brains actually work. At the foundation lies procedural chunking, an evidence-based method that restructures complex standard operating procedures (SOPs) into digestible cognitive units aligned with working memory limitations. This approach segments multiphase processes like aseptic filling into discrete verification checkpoints, reducing cognitive overload while maintaining procedural integrity through sequenced validation gates. By mirroring the brain’s natural pattern recognition capabilities, chunked protocols demonstrate significantly higher compliance rates compared to traditional monolithic SOP formats.

Complementing this cognitive scaffolding, mistake-proof redesigns create inherent error detection mechanisms.

To sustain these engineered safeguards, progressive facilities implement peer-to-peer audit protocols during critical operations and transition periods.

Leverage Error Data Analytics

The integration of data analytics into organizational processes has emerged as a critical strategy for minimizing human error, enhancing accuracy, and driving informed decision-making. By leveraging advanced computational techniques, automation, and machine learning, data analytics addresses systemic vulnerabilities.

Human Error Assessment and Reduction Technique (HEART): A Systematic Framework for Error Mitigation

Benefits of the Human Error Assessment and Reduction Technique (HEART)

1. Simplicity and Speed: HEART is designed to be straightforward and does not require complex tools, software, or large datasets. This makes it accessible to organizations without extensive human factors expertise and allows for rapid assessments. The method is easy to understand and apply, even in time-constrained or resource-limited environments.

2. Flexibility and Broad Applicability: HEART can be used across a wide range of industries—including nuclear, healthcare, aviation, rail, process industries, and engineering—due to its generic task classification and adaptability to different operational contexts. It is suitable for both routine and complex tasks.

3. Systematic Identification of Error Influences: The technique systematically identifies and quantifies Error Producing Conditions (EPCs) that increase the likelihood of human error. This structured approach helps organizations recognize the specific factors—such as time pressure, distractions, or poor procedures—that most affect reliability.

4. Quantitative Error Prediction: HEART provides a numerical estimate of human error probability for specific tasks, which can be incorporated into broader risk assessments, safety cases, or design reviews. This quantification supports evidence-based decision-making and prioritization of interventions.

5. Actionable Risk Reduction: By highlighting which EPCs most contribute to error, HEART offers direct guidance on where to focus improvement efforts—whether through engineering redesign, training, procedural changes, or automation. This can lead to reduced error rates, improved safety, fewer incidents, and increased productivity.

6. Supports Accident Investigation and Design: HEART is not only a predictive tool but also valuable in investigating incidents and guiding the design of safer systems and procedures. It helps clarify how and why errors occurred, supporting root cause analysis and preventive action planning.

7. Encourages Safety and Quality Culture and Awareness: Regular use of HEART increases awareness of human error risks and the importance of control measures among staff and management, fostering a proactive culture.

When Is HEART Best Used?

  • Risk Assessment for Critical Tasks: When evaluating tasks where human error could have severe consequences (e.g., operating nuclear control systems, administering medication, critical maintenance), HEART helps quantify and reduce those risks.
  • Design and Review of Procedures: During the design or revision of operational procedures, HEART can identify steps most vulnerable to error and suggest targeted improvements.
  • Incident Investigation: After an failure or near-miss, HEART helps reconstruct the event, identify contributing EPCs, and recommend changes to prevent recurrence.
  • Training and Competence Assessment: HEART can inform training programs by highlighting the conditions and tasks where errors are most likely, allowing for focused skill development and awareness.
  • Resource-Limited or Fast-Paced Environments: Its simplicity and speed make HEART ideal for organizations needing quick, reliable human error assessments without extensive resources or data.

Generic Task Types (GTTs): Establishing Baselines

HEART classifies human activities into nine Generic Task Types (GTT) with predefined nominal human error probabilities (NHEPs) derived from decades of industrial incident data:

GTT CodeTask DescriptionNominal HEP Range
AComplex, novel tasks requiring problem-solving0.55 (0.35–0.97)
BShifting attention between multiple systems0.26 (0.14–0.42)
CHigh-skill tasks under time constraints0.16 (0.12–0.28)
DRule-based diagnostics under stress0.09 (0.06–0.13)
ERoutine procedural tasks0.02 (0.007–0.045)
FRestoring system states0.003 (0.0008–0.007)
GHighly practiced routine operations0.0004 (0.00008–0.009)
HSupervised automated actions0.00002 (0.000006–0.0009)
MMiscellaneous/undefined tasks0.003 (0.008–0.11)

Comprehensive Taxonomy of Error-Producing Conditions (EPCs)

HEART’s 38 Error Producing Conditionss represent contextual amplifiers of error probability, categorized under the 4M Framework (Man, Machine, Media, Management):

EPC CodeDescriptionMax Effect4M Category
1Unfamiliarity with task17×Man
2Time shortage11×Management
3Low signal-to-noise ratio10×Machine
4Override capability of safety featuresMachine
5Spatial/functional incompatibilityMachine
6Model mismatch between mental and system statesMan
7Irreversible actionsMachine
8Channel overload (information density)Media
9Technique unlearningMan
10Inadequate knowledge transfer5.5×Management
11Performance ambiguityMedia
12Misperception of riskMan
13Poor feedback systemsMachine
14Delayed/incomplete feedbackMedia
15Operator inexperienceMan
16Impoverished information qualityMedia
17Inadequate checking proceduresManagement
18Conflicting objectives2.5×Management
19Lack of information diversity2.5×Media
20Educational/training mismatchManagement
21Dangerous incentivesManagement
22Lack of skill practice1.8×Man
23Unreliable instrumentation1.6×Machine
24Need for absolute judgments1.6×Man
25Unclear functional allocation1.6×Management
26No progress tracking1.4×Media
27Physical capability mismatches1.4×Man
28Low semantic meaning of information1.4×Media
29Emotional stress1.3×Man
30Ill-health1.2×Man
31Low workforce morale1.2×Management
32Inconsistent interface design1.15×Machine
33Poor environmental conditions1.1×Media
34Low mental workload1.1×Man
35Circadian rhythm disruption1.06×Man
36External task pacing1.03×Management
37Supernumerary staffing issues1.03×Management
38Age-related capability decline1.02×Man

HEP Calculation Methodology

The HEART equation incorporates both multiplicative and additive effects of EPCs:

Where:

  • NHEP: Nominal Human Error Probability from GTT
  • EPC_i: Maximum effect of i-th EPC
  • APOE_i: Assessed Proportion of Effect (0–1)

HEART Case Study: Operator Error During Biologics Drug Substance Manufacturing

A biotech facility was producing a monoclonal antibody (mAb) drug substance using mammalian cell culture in large-scale bioreactors. The process involved upstream cell culture (expansion and production), followed by downstream purification (protein A chromatography, filtration), and final bulk drug substance filling. The manufacturing process required strict adherence to parameters such as temperature, pH, and feed rates to ensure product quality, safety, and potency.

During a late-night shift, an operator was responsible for initiating a nutrient feed into a 2,000L production bioreactor. The standard operating procedure (SOP) required the feed to be started at 48 hours post-inoculation, with a precise flow rate of 1.5 L/hr for 12 hours. The operator, under time pressure and after a recent shift change, incorrectly programmed the feed rate as 15 L/hr rather than 1.5 L/hr.

Outcome:

  • The rapid addition of nutrients caused a metabolic imbalance, leading to excessive cell growth, increased waste metabolite (lactate/ammonia) accumulation, and a sharp drop in product titer and purity.
  • The batch failed to meet quality specifications for potency and purity, resulting in the loss of an entire production lot.
  • Investigation revealed no system alarms for the high feed rate, and the error was only detected during routine in-process testing several hours later.

HEART Analysis

Task Definition

  • Task: Programming and initiating nutrient feed in a GMP biologics manufacturing bioreactor.
  • Criticality: Direct impact on cell culture health, product yield, and batch quality.

Generic Task Type (GTT)

GTT CodeDescriptionNominal HEP
ERoutine procedural task with checking0.02

Error-Producing Conditions (EPCs) Using the 5M Model

5M CategoryEPC (HEART)Max EffectAPOEExample in Incident
ManInexperience with new feed system (EPC15)0.8Operator recently trained on upgraded control interface
MachinePoor feedback (no alarm for high feed rate, EPC13)0.7System did not alert on out-of-range input
MediaAmbiguous SOP wording (EPC11)0.5SOP listed feed rate as “1.5L/hr” in a table, not text
ManagementTime pressure to meet batch deadlines (EPC2)11×0.6Shift was behind schedule due to earlier equipment delay
MilieuDistraction during shift change (EPC36)1.03×0.9Handover occurred mid-setup, leading to divided attention

Human Error Probability (HEP) Calculation

HEP ≈ 3.5 (350%)
This extremely high error probability highlights a systemic vulnerability, not just an individual lapse.

Root Cause and Contributing Factors

  • Operator: Recently trained, unfamiliar with new interface (Man)
  • System: No feedback or alarm for out-of-spec feed rate (Machine)
  • SOP: Ambiguous presentation of critical parameter (Media)
  • Management: High pressure to recover lost time (Management)
  • Environment: Shift handover mid-task, causing distraction (Milieu)

Corrective Actions

Technical Controls

  • Automated Range Checks: Bioreactor control software now prevents entry of feed rates outside validated ranges and requires supervisor override for exceptions.
  • Visual SOP Enhancements: Critical parameters are now highlighted in both text and tables, and reviewed during operator training.

Human Factors & Training

  • Simulation-Based Training: Operators practice feed setup in a virtual environment simulating distractions and time pressure.
  • Shift Handover Protocol: Critical steps cannot be performed during handover periods; tasks must be paused or completed before/after shift changes.

Management & Environmental Controls

  • Production Scheduling: Buffer time added to schedules to reduce time pressure during critical steps.
  • Alarm System Upgrade: Real-time alerts for any parameter entry outside validated ranges.

Outcomes (6-Month Review)

MetricPre-InterventionPost-Intervention
Feed rate programming errors4/year0/year
Batch failures (due to feed)2/year0/year
Operator confidence (survey)62/10091/100

Lessons Learned

  • Systemic Safeguards: Reliance on operator vigilance alone is insufficient in complex biologics manufacturing; layered controls are essential.
  • Human Factors: Addressing EPCs across the 5M model—Man, Machine, Media, Management, Milieu—dramatically reduces error probability.
  • Continuous Improvement: Regular review of near-misses and operator feedback is crucial for maintaining process robustness in biologics manufacturing.

This case underscores how a HEART-based approach, tailored to biologics drug substance manufacturing, can identify and mitigate multi-factorial risks before they result in costly failures.