Tag: expertise

Building a Competency Framework for Quality Professionals as System Gardeners

Quality management requires a sophisticated blend of skills that transcend traditional audit and compliance approaches. As organizations increasingly recognize quality systems as living entities rather than static frameworks, quality professionals must evolve from mere enforcers to nurturers—from auditors to gardeners. This paradigm shift demands a new approach to competency development that embraces both technical expertise and adaptive capabilities.

Building Competencies: The Integration of Skills, Knowledge, and Behavior

A comprehensive competency framework for quality professionals must recognize that true competency is more than a simple checklist of abilities. Rather, it represents the harmonious integration of three critical elements: skills, knowledge, and behaviors. Understanding how these elements interact and complement each other is essential for developing quality professionals who can thrive as “system gardeners” in today’s complex organizational ecosystems.

The Competency Triad

Competencies can be defined as the measurable or observable knowledge, skills, abilities, and behaviors critical to successful job performance. They represent a holistic approach that goes beyond what employees can do to include how they apply their capabilities in real-world contexts.

Knowledge: The Foundation of Understanding

Knowledge forms the theoretical foundation upon which all other aspects of competency are built. For quality professionals, this includes:

  • Comprehension of regulatory frameworks and compliance requirements
  • Understanding of statistical principles and data analysis methodologies
  • Familiarity with industry-specific processes and technical standards
  • Awareness of organizational systems and their interconnections

Knowledge is demonstrated through consistent application to real-world scenarios, where quality professionals translate theoretical understanding into practical solutions. For example, a quality professional might demonstrate knowledge by correctly interpreting a regulatory requirement and identifying its implications for a manufacturing process.

Skills: The Tools for Implementation

Skills represent the practical “how-to” abilities that quality professionals use to implement their knowledge effectively. These include:

  • Technical skills like statistical process control and data visualization
  • Methodological skills such as root cause analysis and risk assessment
  • Social skills including facilitation and stakeholder management
  • Self-management skills like prioritization and adaptability

Skills are best measured through observable performance in relevant contexts. A quality professional might demonstrate skill proficiency by effectively facilitating a cross-functional investigation meeting that leads to meaningful corrective actions.

Behaviors: The Expression of Competency

Behaviors are the observable actions and reactions that reflect how quality professionals apply their knowledge and skills in practice. These include:

  • Demonstrating curiosity when investigating deviations
  • Showing persistence when facing resistance to quality initiatives
  • Exhibiting patience when coaching others on quality principles
  • Displaying integrity when reporting quality issues

Behaviors often distinguish exceptional performers from average ones. While two quality professionals might possess similar knowledge and skills, the one who consistently demonstrates behaviors aligned with organizational values and quality principles will typically achieve superior results.

Building an Integrated Competency Development Approach

To develop well-rounded quality professionals who embody all three elements of competency, organizations should:

  1. Map the Competency Landscape: Create a comprehensive inventory of the knowledge, skills, and behaviors required for each quality role, categorized by proficiency level.
  2. Implement Multi-Modal Development: Recognize that different competency elements require different development approaches:
    • Knowledge is often best developed through structured learning, reading, and formal education
    • Skills typically require practice, coaching, and experiential learning
    • Behaviors are shaped through modeling, feedback, and reflective practice
  3. Assess Holistically: Develop assessment methods that evaluate all three elements:
    • Knowledge assessments through tests, case studies, and discussions
    • Skill assessments through demonstrations, simulations, and work products
    • Behavioral assessments through observation, peer feedback, and self-reflection
  4. Create Developmental Pathways: Design career progression frameworks that clearly articulate how knowledge, skills, and behaviors should evolve as quality professionals advance from foundational to leadership roles.

By embracing this integrated approach to competency development, organizations can nurture quality professionals who not only know what to do and how to do it, but who also consistently demonstrate the behaviors that make quality initiatives successful. These professionals will be equipped to serve as true “system gardeners,” cultivating environments where quality naturally flourishes rather than merely enforcing compliance with standards.

Understanding the Four Dimensions of Professional Skills

A comprehensive competency framework for quality professionals should address four fundamental skill dimensions that work in harmony to create holistic expertise:

Technical Skills: The Roots of Quality Expertise

Technical skills form the foundation upon which all quality work is built. For quality professionals, these specialized knowledge areas provide the essential tools needed to assess, measure, and improve systems.

Examples for Quality Gardeners:

  • Mastery of statistical process control and data analysis methodologies
  • Deep understanding of regulatory requirements and compliance frameworks
  • Proficiency in quality management software and digital tools
  • Knowledge of industry-specific technical processes (e.g., aseptic processing, sterilization validation, downstream chromatography)

Technical skills enable quality professionals to diagnose system health with precision—similar to how a gardener understands soil chemistry and plant physiology.

Methodological Skills: The Framework for System Cultivation

Methodological skills represent the structured approaches and techniques that quality professionals use to organize their work. These skills provide the scaffolding that supports continuous improvement and systematic problem-solving.

Examples for Quality Gardeners:

  • Application of problem solving methodologies
  • Risk management framework, methodology and and tools
  • Design and execution of effective audit programs
  • Knowledge management to capture insights and lessons learned

As gardeners apply techniques like pruning, feeding, and crop rotation, quality professionals use methodological skills to cultivate environments where quality naturally thrives.

Social Skills: Nurturing Collaborative Ecosystems

Social skills facilitate the human interactions necessary for quality to flourish across organizational boundaries. In living quality systems, these skills help create an environment where collaboration and improvement become cultural norms.

Examples for Quality Gardeners:

  • Coaching stakeholders rather than policing them
  • Facilitating cross-functional improvement initiatives
  • Mediating conflicts around quality priorities
  • Building trust through transparent communication
  • Inspiring leadership that emphasizes quality as shared responsibility

Just as gardeners create environments where diverse species thrive together, quality professionals with strong social skills foster ecosystems where teams naturally collaborate toward excellence.

Self-Skills: Personal Adaptability and Growth

Self-skills represent the quality professional’s ability to manage themselves effectively in dynamic environments. These skills are especially crucial in today’s volatile and complex business landscape.

Examples for Quality Gardeners:

  • Adaptability to changing regulatory landscapes and business priorities
  • Resilience when facing resistance to quality initiatives
  • Independent decision-making based on principles rather than rules
  • Continuous personal development and knowledge acquisition
  • Working productively under pressure

Like gardeners who must adapt to changing seasons and unexpected weather patterns, quality professionals need strong self-management skills to thrive in unpredictable environments.

DimensionDefinitionExamplesImportance
Technical SkillReferring to the specialized knowledge and practical skills– Mastering data analysis
– Understanding aseptic processing or freeze drying		Fundamental for any professional role; influences the ability to effectively perform specialized tasks
Methodological SkillAbility to apply appropriate techniques and methods– Applying Scrum or Lean Six Sigma
– Documenting and transferring insights into knowledge		Essential to promote innovation, strategic thinking, and investigation of deviations
Social SkillSkills for effective interpersonal interactions– Promoting collaboration
– Mediating team conflicts
– Inspiring leadership		Important in environments that rely on teamwork, dynamics, and culture
Self-SkillAbility to manage oneself in various professional contexts– Adapting to a fast-paced work environment
– Working productively under pressure
– Independent decision-making		Crucial in roles requiring a high degree of autonomy, such as leadership positions or independent work environments

Developing a Competency Model for Quality Gardeners

Building an effective competency model for quality professionals requires a systematic approach that aligns individual capabilities with organizational needs.

Step 1: Define Strategic Goals and Identify Key Roles

Begin by clearly articulating how quality contributes to organizational success. For a “living systems” approach to quality, goals might include:

  • Cultivating adaptive quality systems that evolve with the organization
  • Building resilience to regulatory changes and market disruptions
  • Fostering a culture where quality is everyone’s responsibility

From these goals, identify the critical roles needed to achieve them, such as:

  • Quality System Architects who design the overall framework
  • Process Gardeners who nurture specific quality processes
  • Cross-Pollination Specialists who transfer best practices across departments
  • System Immunologists who identify and respond to potential threats

Given your organization, you probably will have more boring titles than these. I certainly do, but it is still helpful to use the names when planning and imagining.

Step 2: Identify and Categorize Competencies

For each role, define the specific competencies needed across the four skill dimensions. For example:

Quality System Architect

  • Technical: Understanding of regulatory frameworks and system design principles
  • Methodological: Expertise in process mapping and system integration
  • Social: Ability to influence across the organization and align diverse stakeholders
  • Self: Strategic thinking and long-term vision implementation

Process Gardener

  • Technical: Deep knowledge of specific processes and measurement systems
  • Methodological: Proficiency in continuous improvement and problem-solving techniques
  • Social: Coaching skills and ability to build process ownership
  • Self: Patience and persistence in nurturing gradual improvements

Step 3: Create Behavioral Definitions

Develop clear behavioral indicators that demonstrate proficiency at different levels. For example, for the competency “Cultivating Quality Ecosystems”:

Foundational level: Understands basic principles of quality culture and can implement prescribed improvement tools

Intermediate level: Adapts quality approaches to fit specific team environments and facilitates process ownership among team members

Advanced level: Creates innovative approaches to quality improvement that harness the natural dynamics of the organization

Leadership level: Transforms organizational culture by embedding quality thinking into all business processes and decision-making structures

Step 4: Map Competencies to Roles and Development Paths

Create a comprehensive matrix that aligns competencies with roles and shows progression paths. This allows individuals to visualize their development journey and organizations to identify capability gaps.

For example:

CompetencyQuality SpecialistProcess GardenerQuality System Architect
Statistical AnalysisIntermediateAdvancedIntermediate
Process ImprovementFoundationalAdvancedIntermediate
Stakeholder EngagementFoundationalIntermediateAdvanced
Systems ThinkingFoundationalIntermediateAdvanced

Building a Training Plan for Quality Gardeners

A well-designed training plan translates the competency model into actionable development activities for each individual.

Step 1: Job Description Analysis

Begin by analyzing job descriptions to identify the specific processes and roles each quality professional interacts with. For example, a Quality Control Manager might have responsibilities for:

  • Leading inspection readiness activities
  • Supporting regulatory site inspections
  • Participating in vendor management processes
  • Creating and reviewing quality agreements
  • Managing deviations, change controls, and CAPAs

Step 2: Role Identification

For each job responsibility, identify the specific roles within relevant processes:

ProcessRole
Inspection ReadinessLead
Regulatory Site InspectionsSupport
Vendor ManagementParticipant
Quality AgreementsAuthor/Reviewer
Deviation/CAPAAuthor/Reviewer/Approver
Change ControlAuthor/Reviewer/Approver

Step 3: Training Requirements Mapping

Working with process owners, determine the training requirements for each role. Consider creating modular curricula that build upon foundational skills:

Foundational Quality Curriculum: Regulatory basics, quality system overview, documentation standards

Technical Writing Curriculum: Document creation, effective review techniques, technical communication

Process-Specific Curricula: Tailored training for each process (e.g., change control, deviation management)

Step 4: Implementation and Evolution

Recognize that like the quality systems they support, training plans should evolve over time:

  • Update as job responsibilities change
  • Adapt as processes evolve
  • Incorporate feedback from practical application
  • Balance formal training with experiential learning opportunities

Cultivating Excellence Through Competency Development

Building a competency framework aligned with the “living systems” view of quality management transforms how organizations approach quality professional development. By nurturing technical, methodological, social, and self-skills in balance, organizations create quality professionals who act as true gardeners—professionals who cultivate environments where quality naturally flourishes rather than imposing it through rigid controls.

As quality systems continue to evolve, the most successful organizations will be those that invest in developing professionals who can adapt and thrive amid complexity. These “quality gardeners” will lead the way in creating systems that, like healthy ecosystems, become more resilient and vibrant over time.

Applying the Competency Model

For organizational leadership in quality functions, adopting a competency model is a transformative step toward building a resilient, adaptive, and high-performing team—one that nurtures quality systems as living, evolving ecosystems rather than static structures. The competency model provides a unified language and framework to define, develop, and measure the capabilities needed for success in this gardener paradigm.

The Four Dimensions of the Competency Model

Competency Model DimensionDefinitionExamplesStrategic Importance
Technical CompetencySpecialized knowledge and practical abilities required for quality roles– Understanding aseptic processing
– Mastering root cause analysis
– Operating quality management software		Fundamental for effective execution of specialized quality tasks and ensuring compliance
Methodological CompetencyAbility to apply structured techniques, frameworks, and continuous improvement methods– Applying Lean Six Sigma
– Documenting and transferring process knowledge
– Designing audit frameworks		Drives innovation, strategic problem-solving, and systematic improvement of quality processes
Social CompetencySkills for effective interpersonal interactions and collaboration– Facilitating cross-functional teams
– Mediating conflicts
– Coaching and inspiring others		Essential for cultivating a culture of shared ownership and teamwork in quality initiatives
Self-CompetencyCapacity to manage oneself, adapt, and demonstrate resilience in dynamic environments– Adapting to change
– Working under pressure
– Exercising independent judgment		Crucial for autonomy, leadership, and thriving in evolving, complex quality environments

Leveraging the Competency Model Across Organizational Practices

To fully realize the gardener approach, integrate the competency model into every stage of the talent lifecycle:

Recruitment and Selection

  • Role Alignment: Use the competency model to define clear, role-specific requirements—ensuring candidates are evaluated for technical, methodological, social, and self-competencies, not just past experience.
  • Behavioral Interviewing: Structure interviews around observable behaviors and scenarios that reflect the gardener mindset (e.g., “Describe a time you nurtured a process improvement across teams”).

Rewards and Recognition

  • Competency-Based Rewards: Recognize and reward not only outcomes, but also the demonstration of key competencies—such as collaboration, adaptability, and continuous improvement behaviors.
  • Transparency: Use the competency model to provide clarity on what is valued and how employees can be recognized for growing as “quality gardeners.”

Performance Management

  • Objective Assessment: Anchor performance reviews in the competency model, focusing on both results and the behaviors/skills that produced them.
  • Feedback and Growth: Provide structured, actionable feedback linked to specific competencies, supporting a culture of continuous development and accountability.

Training and Development

  • Targeted Learning: Identify gaps at the individual and team level using the competency model, and develop training programs that address all four competency dimensions.
  • Behavioral Focus: Ensure training goes beyond knowledge transfer, emphasizing the practical application and demonstration of new competencies in real-world settings.

Career Development

  • Progression Pathways: Map career paths using the competency model, showing how employees can grow from foundational to advanced levels in each competency dimension.
  • Self-Assessment: Empower employees to self-assess against the model, identify growth areas, and set targeted development goals.

Succession Planning

  • Future-Ready Talent: Use the competency model to identify and develop high-potential employees who exhibit the gardener mindset and can step into critical roles.
  • Capability Mapping: Regularly assess organizational competency strengths and gaps to ensure a robust pipeline of future leaders aligned with the gardener philosophy.

Leadership Call to Action

For quality organizations moving to the gardener approach, the competency model is a strategic lever. By consistently applying the model across recruitment, recognition, performance, development, career progression, and succession, leadership ensures the entire organization is equipped to nurture adaptive, resilient, and high-performing quality systems.

This integrated approach creates clarity, alignment, and a shared vision for what excellence looks like in the gardener era. It enables quality professionals to thrive as cultivators of improvement, collaboration, and innovation—ensuring your quality function remains vital and future-ready.

The Hidden Pitfalls of Naïve Realism in Problem Solving, Risk Management, and Decision Making

Naïve realism—the unconscious belief that our perception of reality is objective and universally shared—acts as a silent saboteur in professional and personal decision-making. While this mindset fuels confidence, it also blinds us to alternative perspectives, amplifies cognitive biases, and undermines collaborative problem-solving. This blog post explores how this psychological trap distorts critical processes and offers actionable strategies to counteract its influence, drawing parallels to frameworks like the Pareto Principle and insights from risk management research.

Problem Solving: When Certainty Breeds Blind Spots

Naïve realism convinces us that our interpretation of a problem is the only logical one, leading to overconfidence in solutions that align with preexisting beliefs. For instance, teams often dismiss contradictory evidence in favor of data that confirms their assumptions. A startup scaling a flawed product because early adopters praised it—while ignoring churn data—exemplifies this trap. The Pareto Principle’s “vital few” heuristic can exacerbate this bias by oversimplifying complex issues. Organizations might prioritize frequent but low-impact problems, neglecting rare yet catastrophic risks, such as cybersecurity vulnerabilities masked by daily operational hiccups.

Functional fixedness, another byproduct of naïve realism, stifles innovation by assuming resources can only be used conventionally. To mitigate this pitfall, teams should actively challenge assumptions through adversarial brainstorming, asking questions like “Why will this solution fail?” Involving cross-functional teams or external consultants can also disrupt echo chambers, injecting fresh perspectives into problem-solving processes.

Risk Management: The Illusion of Objectivity

Risk assessments are inherently subjective, yet naïve realism convinces decision-makers that their evaluations are purely data-driven. Overreliance on historical data, such as prioritizing minor customer complaints over emerging threats, mirrors the Pareto Principle’s “static and historical bias” pitfall.

Reactive devaluation further complicates risk management. Organizations can counteract these biases by appropriately leveraging risk management to drive subjectivity out while better accounting for uncertainty. Simulating worst-case scenarios, such as sudden supplier price hikes or regulatory shifts, also surfaces blind spots that static models overlook.

Decision Making: The Myth of the Rational Actor

Even in data-driven cultures, subjectivity stealthily shapes choices. Leaders often overestimate alignment within teams, mistaking silence for agreement. Individuals frequently insist their assessments are objective despite clear evidence of self-enhancement bias. This false consensus erodes trust and stifles dissent with the assumption that future preferences will mirror current ones.

Organizations must normalize dissent through anonymous voting or “red team” exercises to dismantle these myths, including having designated critics scrutinize plans. Adopting probabilistic thinking, where outcomes are assigned likelihoods instead of binary predictions, reduces overconfidence.

Acknowledging Subjectivity: Three Practical Steps

1. Map Mental Models

Mapping mental models involves systematically documenting and challenging assumptions to ensure compliance, quality, and risk mitigation. For example, during risk assessments or deviation investigations, teams should explicitly outline their assumptions about processes, equipment, and personnel. Statements such as “We assume the equipment calibration schedule is sufficient to prevent deviations” or “We assume operator training is adequate to avoid errors” can be identified and critically evaluated.

Foster a culture of continuous improvement and accountability by stress-testing assumptions against real-world data—such as audit findings, CAPA (Corrective and Preventive Actions) trends, or process performance metrics—to reveal gaps that might otherwise go unnoticed. For instance, a team might discover that while calibration schedules meet basic requirements, they fail to account for unexpected environmental variables that impact equipment accuracy.

By integrating assumption mapping into routine GMP activities like risk assessments, change control reviews, and deviation investigations, organizations can ensure their decision-making processes are robust and grounded in evidence rather than subjective beliefs. This practice enhances compliance and strengthens the foundation for proactive quality management.

2. Institutionalize ‘Beginner’s Mind’

A beginner’s mindset is about approaching situations with openness, curiosity, and a willingness to learn as if encountering them for the first time. This mindset challenges the assumptions and biases that often limit creativity and problem-solving. In team environments, fostering a beginner’s mindset can unlock fresh perspectives, drive innovation, and create a culture of continuous improvement. However, building this mindset in teams requires intentional strategies and ongoing reinforcement to ensure it is actively utilized.

What is a Beginner’s Mindset?

At its core, a beginner’s mindset involves setting aside preconceived notions and viewing problems or opportunities with fresh eyes. Unlike experts who may rely on established knowledge or routines, individuals with a beginner’s mindset embrace uncertainty and ask fundamental questions such as “Why do we do it this way?” or “What if we tried something completely different?” This perspective allows teams to challenge the status quo, uncover hidden opportunities, and explore innovative solutions that might be overlooked.

For example, adopting this mindset in the workplace might mean questioning long-standing processes that no longer serve their purpose or rethinking how resources are allocated to align with evolving goals. By removing the constraints of “we’ve always done it this way,” teams can approach challenges with curiosity and creativity.

How to Build a Beginner’s Mindset in Teams

Fostering a beginner’s mindset within teams requires deliberate actions from leadership to create an environment where curiosity thrives. Here are some key steps to build this mindset:

  1. Model Curiosity and Openness
    Leaders play a critical role in setting the tone for their teams. By modeling curiosity—asking questions, admitting gaps in knowledge, and showing enthusiasm for learning—leaders demonstrate that it is safe and encouraged to approach work with an open mind. For instance, during meetings or problem-solving sessions, leaders can ask questions like “What haven’t we considered yet?” or “What would we do if we started from scratch?” This signals to team members that exploring new ideas is valued over rigid adherence to past practices.
  2. Encourage Questioning Assumptions
    Teams should be encouraged to question their assumptions regularly. Structured exercises such as “assumption audits” can help identify ingrained beliefs that may no longer hold true. By challenging assumptions, teams open themselves up to new insights and possibilities.
  3. Create Psychological Safety
    A beginner’s mindset flourishes in environments where team members feel safe taking risks and sharing ideas without fear of judgment or failure. Leaders can foster psychological safety by emphasizing that mistakes are learning opportunities rather than failures. For example, during project reviews, instead of focusing solely on what went wrong, leaders can ask, “What did we learn from this experience?” This shifts the focus from blame to growth and encourages experimentation.
  4. Rotate Roles and Responsibilities
    Rotating team members across roles or projects is an effective way to cultivate fresh perspectives. When individuals step into unfamiliar areas of responsibility, they are less likely to rely on habitual thinking and more likely to approach tasks with curiosity and openness. For instance, rotating quality assurance personnel into production oversight roles can reveal inefficiencies or risks that might have been overlooked due to overfamiliarity within silos.
  5. Provide Opportunities for Learning
    Continuous learning is essential for maintaining a beginner’s mindset. Organizations should invest in training programs, workshops, or cross-functional collaborations that expose teams to new ideas and approaches. For example, inviting external speakers or consultants to share insights from other industries can inspire innovative thinking within teams by introducing them to unfamiliar concepts or methodologies.
  6. Use Structured Exercises for Fresh Thinking
    Design Thinking exercises or brainstorming techniques like “reverse brainstorming” (where participants imagine how to create the worst possible outcome) can help teams break free from conventional thinking patterns. These activities force participants to look at problems from unconventional angles and generate novel solutions.

Ensuring Teams Utilize a Beginner’s Mindset

Building a beginner’s mindset is only half the battle; ensuring it is consistently applied requires ongoing reinforcement:

  • Integrate into Processes: Embed beginner’s mindset practices into regular workflows such as project kickoffs, risk assessments, or strategy sessions. For example, make it standard practice to start meetings by revisiting assumptions or brainstorming alternative approaches before diving into execution plans.
  • Reward Curiosity: Recognize and reward behaviors that reflect a beginner’s mindset—such as asking insightful questions, proposing innovative ideas, or experimenting with new approaches—even if they don’t immediately lead to success.
  • Track Progress: Use metrics like the number of new ideas generated during brainstorming sessions or the diversity of perspectives incorporated into decision-making processes to measure how well teams utilize a beginner’s mindset.
  • Reflect Regularly: Encourage teams to reflect on using the beginner’s mindset through retrospectives or debriefs after significant projects and events. Questions like “How did our openness to new ideas impact our results?” or “What could we do differently next time?” help reinforce the importance of maintaining this perspective.

Organizations can ensure their teams consistently leverage the power of a beginner’s mindset by cultivating curiosity, creating psychological safety, and embedding practices that challenge conventional thinking into daily operations. This drives innovation and fosters adaptability and resilience in an ever-changing business landscape.

3. Revisit Assumptions by Practicing Strategic Doubt

Assumptions are the foundation of decision-making, strategy development, and problem-solving. They represent beliefs or premises we take for granted, often without explicit evidence. While assumptions are necessary to move forward in uncertain environments, they are not static. Over time, new information, shifting circumstances, or emerging trends can render them outdated or inaccurate. Periodically revisiting core assumptions is essential to ensure decisions remain relevant, strategies stay robust, and organizations adapt effectively to changing realities.

Why Revisiting Assumptions Matters

Assumptions often shape the trajectory of decisions and strategies. When left unchecked, they can lead to flawed projections, misallocated resources, and missed opportunities. For example, Kodak’s assumption that film photography would dominate forever led to its downfall in the face of digital innovation. Similarly, many organizations assume their customers’ preferences or market conditions will remain stable, only to find themselves blindsided by disruptive changes. Revisiting assumptions allows teams to challenge these foundational beliefs and recalibrate their approach based on current realities.

Moreover, assumptions are frequently made with incomplete knowledge or limited data. As new evidence emerges, whether through research, technological advancements, or operational feedback, testing these assumptions against reality is critical. This process ensures that decisions are informed by the best available information rather than outdated or erroneous beliefs.

How to Periodically Revisit Core Assumptions

Revisiting assumptions requires a structured approach integrating critical thinking, data analysis, and collaborative reflection.

1. Document Assumptions from the Start

The first step is identifying and articulating assumptions explicitly during the planning stages of any project or strategy. For instance, a team launching a new product might document assumptions about market size, customer preferences, competitive dynamics, and regulatory conditions. By making these assumptions visible and tangible, teams create a baseline for future evaluation.

2. Establish Regular Review Cycles

Revisiting assumptions should be institutionalized as part of organizational processes rather than a one-off exercise. Build assumption audits into the quality management process. During these sessions, teams critically evaluate whether their assumptions still hold true in light of recent data or developments. This ensures that decision-making remains agile and responsive to change.

3. Use Feedback Loops

Feedback loops provide real-world insights into whether assumptions align with reality. Organizations can integrate mechanisms such as surveys, operational metrics, and trend analyses into their workflows to continuously test assumptions.

4. Test Assumptions Systematically

Not all assumptions carry equal weight; some are more critical than others. Teams can prioritize testing based on three parameters: severity (impact if the assumption is wrong), probability (likelihood of being inaccurate), and cost of resolution (resources required to validate or adjust). 

5. Encourage Collaborative Reflection

Revisiting assumptions is most effective when diverse perspectives are involved. Bringing together cross-functional teams—including leaders, subject matter experts, and customer-facing roles—ensures that blind spots are uncovered and alternative viewpoints are considered. Collaborative workshops or strategy recalibration sessions can facilitate this process by encouraging open dialogue about what has changed since the last review.

6. Challenge Assumptions with Data

Assumptions should always be validated against evidence rather than intuition alone. Teams can leverage predictive analytics tools to assess whether their assumptions align with emerging trends or patterns. 

How Organizations Can Ensure Assumptions Are Utilized Effectively

To ensure revisited assumptions translate into actionable insights, organizations must integrate them into decision-making processes:

Monitor Continuously: Establish systems for continuously monitoring critical assumptions through dashboards or regular reporting mechanisms. This allows leadership to identify invalidated assumptions promptly and course-correct before significant risks materialize.

Update Strategies and Goals: Adjust goals and objectives based on revised assumptions to maintain alignment with current realities. 

Refine KPIs: Key Performance Indicators (KPIs) should evolve alongside updated assumptions to reflect shifting priorities and external conditions. Metrics that once seemed relevant may need adjustment as new data emerges.

Embed Assumption Testing into Culture: Encourage teams to view assumption testing as an ongoing practice rather than a reactive measure. Leaders can model this behavior by openly questioning their own decisions and inviting critique from others.

From Certainty to Curious Inquiry

Naïve realism isn’t a personal failing but a universal cognitive shortcut. By recognizing its influence—whether in misapplying the Pareto Principle or dismissing dissent—we can reframe conflicts as opportunities for discovery. The goal isn’t to eliminate subjectivity but to harness it, transforming blind spots into lenses for sharper, more inclusive decision-making.

The path to clarity lies not in rigid certainty but in relentless curiosity.

Selecting the Right Consultant for Facility Evaluation

When considering the engagement of an external consultant for your facility, the decision should not be taken lightly. Consultants can provide invaluable insights when addressing compliance gaps, resolving environmental control issues, or conducting design reviews. However, the real value lies in their ability to bring expertise and actionable solutions tailored to your specific needs. To ensure this, assessing their relevant expertise and experience is paramount.

Choosing the Right Consultancy Firm

The first step in evaluating a consultant’s expertise is to scrutinize their professional background and track record. This involves examining their history of projects within your industry and determining whether they have successfully addressed challenges similar to yours. For instance, if you are dealing with deviations in environmental monitoring trends, you should confirm that the consultant has prior experience diagnosing and resolving such issues in facilities governed by comparable regulatory frameworks. Look for evidence of their familiarity with regulations and standards such as FDA 21 CFR Part 211 or ISO 14644 for cleanroom environments. Additionally, assess whether they have worked with facilities of a similar scale and complexity to yours—what works for a small-scale operation may not translate effectively to a larger, more intricate system.

To gain deeper insights into their qualifications, ask targeted questions during the evaluation process. For example:

  • “Can you describe a recent project where you addressed similar challenges? What were the outcomes?”
  • “How do you approach identifying root causes in complex systems?”
  • “What methodologies or tools do you use to ensure compliance with regulatory standards?”
    These questions not only help verify their technical knowledge but also reveal their problem-solving approach and adaptability.

Another critical aspect of assessing expertise is understanding their familiarity with current regulations and industry trends. A consultant who actively engages with updated guidelines from regulatory bodies like the FDA or EMA demonstrates a commitment to staying relevant. You might ask: “How do you stay informed about changes in regulations or advancements in technology that could impact our operations?” Their response can indicate whether they are proactive in maintaining their expertise or rely on outdated practices.

Experience is equally important in assessing whether a consultant can deliver practical, actionable recommendations. Review case studies or examples of past work that demonstrate measurable results—such as improved compliance rates, reduced deviations, or enhanced operational efficiency. Requesting references from previous clients is another effective way to validate their claims. When speaking with references, inquire about the consultant’s ability to communicate effectively, collaborate with internal teams, and deliver results within agreed timelines.

Ultimately, assessing expertise and experience requires a thorough evaluation of both technical qualifications and practical application. By asking detailed questions and reviewing tangible evidence of success, you can ensure that the consultant you hire has the skills and knowledge necessary to address your facility’s unique challenges effectively.

Companies that have participated in GMP remediation in response to warning letters or consent decrees offer a unique perspective on the intricacies of the facility. This experience allows them to:

  1. Identify systemic issues more effectively: Remediation veterans are better equipped to recognize underlying problems that may not be immediately apparent, having seen how seemingly minor issues can cascade into major compliance failures.
  2. Understand regulatory expectations: Direct experience with regulatory agencies during remediation provides insight into their thought processes, priorities, and interpretation of GMP requirements.
  3. Implement sustainable solutions: Those who have been through remediation understand the importance of addressing root causes rather than applying superficial fixes, ensuring long-term compliance.
  4. Prioritize effectively: Experience helps in distinguishing between critical issues that require immediate attention and those that can be addressed over time, allowing for more efficient resource allocation

Questions to Ask During Evaluation

To identify the best fit for your needs, ask potential consultants these critical questions:

  1. Can you provide examples of similar projects you’ve completed?
    • This helps verify their experience with challenges of GMP facilities.
    • Look for previous remediation experience
  2. What methodologies do you use?
    • Ensure their approach aligns with your facility’s operational style and regulatory requirements.
  3. How do you ensure actionable recommendations?
    • Look for consultants who provide clear implementation plans rather than vague advice.
  4. How do you handle confidentiality?
    • Confirm safeguards are in place to protect sensitive information.
  5. Can you share references from past clients?
    • Contact references to assess reliability, responsiveness, and outcomes achieved.
  6. What is your communication style?
    • Evaluate their ability to provide timely updates and collaborate effectively with your team.

Ensuring Actionable Outcomes

The ultimate goal of hiring a consultant is actionable improvements that enhance compliance, efficiency, or performance. To achieve this:

  1. Define Clear Objectives
    • Before engaging a consultant, outline your project scope, goals, budget, and desired outcomes. This clarity helps both parties align expectations.
  2. Insist on Detailed Proposals
    • Request proposals that include timelines, deliverables, methodologies, and pricing structures. This ensures transparency and sets benchmarks for success.
  3. Collaborate Throughout the Process
    • Involve your team in discussions with the consultant to ensure alignment on priorities and feasibility of recommendations.
  4. Monitor Implementation
    • Establish metrics to track progress against the consultant’s recommendations (e.g., compliance rates, operational efficiency improvements).

Communication Loops and Silos: A Barrier to Effective Decision Making in Complex Industries

In complex industries such as aviation and biotechnology, effective communication is crucial for ensuring safety, quality, and efficiency. However, the presence of communication loops and silos can significantly hinder these efforts. The concept of the “Tower of Babel” problem, as explored in the aviation sector by Follet, Lasa, and Mieusset in HS36, highlights how different professional groups develop their own languages and operate within isolated loops, leading to misunderstandings and disconnections. This article has really got me thinking about similar issues in my own industry.

The Tower of Babel Problem: A Thought-Provoking Perspective

The HS36 article provides a thought-provoking perspective on the “Tower of Babel” problem, where each aviation professional feels in control of their work but operates within their own loop. This phenomenon is reminiscent of the biblical story where a common language becomes fragmented, causing confusion and separation among people. In modern industries, this translates into different groups using their own jargon and working in isolation, making it difficult for them to understand each other’s perspectives and challenges.

For instance, in aviation, air traffic controllers (ATCOs), pilots, and managers each have their own “loop,” believing they are in control of their work. However, when these loops are disconnected, it can lead to miscommunication, especially when each group uses different terminology and operates under different assumptions about how work should be done (work-as-prescribed vs. work-as-done). This issue is equally pertinent in the biotech industry, where scientists, quality assurance teams, and regulatory affairs specialists often work in silos, which can impede the development and approval of new products.

Tower of Babel by Joos de Momper, Old Masters Museum

Impact on Decision Making

Decision making in biotech is heavily influenced by Good Practice (GxP) guidelines, which emphasize quality, safety, and compliance – and I often find that the aviation industry, as a fellow highly regulated industry, is a great place to draw perspective.

When communication loops are disconnected, decisions may not fully consider all relevant perspectives. For example, in GMP (Good Manufacturing Practice) environments, quality control teams might focus on compliance with regulatory standards, while research and development teams prioritize innovation and efficiency. If these groups do not effectively communicate, decisions might overlook critical aspects, such as the practicality of implementing new manufacturing processes or the impact on product quality.

Furthermore, ICH Q9(R1) guideline emphasizes the importance of reducing subjectivity in Quality Risk Management (QRM) processes. Subjectivity can arise from personal opinions, biases, or inconsistent interpretations of risks by stakeholders, impacting every stage of QRM. To combat this, organizations must adopt structured approaches that prioritize scientific knowledge and data-driven decision-making. Effective knowledge management is crucial in this context, as it involves systematically capturing, organizing, and applying internal and external knowledge to inform QRM activities.

Academic Research on Communication Loops

Research in organizational behavior and communication highlights the importance of bridging these silos. Studies have shown that informal interactions and social events can significantly improve relationships and understanding among different professional groups (Katz & Fodor, 1963). In the biotech industry, fostering a culture of open communication can help ensure that GxP decisions are well-rounded and effective.

Moreover, the concept of “work-as-done” versus “work-as-prescribed” is relevant in biotech as well. Operators may adapt procedures to fit practical realities, which can lead to discrepancies between intended and actual practices. This gap can be bridged by encouraging feedback and continuous improvement processes, ensuring that decisions reflect both regulatory compliance and operational feasibility.

Case Studies and Examples

  1. Aviation Example: The HS36 article provides a compelling example of how disconnected loops can hinder effective decision making in aviation. For instance, when a standardized phraseology was introduced, frontline operators felt that this change did not account for their operational needs, leading to resistance and potential safety issues. This illustrates how disconnected loops can hinder effective decision making.
  2. Product Development: In the development of a new biopharmaceutical, different teams might have varying priorities. If the quality assurance team focuses solely on regulatory compliance without fully understanding the manufacturing challenges faced by production teams, this could lead to delays or quality issues. By fostering cross-functional communication, these teams can align their efforts to ensure both compliance and operational efficiency.
  3. ICH Q9(R1) Example: The revised ICH Q9(R1) guideline emphasizes the need to manage and minimize subjectivity in QRM. For instance, in assessing the risk of a new manufacturing process, a structured approach using historical data and scientific evidence can help reduce subjective biases. This ensures that decisions are based on comprehensive data rather than personal opinions.
  4. Technology Deployment: . A recent FDA Warning Letter to Sanofi highlighted the importance of timely technological upgrades to equipment and facility infrastructure. This emphasizes that staying current with technological advancements is essential for maintaining regulatory compliance and ensuring product quality. However the individual loops of decision making amongst the development teams, operations and quality can lead to major mis-steps.
Reducing Subjectivity in Quality Risk Management: Aligning with ICH Q9(R1)

Strategies for Improvement

To overcome the challenges posed by communication loops and silos, organizations can implement several strategies:

  • Promote Cross-Functional Training: Encourage professionals to explore other roles and challenges within their organization. This can help build empathy and understanding across different departments.
  • Foster Informal Interactions: Organize social events and informal meetings where professionals from different backgrounds can share experiences and perspectives. This can help bridge gaps between silos and improve overall communication.
  • Define Core Knowledge: Establish a minimum level of core knowledge that all stakeholders should possess. This can help ensure that everyone has a basic understanding of each other’s roles and challenges.
  • Implement Feedback Loops: Encourage continuous feedback and improvement processes. This allows organizations to adapt procedures to better reflect both regulatory requirements and operational realities.
  • Leverage Knowledge Management: Implement robust knowledge management systems to reduce subjectivity in decision-making processes. This involves capturing, organizing, and applying internal and external knowledge to inform QRM activities.

Combating Subjectivity in Decision Making

In addition to bridging communication loops, reducing subjectivity in decision making is crucial for ensuring quality and safety. The revised ICH Q9(R1) guideline provides several strategies for this:

  • Structured Approaches: Use structured risk assessment tools and methodologies to minimize personal biases and ensure that decisions are based on scientific evidence.
  • Data-Driven Decision Making: Prioritize data-driven decision making by leveraging historical data and real-time information to assess risks and opportunities.
  • Cognitive Bias Awareness: Train stakeholders to recognize and mitigate cognitive biases that can influence risk assessments and decision-making processes.

Conclusion

In complex industries effective communication is essential for ensuring safety, quality, and efficiency. The presence of communication loops and silos can lead to misunderstandings and poor decision making. By promoting cross-functional understanding, fostering informal interactions, and implementing feedback mechanisms, organizations can bridge these gaps and improve overall performance. Additionally, reducing subjectivity in decision making through structured approaches and data-driven decision making is critical for ensuring compliance with GxP guidelines and maintaining product quality. As industries continue to evolve, addressing these communication challenges will be crucial for achieving success in an increasingly interconnected world.

References:

  • Follet, S., Lasa, S., & Mieusset, L. (n.d.). The Tower of Babel Problem in Aviation. In HindSight Magazine, HS36. Retrieved from https://skybrary.aero/sites/default/files/bookshelf/hs36/HS36-Full-Magazine-Hi-Res-Screen-v3.pdf
  • Katz, D., & Fodor, J. (1963). The Structure of a Semantic Theory. Language, 39(2), 170–210.
  • Dekker, S. W. A. (2014). The Field Guide to Understanding Human Error. Ashgate Publishing.
  • Shorrock, S. (2023). Editorial. Who are we to judge? From work-as-done to work-as-judged. HindSight, 35, Just Culture…Revisited. Brussels: EUROCONTROL.

Reducing Subjectivity in Quality Risk Management: Aligning with ICH Q9(R1)

In a previous post, I discussed how overcoming subjectivity in risk management and decision-making requires fostering a culture of quality and excellence. This is an issue that it is important to continue to evaluate and push for additional improvement.

Overcoming Subjectivity in Risk Management and Decision Making Requires a Culture of Quality and Excellence

The revised ICH Q9(R1) guideline, finalized in January 2023, introduces critical updates to Quality Risk Management (QRM) practices, emphasizing the need to address subjectivity, enhance formality, improve risk-based decision-making, and manage product availability risks. These revisions aim to ensure that QRM processes are more science-driven, knowledge-based, and effective in safeguarding product quality and patient safety. Two years later it is important to continue to build on key strategies for reducing subjectivity in QRM and aligning with the updated requirements.

Understanding Subjectivity in QRM

Subjectivity in QRM arises from personal opinions, biases, heuristics, or inconsistent interpretations of risks by stakeholders. This can impact every stage of the QRM process—from hazard identification to risk evaluation and mitigation. The revised ICH Q9(R1) explicitly addresses this issue by introducing a new subsection, “Managing and Minimizing Subjectivity,” which emphasizes that while subjectivity cannot be entirely eliminated, it can be controlled through structured approaches.

The guideline highlights that subjectivity often stems from poorly designed scoring systems, differing perceptions of hazards and risks among stakeholders, and cognitive biases. To mitigate these challenges, organizations must adopt robust strategies that prioritize scientific knowledge and data-driven decision-making.

Strategies to Reduce Subjectivity

Leveraging Knowledge Management

ICH Q9(R1) underscores the importance of knowledge management as a tool to reduce uncertainty and subjectivity in risk assessments. Effective knowledge management involves systematically capturing, organizing, and applying internal and external knowledge to inform QRM activities. This includes maintaining centralized repositories for technical data, fostering real-time information sharing across teams, and learning from past experiences through structured lessons-learned processes.

By integrating knowledge management into QRM, organizations can ensure that decisions are based on comprehensive data rather than subjective estimations. For example, using historical data on process performance or supplier reliability can provide objective insights into potential risks.

To integrate knowledge management (KM) more effectively into quality risk management (QRM), organizations can implement several strategies to ensure decisions are based on comprehensive data rather than subjective estimations:

Establish Robust Knowledge Repositories

Create centralized, easily accessible repositories for storing and organizing historical data, lessons learned, and best practices. These repositories should include:

  • Process performance data
  • Supplier reliability metrics
  • Deviation and CAPA records
  • Audit findings and inspection observations
  • Technology transfer documentation

By maintaining these repositories, organizations can quickly access relevant historical information when conducting risk assessments.

Implement Knowledge Mapping

Conduct knowledge mapping exercises to identify key sources of knowledge within the organization. This process helps to:

Use the resulting knowledge maps to guide risk assessment teams to relevant information and expertise.

Develop Data Analytics Capabilities

Invest in data analytics tools and capabilities to extract meaningful insights from historical data. For example:

  • Use statistical process control to identify trends in manufacturing performance
  • Apply machine learning algorithms to predict potential quality issues based on historical patterns
  • Utilize data visualization tools to present complex risk data in an easily understandable format

These analytics can provide objective, data-driven insights into potential risks and their likelihood of occurrence.

Integrate KM into QRM Processes

Embed KM activities directly into QRM processes to ensure consistent use of available knowledge:

  • Include a knowledge gathering step at the beginning of risk assessments
  • Require risk assessment teams to document the sources of knowledge used in their analysis
  • Implement a formal process for capturing new knowledge generated during risk assessments

This integration helps ensure that all relevant knowledge is considered and that new insights are captured for future use.

Foster a Knowledge-Sharing Culture

Encourage a culture of knowledge sharing and collaboration within the organization:

  • Implement mentoring programs to facilitate the transfer of tacit knowledge
  • Establish communities of practice around key risk areas
  • Recognize and reward employees who contribute valuable knowledge to risk management efforts

By promoting knowledge sharing, organizations can tap into the collective expertise of their workforce to improve risk assessments.

Implementing Structured Risk-Based Decision-Making

The revised guideline introduces a dedicated section on risk-based decision-making, emphasizing the need for structured approaches that consider the complexity, uncertainty, and importance of decisions. Organizations should establish clear criteria for decision-making processes, define acceptable risk tolerance levels, and use evidence-based methods to evaluate options.

Structured decision-making tools can help standardize how risks are assessed and prioritized. Additionally, calibrating expert opinions through formal elicitation techniques can further reduce variability in judgments.

Addressing Cognitive Biases

Cognitive biases—such as overconfidence or anchoring—can distort risk assessments and lead to inconsistent outcomes. To address this, organizations should provide training on recognizing common biases and their impact on decision-making. Encouraging diverse perspectives within risk assessment teams can also help counteract individual biases.

For example, using cross-functional teams ensures that different viewpoints are considered when evaluating risks, leading to more balanced assessments. Regularly reviewing risk assessment outputs for signs of bias or inconsistencies can further enhance objectivity.

Enhancing Formality in QRM

ICH Q9(R1) introduces the concept of a “formality continuum,” which aligns the level of effort and documentation with the complexity and significance of the risk being managed. This approach allows organizations to allocate resources effectively by applying less formal methods to lower-risk issues while reserving rigorous processes for high-risk scenarios.

For instance, routine quality checks may require minimal documentation compared to a comprehensive risk assessment for introducing new manufacturing technologies. By tailoring formality levels appropriately, organizations can ensure consistency while avoiding unnecessary complexity.

Calibrating Expert Opinions

We need to recognize the importance of expert knowledge in QRM activities, but also acknowledges the potential for subjectivity and bias in expert judgments. We need to ensure we:

  • Implement formal processes for expert opinion elicitation
  • Use techniques to calibrate expert judgments, especially when estimating probabilities
  • Provide training on common cognitive biases and their impact on risk assessment
  • Employ diverse teams to counteract individual biases
  • Regularly review risk assessment outputs for signs of bias or inconsistencies

Calibration techniques may include:

  • Structured elicitation protocols that break down complex judgments into more manageable components
  • Feedback and training to help experts align their subjective probability estimates with actual frequencies of events
  • Using multiple experts and aggregating their judgments through methods like Cooke’s classical model
  • Employing facilitation techniques to mitigate groupthink and encourage independent thinking

By calibrating expert opinions, organizations can leverage valuable expertise while minimizing subjectivity in risk assessments.

Utilizing Cooke’s Classical Model

Cooke’s Classical Model is a rigorous method for evaluating and combining expert judgments to quantify uncertainty. Here are the key steps for using the Classical Model to evaluate expert judgment:

Select and calibrate experts:
    • Choose 5-10 experts in the relevant field
    • Have experts assess uncertain quantities (“calibration questions”) for which true values are known or will be known soon
    • These calibration questions should be from the experts’ domain of expertise
    Elicit expert assessments:
      • Have experts provide probabilistic assessments (usually 5%, 50%, and 95% quantiles) for both calibration questions and questions of interest
      • Document experts’ reasoning and rationales
      Score expert performance:
      • Evaluate experts on two measures:
        a) Statistical accuracy: How well their probabilistic assessments match the true values of calibration questions
        b) Informativeness: How precise and focused their uncertainty ranges are
      Calculate performance-based weights:
        • Derive weights for each expert based on their statistical accuracy and informativeness scores
        • Experts performing poorly on calibration questions receive little or no weight
        Combine expert assessments:
        • Use the performance-based weights to aggregate experts’ judgments on the questions of interest
        • This creates a “Decision Maker” combining the experts’ assessments
        Validate the combined assessment:
        • Evaluate the performance of the weighted combination (“Decision Maker”) using the same scoring as for individual experts
        • Compare to equal-weight combination and best-performing individual experts
        Conduct robustness checks:
        • Perform cross-validation by using subsets of calibration questions to form weights
        • Assess how well performance on calibration questions predicts performance on questions of interest

        The Classical Model aims to create an optimal aggregate assessment that outperforms both equal-weight combinations and individual experts. By using objective performance measures from calibration questions, it provides a scientifically defensible method for evaluating and synthesizing expert judgment under uncertainty.

        Using Data to Support Decisions

        ICH Q9(R1) emphasizes the importance of basing risk management decisions on scientific knowledge and data. The guideline encourages organizations to:

        • Develop robust knowledge management systems to capture and maintain product and process knowledge
        • Create standardized repositories for technical data and information
        • Implement systems to collect and convert data into usable knowledge
        • Gather and analyze relevant data to support risk-based decisions
        • Use quantitative methods where feasible, such as statistical models or predictive analytics

        Specific approaches for using data in QRM may include:

        • Analyzing historical data on process performance, deviations, and quality issues to inform risk assessments
        • Employing statistical process control and process capability analysis to evaluate and monitor risks
        • Utilizing data mining and machine learning techniques to identify patterns and potential risks in large datasets
        • Implementing real-time data monitoring systems to enable proactive risk management
        • Conducting formal data quality assessments to ensure decisions are based on reliable information

        Digitalization and emerging technologies can support data-driven decision making, but remember that validation requirements for these technologies should not be overlooked.

        Improving Risk Assessment Tools

        The design of risk assessment tools plays a critical role in minimizing subjectivity. Tools with well-defined scoring criteria and clear guidance on interpreting results can reduce variability in how risks are evaluated. For example, using quantitative methods where feasible—such as statistical models or predictive analytics—can provide more objective insights compared to qualitative scoring systems.

        Organizations should also validate their tools periodically to ensure they remain fit-for-purpose and aligned with current regulatory expectations.

        Leverage Good Risk Questions

        A well-formulated risk question can significantly help reduce subjectivity in quality risk management (QRM) activities. Here’s how a good risk question contributes to reducing subjectivity:

        Clarity and Focus

        A good risk question provides clarity and focus for the risk assessment process. By clearly defining the scope and context of the risk being evaluated, it helps align all participants on what specifically needs to be assessed. This alignment reduces the potential for individual interpretations and subjective assumptions about the risk scenario.

        Specific and Measurable Terms

        Effective risk questions use specific and measurable terms rather than vague or ambiguous language. For example, instead of asking “What are the risks to product quality?”, a better question might be “What are the potential causes of out-of-specification dissolution results for Product X in the next 6 months?”. The specificity in the latter question helps anchor the assessment in objective, measurable criteria.

        Factual Basis

        A well-crafted risk question encourages the use of factual information and data rather than opinions or guesses. It should prompt the risk assessment team to seek out relevant data, historical information, and scientific knowledge to inform their evaluation. This focus on facts and evidence helps minimize the influence of personal biases and subjective judgments.

        Standardized Approach

        Using a consistent format for risk questions across different assessments promotes a standardized approach to risk identification and analysis. This consistency reduces variability in how risks are framed and evaluated, thereby decreasing the potential for subjective interpretations.

        Objective Criteria

        Good risk questions often incorporate or imply objective criteria for risk evaluation. For instance, a question like “What factors could lead to a deviation from the acceptable range of 5-10% for impurity Y?” sets clear, objective parameters for the assessment, reducing the room for subjective interpretation of what constitutes a significant risk.

        Promotes Structured Thinking

        Well-formulated risk questions encourage structured thinking about potential hazards, their causes, and consequences. This structured approach helps assessors focus on objective factors and causal relationships rather than relying on gut feelings or personal opinions.

        Facilitates Knowledge Utilization

        A good risk question should prompt the assessment team to utilize available knowledge effectively. It encourages the team to draw upon relevant data, past experiences, and scientific understanding, thereby grounding the assessment in objective information rather than subjective impressions.

        By crafting risk questions that embody these characteristics, QRM practitioners can significantly reduce the subjectivity in risk assessments, leading to more reliable, consistent, and scientifically sound risk management decisions.

        Fostering a Culture of Continuous Improvement

        Reducing subjectivity in QRM is an ongoing process that requires a commitment to continuous improvement. Organizations should regularly review their QRM practices to identify areas for enhancement and incorporate feedback from stakeholders. Investing in training programs that build competencies in risk assessment methodologies and decision-making frameworks is essential for sustaining progress.

        Moreover, fostering a culture that values transparency, collaboration, and accountability can empower teams to address subjectivity proactively. Encouraging open discussions about uncertainties or disagreements during risk assessments can lead to more robust outcomes.

        Conclusion

        The revisions introduced in ICH Q9(R1) represent a significant step forward in addressing long-standing challenges associated with subjectivity in QRM. By leveraging knowledge management, implementing structured decision-making processes, addressing cognitive biases, enhancing formality levels appropriately, and improving risk assessment tools, organizations can align their practices with the updated guidelines while ensuring more reliable and science-based outcomes.

        It has been two years, it is long past time be be addressing these in your risk management process and quality system.

        Ultimately, reducing subjectivity not only strengthens compliance with regulatory expectations but also enhances the quality of pharmaceutical products and safeguards patient safety—a goal that lies at the heart of effective Quality Risk Management.