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

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.
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.
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.
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.

Quality Policies

‘Reputation’ has no place in any purpose statement!

Great thought piece on the use of “reputation” in purpose statement, which should include quality policies.

Writing a quality policy is a crucial step in establishing a quality management system within an organization. Here are some best practices to consider when crafting an effective quality policy:

Key Components of a Quality Policy

Management’s Quality Commitment

The Quality Policy reflects top management’s dedication to quality standards. It includes clear quality objectives, resource allocation, regular policy reviews, active participation in quality initiatives, and support for quality-focused training. The quality policy is a lynchpin artifact to quality culture.

Customer-Centric Approach

Identify customer requirements.
Meet customer expectations.
Handle customer feedback.
Improve customer satisfaction.
Track customer experience metrics

Drive for Continuous Improvement

Regularly evaluate process effectiveness, product quality metrics, service delivery standards, employee performance, and quality management systems. Document specific improvement methods and set measurable targets.

Steps to Write a Quality Policy

Define the Quality Vision

Develop a concise and inspiring statement that describes what quality means to your organization and how it supports your mission and values.

Identify Quality Objectives

Align these objectives with your strategic goals and customer needs.

Develop the Quality Policy

Focus on clear, actionable statements that reflect your organization’s quality commitments. Include specific quality objectives, measurement criteria, and implementation strategies.

Communicate the Quality Policy

Ensure all employees understand the policy and their roles in implementing it. Use various channels such as publishing on the company website or displaying in premises.

Implement and Review:

Create a structured implementation timeline with clear milestones. Establish communication channels for ongoing feedback and questions. Make sure employees at all levels are involved. Regularly review and refine the policy to ensure it remains relevant and effective.

Additional Best Practices

Keep it Simple and Relevant: Ensure the policy is easy to understand and aligns with your organization’s strategic direction.
Top Management Involvement: Top management should actively participate in creating and endorsing the policy to demonstrate leadership commitment.
ISO Compliance: If applicable, ensure the policy meets ISO standards such as ISO 9001:2015, which requires the policy to be documented, communicated, and enforced by top management.

By following these guidelines, you can create a quality policy that effectively guides your organization towards achieving its quality goals and maintaining a culture of excellence.

Data and a Good Data Culture

I often joke that as a biotech company employee I am primarily responsible for the manufacture of data (and water) first and foremost, and as a result we get a byproduct of a pharmaceutical drugs.

Many of us face challenges within organizations when it comes to effectively managing data. There tends to be a prevailing mindset that views data handling as a distinct activity, often relegated to the responsibility of someone else, rather than recognizing it as an integral part of everyone’s role. This separation can lead to misunderstandings and missed opportunities for utilizing data to its fullest potential.

Many organizations suffer some multifaceted challenges around data management:

Lack of ownership: When data is seen as “someone else’s job,” it often falls through the cracks.
Inconsistent quality: Without a unified approach, data quality can vary widely across departments.
Missed insights: Siloed data management can result in missed opportunities for valuable insights.
Inefficient processes: Disconnected data handling often leads to duplicated efforts and wasted resources.

Integrate Data into Daily Work

Make data part of job descriptions: Clearly define data-related responsibilities for each role, emphasizing how data contributes to overall job performance.
Provide context: Help employees understand how their data-related tasks directly impact business outcomes and decision-making processes.
Encourage data-driven decision making: Train employees to use data in their daily work, from small decisions to larger strategic choices.

We want to strive to ask four questions.

Understanding: Do people understand that they are data creators and how the data they create fits into the bigger picture?
Empowerment: Are there mechanisms for people to voice concerns, suggest potential improvements, and make changes? Do you provide psychological safety so they do so without fear?
Accountability: Do people feel pride of ownership and take on responsibly to create, obtain, and put to work data that supports the organization’s mission?
Collaboration: Do people see themselves as customers of data others create, with the right and responsibility to explain what they need and help creators craft solutions for the good of all involved?

Foster a Data-Driven Culture

Fostering a data-driven culture is essential for organizations seeking to leverage the full potential of their data assets. This cultural shift requires a multi-faceted approach that starts at the top and permeates throughout the entire organization.

Leadership by example is crucial in establishing a data-driven culture. Managers and executives must actively incorporate data into their decision-making processes and discussions. By consistently referencing data in meetings, presentations, and communications, leaders demonstrate the value they place on data-driven insights. This behavior sets the tone for the entire organization, encouraging employees at all levels to adopt a similar approach. When leaders ask data-informed questions and base their decisions on factual evidence, it reinforces the importance of data literacy and analytical thinking across the company.

Continuous learning is another vital component of a data-driven culture. Organizations should invest in regular training sessions that enhance data literacy and proficiency with relevant analysis tools. These educational programs should be tailored to each role within the company, ensuring that employees can apply data skills directly to their specific responsibilities. By providing ongoing learning opportunities, companies empower their workforce to make informed decisions and contribute meaningfully to data-driven initiatives. This investment in employee development not only improves individual performance but also strengthens the organization’s overall analytical capabilities.

Creating effective feedback loops is essential for refining and improving data processes over time. Organizations should establish systems that allow employees to provide input on data-related practices and suggest enhancements. This two-way communication fosters a sense of ownership and engagement among staff, encouraging them to actively participate in the data-driven culture. By valuing employee feedback, companies can identify bottlenecks, streamline processes, and uncover innovative ways to utilize data more effectively. These feedback mechanisms also help in closing the loop between data insights and actionable outcomes, ensuring that the organization continually evolves its data practices to meet changing needs and challenges.

Build Data as a Core Principle

Focus on quality: Emphasize the importance of data quality to the mission of the organization
Continuous improvement: Encourage ongoing refinement of data processes,.
Pride in workmanship: Foster a sense of ownership and pride in data-related tasks, .
Break down barriers: Promote cross-departmental collaboration on data initiatives and eliminate silos.
Drive out fear: Create a safe environment for employees to report data issues or inconsistencies without fear of reprisal.

By implementing these strategies, organizations can effectively tie data to employees’ daily work and create a robust data culture that enhances overall performance and decision-making capabilities.

Be Your Authentic Self

The best quality folks I know, indeed the best of any profession I know, are those who manage to bring their authentic self to the job. This capability is core to building psychological safety and driving quality culture. And yet, too often, we teach people how to bury it or reward a degree of inauthenticity in service of some idea of “professional.” People quickly tune out, disengage, and lose trust when they sense insincerity. Being authentic allows you to connect and relate much more quickly with and bond with our fellow workers. To be an authentic quality champion, you must create a safe space to encourage people to open up and express themselves without fearing retribution. If people do not feel comfortable or safe conveying their feelings, they won’t be able to present their true, authentic selves. Trust is the key to encouraging others to express their thoughts and feelings. Without trust and authenticity, there can be no learning culture, no improvement, and little to no quality.

Be Yourself

Authenticity starts with being true to who you are. Don’t try to adopt a stereotypical quality personality or style that doesn’t feel natural to you. Instead:

Embrace your unique personality and style, whether that’s reserved, energetic, or straightforward
Be honest about your knowledge and expertise
Admit when you don’t know something rather than pretending

By bringing your true self to the role, you build trust and create a psychologically safe environment.

Foster Genuine Connections

By building authentic relationships with colleagues, we can enhance collaboration, boost job satisfaction, and contribute to a more fulfilling professional experience. These connections go beyond superficial interactions and involve showing a genuine interest in coworkers’ success, engaging in healthy competition, and contributing to an authentic workplace culture.

Strive to find time for relationship-building with and among your fellows
Share personal anecdotes and experiences when relevant
Demonstrate vulnerability by discussing your own learning journey and challenges you’ve overcome

Practice Active Listening

Active listening contributes to authenticity by encouraging open communication and transparency. When we actively listen to one another, we create a safe space for sharing ideas, concerns, and feedback without fear of judgment. This openness allows individuals to be true to their personalities and values, fostering a culture where authenticity is valued and respected. Moreover, active listening helps in recognizing the unsaid emotions and underlying messages, enabling a deeper understanding of colleagues’ experiences and perspectives.

Give your full attention to speakers, noting both verbal and non-verbal cues
Paraphrase and summarize to ensure you’ve understood correctly
Ask probing questions to dig deeper into folk’s thoughts and ideas

Model the Desired Culture

When a quality partner brings their authentic self to the team, they set the tone. This demonstrates the behaviors and attitudes we want to see in our culture. This is important at all levels of the quality organization, but frankly I think quality leaders may be a little to uncomfortable here. Many people get ahead in quality by being analytical, which means thse who are outside that norm are asked to act like they are to get ahead. Which frankly, can be prety disastrrious.

Deep Rules

In his column “What You Still Can’t Say at Work” Jim Detert explores the concept of “deep rules” in organizations and their impact on workplace communication and culture. He convincingly argues that despite efforts to improve workplace communication and psychological safety, there are still unwritten “deep rules” that prevent employees from expressing certain thoughts and concerns, particularly those that challenge existing power structures or leadership practices.

To his very good list, I’d add a few around quality:

“Our leaders talk about quality but don’t actually prioritize it when making key decisions.”
“Employees aren’t truly empowered to make quality-related decisions, despite what our policy states.”
“We have processes in place mainly to pass audits, not because they actually improve quality.”
“Quality data is often manipulated or selectively presented to paint a more positive picture.”
“We make decisions based on politics or personal preferences rather than quality data and analysis.”