Tag: Industry 4.0

Industry 5.0, seriously?

This morning, an article landed in my inbox with the headline: “Why MES Remains the Digital Backbone, Even in Industry 5.0.” My immediate reaction? “You have got to be kidding me.” Honestly, that was also my second, third, and fourth reaction—each one a little more exasperated than the last. Sometimes, it feels like this relentless urge to slap a new number on every wave of technology is exactly why we can’t have nice things.

Curiosity got the better of me, though, and I clicked through. To my surprise, the article raised some interesting points. Still, I couldn’t help but wonder: do we really need another numbered revolution?

So, what exactly is Industry 5.0—and why is everyone talking about it? Let’s dig in.

The Origins and Evolution of Industry 5.0: From Japanese Society 5.0 to European Industrial Policy

The concept of Industry 5.0 emerged from a complex interplay of Japanese technological philosophy and European industrial policy, representing a fundamental shift from purely efficiency-driven manufacturing toward human-centric, sustainable, and resilient production systems. While the term “Industry 5.0” was formally coined by the European Commission in 2021, its intellectual foundations trace back to Japan’s Society 5.0 concept introduced in 2016, which envisioned a “super-smart society” that integrates cyberspace and physical space to address societal challenges. This evolution reflects a growing recognition that the Fourth Industrial Revolution’s focus on automation and digitalization, while transformative, required rebalancing to prioritize human welfare, environmental sustainability, and social resilience alongside technological advancement.

The Japanese Foundation: Society 5.0 as Intellectual Precursor

The conceptual roots of Industry 5.0 can be traced directly to Japan’s Society 5.0 initiative, which was first proposed in the Fifth Science and Technology Basic Plan adopted by the Japanese government in January 2016. This concept emerged from intensive deliberations by expert committees administered by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and the Ministry of Economy, Trade and Industry (METI) since 2014. Society 5.0 was conceived as Japan’s response to the challenges of an aging population, economic stagnation, and the need to compete in the digital economy while maintaining human-centered values.

The Japanese government positioned Society 5.0 as the fifth stage of human societal development, following the hunter-gatherer society (Society 1.0), agricultural society (Society 2.0), industrial society (Society 3.0), and information society (Society 4.0). This framework was designed to address Japan’s specific challenges, including rapid population aging, social polarization, and depopulation in rural areas. The concept gained significant momentum when it was formally presented by former Prime Minister Shinzo Abe in 2019 and received robust support from the Japan Business Federation (Keidanren), which saw it as a pathway to economic revitalization.

International Introduction and Recognition

The international introduction of Japan’s Society 5.0 concept occurred at the CeBIT 2017 trade fair in Hannover, Germany, where the Japanese Business Federation presented this vision of digitally transforming society as a whole. This presentation marked a crucial moment in the global diffusion of ideas that would later influence the development of Industry 5.0. The timing was significant, as it came just six years after Germany had introduced the Industry 4.0 concept at the same venue in 2011, creating a dialogue between different national approaches to industrial and societal transformation.

The Japanese approach differed fundamentally from the German Industry 4.0 model by emphasizing societal transformation beyond manufacturing efficiency. While Industry 4.0 focused primarily on smart factories and cyber-physical systems, Society 5.0 envisioned a comprehensive integration of digital technologies across all aspects of society to create what Keidanren later termed an “Imagination Society”. This broader vision included autonomous vehicles and drones serving depopulated areas, remote medical consultations, and flexible energy systems tailored to specific community needs.

European Formalization and Policy Development

The formal conceptualization of Industry 5.0 as a distinct industrial paradigm emerged from the European Commission’s research and innovation activities. In January 2021, the European Commission published a comprehensive 48-page white paper titled “Industry 5.0 – Towards a sustainable, human-centric and resilient European industry,” which officially coined the term and established its core principles. This document resulted from discussions held in two virtual workshops organized in July 2020, involving research and technology organizations and funding agencies across Europe.

The European Commission’s approach to Industry 5.0 represented a deliberate complement to, rather than replacement of, Industry 4.0. According to the Commission, Industry 5.0 “provides a vision of industry that aims beyond efficiency and productivity as the sole goals, and reinforces the role and the contribution of industry to society”. This formulation explicitly placed worker wellbeing at the center of production processes and emphasized using new technologies to provide prosperity beyond traditional economic metrics while respecting planetary boundaries.

Policy Integration and Strategic Objectives

The European conceptualization of Industry 5.0 was strategically aligned with three key Commission priorities: “An economy that works for people,” the “European Green Deal,” and “Europe fit for the digital age”. This integration demonstrates how Industry 5.0 emerged not merely as a technological concept but as a comprehensive policy framework addressing multiple societal challenges simultaneously. The approach emphasized adopting human-centric technologies, including artificial intelligence regulation, and focused on upskilling and reskilling European workers to prepare for industrial transformation.

The European Commission’s framework distinguished Industry 5.0 by its explicit focus on three core values: sustainability, human-centricity, and resilience. This represented a significant departure from Industry 4.0’s primary emphasis on efficiency and productivity, instead prioritizing environmental responsibility, worker welfare, and system robustness against external shocks such as the COVID-19 pandemic. The Commission argued that this approach would enable European industry to play an active role in addressing climate change, resource preservation, and social stability challenges.

Conceptual Evolution and Theoretical Development

From Automation to Human-Machine Collaboration

The evolution from Industry 4.0 to Industry 5.0 reflects a fundamental shift in thinking about the role of humans in automated production systems. While Industry 4.0 emphasized machine-to-machine communication, Internet of Things connectivity, and autonomous decision-making systems, Industry 5.0 reintroduced human creativity and collaboration as central elements. This shift emerged from practical experiences with Industry 4.0 implementation, which revealed limitations in purely automated approaches and highlighted the continued importance of human insight, creativity, and adaptability.

Industry 5.0 proponents argue that the concept represents an evolution rather than a revolution, building upon Industry 4.0’s technological foundation while addressing its human and environmental limitations. The focus shifted toward collaborative robots (cobots) that work alongside human operators, combining the precision and consistency of machines with human creativity and problem-solving capabilities. This approach recognizes that while automation can handle routine and predictable tasks effectively, complex problem-solving, innovation, and adaptation to unexpected situations remain distinctly human strengths.

Academic and Industry Perspectives

The academic and industry discourse around Industry 5.0 has emphasized its role as a corrective to what some viewed as Industry 4.0’s overly technology-centric approach. Scholars and practitioners have noted that Industry 4.0’s focus on digitalization and automation, while achieving significant efficiency gains, sometimes neglected human factors and societal impacts. Industry 5.0 emerged as a response to these concerns, advocating for a more balanced approach that leverages technology to enhance rather than replace human capabilities.

The concept has gained traction across various industries as organizations recognize the value of combining technological sophistication with human insight. This includes applications in personalized manufacturing, where human creativity guides AI systems to produce customized products, and in maintenance operations, where human expertise interprerets data analytics to make complex decisions about equipment management416. The approach acknowledges that successful industrial transformation requires not just technological advancement but also social acceptance and worker engagement.

Timeline and Key Milestones

The development of Industry 5.0 can be traced through several key phases, beginning with Japan’s internal policy deliberations from 2014 to 2016, followed by international exposure in 2017, and culminating in European formalization in 2021. The COVID-19 pandemic played a catalytic role in accelerating interest in Industry 5.0 principles, as organizations worldwide experienced the importance of resilience, human adaptability, and sustainable practices in maintaining operations during crisis conditions.

The period from 2017 to 2020 saw growing academic and industry discussion about the limitations of purely automated approaches and the need for more human-centric industrial models. This discourse was influenced by practical experiences with Industry 4.0 implementation, which revealed challenges in areas such as worker displacement, skill gaps, and environmental sustainability. The European Commission’s workshops in 2020 provided a formal venue for consolidating these concerns into a coherent policy framework.

Contemporary Developments and Future Trajectory

Since the European Commission’s formal introduction of Industry 5.0 in 2021, the concept has gained international recognition and adoption across various sectors. The approach has been particularly influential in discussions about sustainable manufacturing, worker welfare, and industrial resilience in the post-pandemic era. Organizations worldwide are beginning to implement Industry 5.0 principles, focusing on human-machine collaboration, environmental responsibility, and system robustness.

The concept continues to evolve as practitioners gain experience with its implementation and as new technologies enable more sophisticated forms of human-machine collaboration. Recent developments have emphasized the integration of artificial intelligence with human expertise, the application of circular economy principles in manufacturing, and the development of resilient supply chains capable of adapting to global disruptions. These developments suggest that Industry 5.0 will continue to influence industrial policy and practice as organizations seek to balance technological advancement with human and environmental considerations.

Evaluating Industry 5.0 Concepts

While I am naturally suspicious of version numbers on frameworks, and certainly exhausted by the Industry 4.0/Quality 4.0 advocates, the more I read about industry 5.0 the more the core concepts resonated with me. Industry 5.0 challenges manufacturers to reshape how they think about quality, people, and technology. And this resonates on what has always been the fundamental focus of this blog: robust Quality Units, data integrity, change control, and the organizational structures needed for true quality oversight.

Human-Centricity: From Oversight to Empowerment

Industry 5.0’s defining feature is its human-centric approach, aiming to put people back at the heart of manufacturing. This aligns closely with my focus on decision-making, oversight, and continuous improvement.

Collaboration Between Humans and Technology

I frequently address the pitfalls of siloed teams and the dangers of relying solely on either manual or automated systems for quality management. Industry 5.0’s vision of human-machine collaboration—where AI and automation support, but don’t replace, expert judgment—mirrors this blog’s call for integrated quality systems.

Proactive, Data-Driven Quality

To say that a central theme in my career has been how reactive, paper-based, or poorly integrated systems lead to data integrity issues and regulatory citations would be an understatement. Thus, I am fully aligned with the advocacy for proactive, real-time management utilizing AI, IoT, and advanced analytics. This continued shift from after-the-fact remediation to predictive, preventive action directly addresses the recurring compliance gaps we continue to struggle with. This blog’s focus on robust documentation, risk-based change control, and comprehensive batch review finds a natural ally in Industry 5.0’s data-driven, risk-based quality management systems.

Sustainability and Quality Culture

Another theme on this blog is the importance of management support and a culture of quality—elements that Industry 5.0 elevates by integrating sustainability and social responsibility into the definition of quality itself. Industry 5.0 is not just about defect prevention; it’s about minimizing waste, ensuring ethical sourcing, and considering the broader impact of manufacturing on people and the planet. This holistic view expands the blog’s advocacy for independent, well-resourced Quality Units to include environmental and social governance as core responsibilities. Something I perhaps do not center as much in my practice as I should.

Democratic Leadership

The principles of democratic leadership explored extensively on this blog provide a critical foundation for realizing the human-centric aspirations of Industry 5.0. Central to the my philosophy is decentralizing decision-making and fostering psychological safety—concepts that align directly with Industry 5.0’s emphasis on empowering workers through collaborative human-machine ecosystems. By advocating for leadership models that distribute authority to frontline employees and prioritize transparency, this blog’s framework mirrors Industry 5.0’s rejection of rigid hierarchies in favor of agile, worker-driven innovation. The emphasis on equanimity—maintaining composed, data-driven responses to quality challenges—resonates with Industry 5.0’s vision of resilient systems where human judgment guides AI and automation. This synergy is particularly evident in the my analysis of decentralized decision-making, which argues that empowering those closest to operational realities accelerates problem-solving while building ownership—a necessity for Industry 5.0’s adaptive production environments. The European Commission’s Industry 5.0 white paper explicitly calls for this shift from “shareholder to stakeholder value,” a transition achievable only through the democratic leadership practices championed in the blog’s critique of Taylorist management models. By merging technological advancement with human-centric governance, this blog’s advocacy for flattened hierarchies and worker agency provides a blueprint for implementing Industry 5.0’s ideals without sacrificing operational rigor.

Convergence and Opportunity

While I have more than a hint of skepticism about the term Industry 5.0, I acknowledge its reliance on the foundational principles that I consider crucial to quality management. By integrating robust organizational quality structures, empowered individuals, and advanced technology, manufacturers can transcend mere compliance to deliver sustainable, high-quality products in a rapidly evolving world. For quality professionals, the implication is clear: the future is not solely about increased automation or stricter oversight but about more intelligent, collaborative, and, importantly, human-centric quality management. This message resonates deeply with me, and it should with you as well, as it underscores the value and importance of our human contribution in this process.

Key Sources on Industry 5.0

Here is a curated list of foundational and authoritative sources for understanding Industry 5.0, including official reports, academic articles, and expert analyses that I found most helpful when evaluating the concept of Industry 5.0:

Beyond Documents: Embracing Data-Centric Thinking

We live in a fascinating inflection point in quality management, caught between traditional document-centric approaches and the emerging imperative for data-centricity needed to fully realize the potential of digital transformation. For several decades, we’ve been in a process that continues to accelerate through a technology transition that will deliver dramatic improvements in operations and quality. This transformation is driven by three interconnected trends: Pharma 4.0, the Rise of AI, and the shift from Documents to Data.

The History and Evolution of Documents in Quality Management

The history of document management can be traced back to the introduction of the file cabinet in the late 1800s, providing a structured way to organize paper records. Quality management systems have even deeper roots, extending back to medieval Europe when craftsman guilds developed strict guidelines for product inspection. These early approaches established the document as the fundamental unit of quality management—a paradigm that persisted through industrialization and into the modern era.

The document landscape took a dramatic turn in the 1980s with the increasing availability of computer technology. The development of servers allowed organizations to store documents electronically in centralized mainframes, marking the beginning of electronic document management systems (eDMS). Meanwhile, scanners enabled conversion of paper documents to digital format, and the rise of personal computers gave businesses the ability to create and store documents directly in digital form.

In traditional quality systems, documents serve as the backbone of quality operations and fall into three primary categories: functional documents (providing instructions), records (providing evidence), and reports (providing specific information). This document trinity has established our fundamental conception of what a quality system is and how it operates—a conception deeply influenced by the physical limitations of paper.

Photo by Andrea Piacquadio on Pexels.com

Breaking the Paper Paradigm: Limitations of Document-Centric Thinking

The Paper-on-Glass Dilemma

The maturation path for quality systems typically progresses mainly from paper execution to paper-on-glass to end-to-end integration and execution. However, most life sciences organizations remain stuck in the paper-on-glass phase of their digital evolution. They still rely on the paper-on-glass data capture method, where digital records are generated that closely resemble the structure and layout of a paper-based workflow. In general, the wider industry is still reluctant to transition away from paper-like records out of process familiarity and uncertainty of regulatory scrutiny.

Paper-on-glass systems present several specific limitations that hamper digital transformation:

  1. Constrained design flexibility: Data capture is limited by the digital record’s design, which often mimics previous paper formats rather than leveraging digital capabilities. A pharmaceutical batch record system that meticulously replicates its paper predecessor inherently limits the system’s ability to analyze data across batches or integrate with other quality processes.
  2. Manual data extraction requirements: When data is trapped in digital documents structured like paper forms, it remains difficult to extract. This means data from paper-on-glass records typically requires manual intervention, substantially reducing data utilization effectiveness.
  3. Elevated error rates: Many paper-on-glass implementations lack sufficient logic and controls to prevent avoidable data capture errors that would be eliminated in truly digital systems. Without data validation rules built into the capture process, quality systems continue to allow errors that must be caught through manual review.
  4. Unnecessary artifacts: These approaches generate records with inflated sizes and unnecessary elements, such as cover pages that serve no functional purpose in a digital environment but persist because they were needed in paper systems.
  5. Cumbersome validation: Content must be fully controlled and managed manually, with none of the advantages gained from data-centric validation approaches.

Broader Digital Transformation Struggles

Pharmaceutical and medical device companies must navigate complex regulatory requirements while implementing new digital systems, leading to stalling initiatives. Regulatory agencies have historically relied on document-based submissions and evidence, reinforcing document-centric mindsets even as technology evolves.

Beyond Paper-on-Glass: What Comes Next?

What comes after paper-on-glass? The natural evolution leads to end-to-end integration and execution systems that transcend document limitations and focus on data as the primary asset. This evolution isn’t merely about eliminating paper—it’s about reconceptualizing how we think about the information that drives quality management.

In fully integrated execution systems, functional documents and records become unified. Instead of having separate systems for managing SOPs and for capturing execution data, these systems bring process definitions and execution together. This approach drives up reliability and drives out error, but requires fundamentally different thinking about how we structure information.

A prime example of moving beyond paper-on-glass can be seen in advanced Manufacturing Execution Systems (MES) for pharmaceutical production. Rather than simply digitizing batch records, modern MES platforms incorporate AI, IIoT, and Pharma 4.0 principles to provide the right data, at the right time, to the right team. These systems deliver meaningful and actionable information, moving from merely connecting devices to optimizing manufacturing and quality processes.

AI-Powered Documentation: Breaking Through with Intelligent Systems

A dramatic example of breaking free from document constraints comes from Novo Nordisk’s use of AI to draft clinical study reports. The company has taken a leap forward in pharmaceutical documentation, putting AI to work where human writers once toiled for weeks. The Danish pharmaceutical company is using Claude, an AI model by Anthropic, to draft clinical study reports—documents that can stretch hundreds of pages.

This represents a fundamental shift in how we think about documents. Rather than having humans arrange data into documents manually, we can now use AI to generate high-quality documents directly from structured data sources. The document becomes an output—a view of the underlying data—rather than the primary artifact of the quality system.

Data Requirements: The Foundation of Modern Quality Systems in Life Sciences

Shifting from document-centric to data-centric thinking requires understanding that documents are merely vessels for data—and it’s the data that delivers value. When we focus on data requirements instead of document types, we unlock new possibilities for quality management in regulated environments.

At its core, any quality process is a way to realize a set of requirements. These requirements come from external sources (regulations, standards) and internal needs (efficiency, business objectives). Meeting these requirements involves integrating people, procedures, principles, and technology. By focusing on the underlying data requirements rather than the documents that traditionally housed them, life sciences organizations can create more flexible, responsive quality systems.

ICH Q9(R1) emphasizes that knowledge is fundamental to effective risk management, stating that “QRM is part of building knowledge and understanding risk scenarios, so that appropriate risk control can be decided upon for use during the commercial manufacturing phase.” We need to recognize the inverse relationship between knowledge and uncertainty in risk assessment. As ICH Q9(R1) notes, uncertainty may be reduced “via effective knowledge management, which enables accumulated and new information (both internal and external) to be used to support risk-based decisions throughout the product lifecycle.”

This approach helps us ensure that our tools take into account that our processes are living and breathing, our tools should take that into account. This is all about moving to a process repository and away from a document mindset.

Documents as Data Views: Transforming Quality System Architecture

When we shift our paradigm to view documents as outputs of data rather than primary artifacts, we fundamentally transform how quality systems operate. This perspective enables a more dynamic, interconnected approach to quality management that transcends the limitations of traditional document-centric systems.

Breaking the Document-Data Paradigm

Traditionally, life sciences organizations have thought of documents as containers that hold data. This subtle but profound perspective has shaped how we design quality systems, leading to siloed applications and fragmented information. When we invert this relationship—seeing data as the foundation and documents as configurable views of that data—we unlock powerful capabilities that better serve the needs of modern life sciences organizations.

The Benefits of Data-First, Document-Second Architecture

When documents become outputs—dynamic views of underlying data—rather than the primary focus of quality systems, several transformative benefits emerge.

First, data becomes reusable across multiple contexts. The same underlying data can generate different documents for different audiences or purposes without duplication or inconsistency. For example, clinical trial data might generate regulatory submission documents, internal analysis reports, and patient communications—all from a single source of truth.

Second, changes to data automatically propagate to all relevant documents. In a document-first system, updating information requires manually changing each affected document, creating opportunities for errors and inconsistencies. In a data-first system, updating the central data repository automatically refreshes all document views, ensuring consistency across the quality ecosystem.

Third, this approach enables more sophisticated analytics and insights. When data exists independently of documents, it can be more easily aggregated, analyzed, and visualized across processes.

In this architecture, quality management systems must be designed with robust data models at their core, with document generation capabilities built on top. This might include:

  1. A unified data layer that captures all quality-related information
  2. Flexible document templates that can be populated with data from this layer
  3. Dynamic relationships between data entities that reflect real-world connections between quality processes
  4. Powerful query capabilities that enable users to create custom views of data based on specific needs

The resulting system treats documents as what they truly are: snapshots of data formatted for human consumption at specific moments in time, rather than the authoritative system of record.

Electronic Quality Management Systems (eQMS): Beyond Paper-on-Glass

Electronic Quality Management Systems have been adopted widely across life sciences, but many implementations fail to realize their full potential due to document-centric thinking. When implementing an eQMS, organizations often attempt to replicate their existing document-based processes in digital form rather than reconceptualizing their approach around data.

Current Limitations of eQMS Implementations

Document-centric eQMS systems treat functional documents as discrete objects, much as they were conceived decades ago. They still think it terms of SOPs being discrete documents. They structure workflows, such as non-conformances, CAPAs, change controls, and design controls, with artificial gaps between these interconnected processes. When a manufacturing non-conformance impacts a design control, which then requires a change control, the connections between these events often remain manual and error-prone.

This approach leads to compartmentalized technology solutions. Organizations believe they can solve quality challenges through single applications: an eQMS will solve problems in quality events, a LIMS for the lab, an MES for manufacturing. These isolated systems may digitize documents but fail to integrate the underlying data.

Data-Centric eQMS Approaches

We are in the process of reimagining eQMS as data platforms rather than document repositories. A data-centric eQMS connects quality events, training records, change controls, and other quality processes through a unified data model. This approach enables more effective risk management, root cause analysis, and continuous improvement.

For instance, when a deviation is recorded in a data-centric system, it automatically connects to relevant product specifications, equipment records, training data, and previous similar events. This comprehensive view enables more effective investigation and corrective action than reviewing isolated documents.

Looking ahead, AI-powered eQMS solutions will increasingly incorporate predictive analytics to identify potential quality issues before they occur. By analyzing patterns in historical quality data, these systems can alert quality teams to emerging risks and recommend preventive actions.

Manufacturing Execution Systems (MES): Breaking Down Production Data Silos

Manufacturing Execution Systems face similar challenges in breaking away from document-centric paradigms. Common MES implementation challenges highlight the limitations of traditional approaches and the potential benefits of data-centric thinking.

MES in the Pharmaceutical Industry

Manufacturing Execution Systems (MES) aggregate a number of the technologies deployed at the MOM level. MES as a technology has been successfully deployed within the pharmaceutical industry and the technology associated with MES has matured positively and is fast becoming a recognized best practice across all life science regulated industries. This is borne out by the fact that green-field manufacturing sites are starting with an MES in place—paperless manufacturing from day one.

The amount of IT applied to an MES project is dependent on business needs. At a minimum, an MES should strive to replace paper batch records with an Electronic Batch Record (EBR). Other functionality that can be applied includes automated material weighing and dispensing, and integration to ERP systems; therefore, helping the optimization of inventory levels and production planning.

Beyond Paper-on-Glass in Manufacturing

In pharmaceutical manufacturing, paper batch records have traditionally documented each step of the production process. Early electronic batch record systems simply digitized these paper forms, creating “paper-on-glass” implementations that failed to leverage the full potential of digital technology.

Advanced Manufacturing Execution Systems are moving beyond this limitation by focusing on data rather than documents. Rather than digitizing batch records, these systems capture manufacturing data directly, using sensors, automated equipment, and operator inputs. This approach enables real-time monitoring, statistical process control, and predictive quality management.

An example of a modern MES solution fully compliant with Pharma 4.0 principles is the Tempo platform developed by Apprentice. It is a complete manufacturing system designed for life sciences companies that leverages cloud technology to provide real-time visibility and control over production processes. The platform combines MES, EBR, LES (Laboratory Execution System), and AR (Augmented Reality) capabilities to create a comprehensive solution that supports complex manufacturing workflows.

Electronic Validation Management Systems (eVMS): Transforming Validation Practices

Validation represents a critical intersection of quality management and compliance in life sciences. The transition from document-centric to data-centric approaches is particularly challenging—and potentially rewarding—in this domain.

Current Validation Challenges

Traditional validation approaches face several limitations that highlight the problems with document-centric thinking:

  1. Integration Issues: Many Digital Validation Tools (DVTs) remain isolated from Enterprise Document Management Systems (eDMS). The eDMS system is typically the first step where vendor engineering data is imported into a client system. However, this data is rarely validated once—typically departments repeat this validation step multiple times, creating unnecessary duplication.
  2. Validation for AI Systems: Traditional validation approaches are inadequate for AI-enabled systems. Traditional validation processes are geared towards demonstrating that products and processes will always achieve expected results. However, in the digital “intellectual” eQMS world, organizations will, at some point, experience the unexpected.
  3. Continuous Compliance: A significant challenge is remaining in compliance continuously during any digital eQMS-initiated change because digital systems can update frequently and quickly. This rapid pace of change conflicts with traditional validation approaches that assume relative stability in systems once validated.

Data-Centric Validation Solutions

Modern electronic Validation Management Systems (eVMS) solutions exemplify the shift toward data-centric validation management. These platforms introduce AI capabilities that provide intelligent insights across validation activities to unlock unprecedented operational efficiency. Their risk-based approach promotes critical thinking, automates assurance activities, and fosters deeper regulatory alignment.

We need to strive to leverage the digitization and automation of pharmaceutical manufacturing to link real-time data with both the quality risk management system and control strategies. This connection enables continuous visibility into whether processes are in a state of control.

The 11 Axes of Quality 4.0

LNS Research has identified 11 key components or “axes” of the Quality 4.0 framework that organizations must understand to successfully implement modern quality management:

  1. Data: In the quality sphere, data has always been vital for improvement. However, most organizations still face lags in data collection, analysis, and decision-making processes. Quality 4.0 focuses on rapid, structured collection of data from various sources to enable informed and agile decision-making.
  2. Analytics: Traditional quality metrics are primarily descriptive. Quality 4.0 enhances these with predictive and prescriptive analytics that can anticipate quality issues before they occur and recommend optimal actions.
  3. Connectivity: Quality 4.0 emphasizes the connection between operating technology (OT) used in manufacturing environments and information technology (IT) systems including ERP, eQMS, and PLM. This connectivity enables real-time feedback loops that enhance quality processes.
  4. Collaboration: Breaking down silos between departments is essential for Quality 4.0. This requires not just technological integration but cultural changes that foster teamwork and shared quality ownership.
  5. App Development: Quality 4.0 leverages modern application development approaches, including cloud platforms, microservices, and low/no-code solutions to rapidly deploy and update quality applications.
  6. Scalability: Modern quality systems must scale efficiently across global operations while maintaining consistency and compliance.
  7. Management Systems: Quality 4.0 integrates with broader management systems to ensure quality is embedded throughout the organization.
  8. Compliance: While traditional quality focused on meeting minimum requirements, Quality 4.0 takes a risk-based approach to compliance that is more proactive and efficient.
  9. Culture: Quality 4.0 requires a cultural shift that embraces digital transformation, continuous improvement, and data-driven decision-making.
  10. Leadership: Executive support and vision are critical for successful Quality 4.0 implementation.
  11. Competency: New skills and capabilities are needed for Quality 4.0, requiring significant investment in training and workforce development.

The Future of Quality Management in Life Sciences

The evolution from document-centric to data-centric quality management represents a fundamental shift in how life sciences organizations approach quality. While documents will continue to play a role, their purpose and primacy are changing in an increasingly data-driven world.

By focusing on data requirements rather than document types, organizations can build more flexible, responsive, and effective quality systems that truly deliver on the promise of digital transformation. This approach enables life sciences companies to maintain compliance while improving efficiency, enhancing product quality, and ultimately delivering better outcomes for patients.

The journey from documents to data is not merely a technical transition but a strategic evolution that will define quality management for decades to come. As AI, machine learning, and process automation converge with quality management, the organizations that successfully embrace data-centricity will gain significant competitive advantages through improved agility, deeper insights, and more effective compliance in an increasingly complex regulatory landscape.

The paper may go, but the document—reimagined as structured data that enables insight and action—will continue to serve as the foundation of effective quality management. The key is recognizing that documents are vessels for data, and it’s the data that drives value in the organization.

Stop it with the 4.0 stuff

Industry 4.0, Quality 4.0, Validaiton 4.0. It is all absurd, so cut it out. Old man rant out.

Seriously though, let’s have a chat about this and why it is a bad practice.

When we put a number after something, we denote a version number. Version numbers have meaning, and individuals react to them in a certain way.

Understanding Version Numbers

A version number is a unique identifier assigned to specific releases of software, hardware, firmware, or drivers. It helps developers and users track changes, improvements, and updates in the product over time. Version numbers are crucial for maintaining software, ensuring compatibility, and managing updates effectively.

Structure of Version Numbers

Version numbers typically follow a structured format, often in the form of major.minor.patch or major.minor.patch.build. Each segment of the version number conveys specific information about the changes made in that release.

Major Version

  • Indicates: Significant changes or overhauls.
  • Example: Going from version 1.0.0 to 2.0.0 might indicate a complete redesign or the addition of major new features.
  • Impact: These changes might not be backward compatible with previous versions.

Minor Version

  • Indicates: Addition of new features or significant improvements that are backward compatible.
  • Example: Updating from version 2.1.0 to 2.2.0 could mean new functionalities were added without altering existing ones.
  • Impact: Users can expect enhancements without losing compatibility with previous minor versions.

Patch Version

  • Indicates: Bug fixes and minor improvements.
  • Example: Moving from version 2.2.1 to 2.2.2 might mean minor bugs were fixed.
  • Impact: These updates are usually safe and recommended as they resolve issues without changing functionality.

Build Number

  • Indicates: Specific builds or iterations, often used internally.
  • Example: Version 2.2.2.45 could indicate the 45th build of this particular version.
  • Impact: Helps in identifying specific builds, useful for debugging and internal tracking.

Semantic Versioning

One of the most widely adopted systems for versioning is Semantic Versioning (SemVer). It uses a three-part version number: major.minor.patch. This system provides a clear and standardized way to communicate the nature of changes in each release.

  • Major: Incompatible API changes.
  • Minor: Backward-compatible functionality added.
  • Patch: Backward-compatible bug fixes.

Importance of Version Numbers

  1. Tracking Changes: Helps developers and users keep track of what changes have been made and when.
  2. Compatibility: Ensures that users know whether new versions will work with their current setup.
  3. Support and Maintenance: Facilitates efficient troubleshooting and support by identifying the exact version in use.
  4. Update Management: Allows users to determine if they need to update their software to the latest version.

Why I Dislike Quality 4.0, Validation 4.0, and the Like

It is meant to denote a major version, but it’s not, for a lot of reasons:

  1. These concepts are more growth of design boxes than major changes. To use version control lingo, there is a lot of backward compatibility.
  2. They are not definitive. There are absolutes and best practices and onward progression.
  3. Each company tends to be in different places in different ways, and there are many maturity scales, not just one.

Maturity models are a better option. Each of these buckets has multiple scales, each of which needs to be evaluated and improved.

This is why I like cGMP

The “c” in cGMP stands for “current,” which signifies that the Good Manufacturing Practices (GMP) being referred to are up-to-date with the latest standards and technologies. This differentiation emphasizes that companies must use the most recent and advanced technologies and systems to comply with the regulations set forth by the FDA. The term cGMP ensures that manufacturing practices are not only good but also current, reflecting ongoing improvements and updates in the industry.

The Role of Mixed Reality in Quality 4.0

Last night I had the honor to speak at the ASQ Boston Section monthly meeting on some of the exciting work Thermo Fisher Scientific is doing in mixed reality and how it fits into the industrial transformation that we are all taking stabs at, as well as the broader concept of Quality 4.0.

A small group, but it was really fun to discuss some of the stuff I’ve gotten involved with in the 5 months I’ve been here, and where we see it going.

Slides are available here.

AI/ML-Based SaMD Framework

The US Food and Drug Administration’s proposed regulatory framework for artificial intelligence- (AI) and machine learning- (ML) based software as a medical device (SaMD) is fascinating in what it exposes about the uncertainty around the near-term future of a lot of industry 4.0 initiatives in pharmaceuticals and medical devices.

While focused on medical devices, this proposal is interesting read for folks interested in applying machine learning and artificial intelligence to other regulated areas, such as manufacturing.

We are seeing is the early stages of consensus building around the concept of Good Machine Learning Practices (GMLP), the idea of applying quality system practices to the unique challenges of machine learning.