Anne Greene, PhD, is a professor at Technological University Dublin and director at Pharmaceutical Regulatory Science Team.

Anne Greene

The current International Council for Harmonisation (ICH) Q9(R1) 

 (QRM) guideline is a targeted revision of the initial guidance issued in 2005, intended to address subjectivity, formality, risk-based decision-making, and other specific areas for improvement. While not a formally defined area for improvement, the revised guidance invokes many new references to “knowledge” and “knowledge management (KM)” in supporting improvement in QRM practice and, by extension, to the goals of an effective pharmaceutical quality system. 

This article was peer reviewed by a member of 

Submitted: April 24, 2023
Accepted: June 14, 2023

Recognizing that QRM and risk-based decision-making (RBDM) both depend on knowledge to be effective, it could be suggested that knowledge is the currency of managing risk. This paper presents research and perspectives on the opportunity to better connect risk and knowledge through improved integration of QRM and KM, including presenting several potential benefits of such integration. A framework is proposed, the 

), which could be used to improve the connectivity of risk and knowledge along with preliminary and practical recommendations to operationalize the 

. In addition, the paper invokes concepts of systems thinking in connecting QRM and KM as well as the opportunity to consider the role of knowledge and KM more holistically across the pharmaceutical business through application of a process classification framework.

There has been a bolus of activity associated with ICH Q9(R1) (1) finalized in January 2023, as the long-awaited update to the 2005 guidance, ICH Q9 

 (2). This activity included scrutiny of the concept paper (3), comments on the draft revision, and revision of the guideline itself. In addition, there has been a focus on the development of supporting training material by the ICH Expert Working Group (EWG) and to interpreting what it all means to industry and to organizations on an individual level as the deployment and adoption process begins.

Concurrent to this activity surrounding ICH Q9(R1), the Pharmaceutical Regulatory Science Team (PRST) at Technological University Dublin have continued their ongoing research into related topics, including QRM, KM, and RBDM. In particular, the PRST are exploring the relationships and interconnectivity between these practices, applying through “systems thinking” mindset and aspects of complexity theory where appropriate, which provide a more holistic approach to analysis and how the parts of a system interact over time (4).

One of the key areas of focus for the PRST is regarding the practice of KM, including:

The role of KM as a pharmaceutical quality system (PQS) enabler as defined by ICH Q10, 

The relationship between QRM and KM as dual enablers of the PQS

The role of KM in informing risk-based decision making.

The purpose of this paper is to compile and synthesize this KM-related research describing how it supports the intent of the ICH Q9(R1) revision and, ultimately, the objectives of the PQS itself as outlined by ICH Q10 

 (5), which are to achieve product realization, establish and maintain a state of control, and facilitate continual improvement.

To start with, however, it is important to demystify the relationship between the terms “knowledge” and KM. Authoritative definitions are presented in the next section of this paper, and the authors have discussed these concepts in more detail in a 2022 paper (6). Consider the following pragmatic descriptions by the authors.

In a pharmaceutical context, “knowledge” is an asset that includes what is collectively known (e.g., scientific understanding and insight) about products, processes, and platforms (e.g., technology transfer documents, regulatory filings, process experience, etc.) inclusive of both documented (explicit) knowledge and know-how, experience, and other tacit knowledge. To further break it down:

“Explicit knowledge” is “knowledge” that can be expressed in words, numbers, and symbols and can be stored (e.g., in books, computers, etc.). Explicit knowledge can be articulated and easily communicated between individuals and organizations (7).

“Tacit knowledge” refers to “knowledge” that resides in the minds of individuals and is surfaced in response to a situation or action (8). Tacit knowledge is often referred to as “know-how.”

Whereas, KM is a set of practices and associated mindsets and behaviors that determine how knowledge is managed (e.g., captured and made available in the future) in an intentional, repeatable, and systematic manner.Common KM practices include content management processes, lessons learned, knowledge transfer activities, among others. KM as a discipline is most successful when certain enabling practices are in place, such as standard processes, dedicated roles (e.g., community stewards, lessons learned facilitators), communications, and training (9) as well as foundational practices of effective data management (e.g., data strategy, standards, and stewardship).

Essentially, if “knowledge” is considered a noun, then “KM” can be considered a verb. This is perhaps a seemingly obvious but important distinction—as in the authors’ experience—these terms of “knowledge” and “KM” are often used loosely, almost interchangeably. It is crucial that the reader is mindful of the difference and—as the authors will make the case—knowledge is the currency of managing risk, and the smart investment is to ensure that the best possible knowledge is available when and where it is needed—via KM—to inform QRM and risk-based decision making, and to support the PQS objectives.

The foundational role of knowledge and KM for risk management, risk-based decision-making, and pharmaceutical quality

When one looks holistically across the ICH quality guidelines (10), the expectations for managing knowledge are implied through the repeated references to the availability and application of knowledge in support of risk management, science and risk-based decision-making, product and process understanding, and, ultimately, ensuring availability of a safe and efficacious product to the patient. While these references are not each overtly stated as “knowledge” or “knowledge management,” they do repeatedly invoke the availability and application of “knowledge” under the guise of many synonyms, including prior knowledge, platform knowledge, scientific knowledge, science, evidence, product knowledge, process knowledge, experience, product development history, expertise, know-how, product understanding, process understanding, and lessons learned (6).

Knowledge and KM across ICH and other regulatory guidance.

 While ICH Q10 does not define the term “knowledge,” the 2018 International Organization for Standardization (ISO) standard on KM, 

ISO 30401:2018, Knowledge Management Systems—Requirements

 provides a helpful definition of knowledge as:

“Human or organizational asset enabling effective decisions and action in context” (11).

The ISO definition is further clarified as follows:

“Knowledge can be individual, collective, or organizational.

“There are diverse views on the scope covered within knowledge, based on context and purpose. The definition above is general as to the various perspectives. Examples of knowledge include insights and know-how.

“Knowledge is acquired through learning or experience” (11).

Regarding the term “knowledge management,” ICH Q10 introduced the concept of KM and provided a definition in 2008 as:

“Knowledge management is a systematic approach to acquiring, analysing, storing and disseminating information related to products, manufacturing processes and components”

It further stated that product and process knowledge should be managed throughout the product lifecycle and includes examples of sources of knowledge:

“Sources of knowledge include, but are not limited to, prior knowledge (public domain or internally documented); pharmaceutical development studies; technology transfer activities; process validation studies over the product lifecycle; manufacturing experience; innovation; continual improvement; and change management activities”

It is in ICH Q10 that QRM and KM were linked as the dual enablers for an effective PQS by “providing the means for science- and risk-based decisions related to product quality” (5). It is worth noting that while ICH Q10 provided a definition of KM, it does this without any references to KM in the literature, or by discussing KM guidance in other industries.

In addition to ICH Q10, there are many other direct and indirect references to knowledge and KM across ICH quality guidelines, including ICH Q8(R2), ICH Q8/Q9/Q10 Questions & Answers, ICH Q11, and ICH Q12, as well as in other regulatory guidance issued by the World Health Organization (WHO), EudraLex in the European Union, and Pharmaceutical Inspection Co-operation Scheme (PIC/S), which have previously been reviewed in detail by the authors in 2020 (12).Furthermore, since the 2020 review, references to the role of knowledge and KM are also included in ICH Q13, 

Continuous Processing of Drug Substances and Drug Products

 (14), and further in FDA’s pharmaceutical quality initiative, known as knowledge-aided assessment and structured applications (KASA) (15).

Considering regulatory guidance taken in aggregate, while it is ICH Q10 that defined KM in 2008, it is heartening to see the concepts of knowledge and KM emerging and repeatedly reinforced in the interceding 15 years. Indeed, a closer look at the recent ICH Q9(R1) extends this theme and provides an opportunity for risk and knowledge to be intentionally connected.

A closer look: the emergence of knowledge management in ICH Q9(R1).

 The concept paper for ICH Q9(R1) (3) identified four primary areas for improvement in the practice of QRM, for both pharma companies and regulatory authorities as follows:

High levels of subjectivity in risk assessments and in QRM outputs

Lack of understanding as to what constitutes formality in QRM

Lack of clarity and understanding on risk-based decision-making.

Furthermore, two additional points were raised in the concept paper to be addressed, namely:

Emphasis on “hazard identification” (rather than “risk identification”).

Collectively these six areas of focus are referred to as the “revision topics.”

While knowledge-related or KM-related areas for improvement were not identified in the concept paper directly (i.e., as one of the six revision topics), a closer look at the revised ICH Q9(R1) guideline reveals that the concepts of both “knowledge” and “knowledge management” have a significant presence. This is demonstrated by an evaluation carried out by the authors, presented in 

 as a summary of the frequency of the use of each team in the original ICH Q9 guideline, and in the revised ICH Q9(R1) guideline. 

 Frequency of “knowledge” and “knowledge management” in ICH Q9(R1).

While the term “knowledge management” is not as prevalent as “knowledge” there is a significant change in the recognition of the centrality and importance of knowledge to support QRM. This is welcomed by the authors, given the nature of ICH Q9(R1) as a “targeted revision” with “limited and specific adjustments” (1).

In fact, a review of the six revision topics could indeed suggest a dependency of each of these six revision topics on knowledge and/or knowledge management, as summarized in 

 Mapping “knowledge” and “knowledge management” (KM) references to ICH Q9(R1) revision topics. QRM is quality risk management. PQS is pharmaceutical quality system.

Knowledge as an invaluable commodity for risk management and decision making.

 A further literature review by the authors in 2020 (12,16) explored the relationship between knowledge and risk—and knowledge management and risk management—in pharmaceutical regulatory guidance and in other industries and organizations, including the National Aeronautics and Space Administration (NASA). A key assertion resulting from this review is that knowledge is both an input to and an output from QRM, thus highlighting the opportunity for QRM and KM to be thoughtfully and intentionally connected.

Arguably, the authors suggest that emergence of the importance of knowledge and KM in ICH Q9(R1) is a logical evolution of the recognition of the foundational role each play in informing risk and improving risk-based decision-making, as is evident through a holistic review of the document as shown in 

At the end of the day, the pharmaceutical industry is a knowledge industry (17) and, on a daily basis, formally and informally assesses risk and makes countless decisions—each of which should seek to ensure quality products and to protect the patient. Knowledge is an indispensable commodity necessary to make this happen effectively. With greater knowledge comes greater understanding. Greater understanding leads to reduced uncertainty. Reduced uncertainty in turn informs risk management activities and ultimately risk-based decision-making. Knowledge, indeed, is the currency for managing risk—and managing such knowledge is a smart investment for benefitting patients.

Exploring the practical connection between knowledge and risk

The authors, having recognized the disconnect between QRM and KM in practice, turned to systems-thinking principles to explore the connections between risk and knowledge, and between QRM and KM, starting with an assessment of practice at the time.

Current state connectivity of quality risk management and knowledge management. 

In 2021, the authors collected data through a survey (18), which reported that, while 96% of survey respondents believe QRM and KM are highly interdependent as concepts supporting the PQS (i.e., how connected should they be), no one believes they are fully integrated in practice today (0%), with 4% responding they are intentionally but not optimally connected, and 85% reporting they are only partially connected. These data are summarized in 

ALL FIGURES ARE COURTESY OF THE AUTHORS. 

 Exploring the integration of quality risk management (QRM) and knowledge management (KM).

Furthermore, when asked about the potential benefits of improved QRM–KM integration, a variety of important benefits associated with better connectivity between risk and knowledge were identified, including:

Better risk-based decisions where decisions are informed by risk and knowledge

More data/knowledge-driven risk assessments

Increased ability to leverage off of prior knowledge

Improved control strategies that better reflect risk and knowledge

Improved PQS effectiveness, better supporting decision making, validation, change management, outsourcing, etc.

Improved protection and value for patients through reduced risk of defects, drug shortages, etc.

Notably, these anticipated benefits reflect the role of knowledge as a commodity—or currency—in delivering improved risk assessments, control strategies, decision making, and quality outcomes.

 coupled with anticipated benefits identified by survey responses clearly suggest that QRM and KM were not well-connected, nor as connected as they should be, and that important benefits are possible if QRM and KM are better integrated in the future. The authors are hopeful that this connection between risk and knowledge will be more prevalent with the adoption of ICH Q9(R1) which clearly recognizes the role knowledge plays in quality risk management and risk-based decision-making.

A framework to connect risk and knowledge.

 As one potential means to address this need to connect risk and knowledge, a framework was developed by the authors in partnership with Kevin O’Donnell, PhD, market compliance manager, Health Products Regulatory Authority. This framework, the 

 (19), was proposed based on the previously referenced literature review of pharmaceutical regulatory guidance, input from industry and regulatory agency experts, practices in other industries (e.g., aerospace), and fundamental principles of knowledge management (e.g., enabling knowledge to “flow”). 

, highlighting the relationship between risk and knowledge, and underlying key concepts reinforced throughout regulatory guidance.

 as applied to ICH Q10, with proposed guiding principles to connect QRM and KM as a means to operationalize the integration of risk and knowledge.

 over the product lifecycle, illustrating that knowledge increases over time, providing the opportunity to apply this to increase understanding, decrease uncertainty, and, ultimately, reduce risk. 

. QRM is quality risk management. KM is knowledge management.

 and supporting concepts and principles, the following sections will explore its application and contribution to ICH Q9(R1).

 can be applied in different ways to help address the disconnect between risk and knowledge. In the simplest manner, the 

 can readily be applied as a conceptual model for organizations to reflect on whether risk and knowledge are intentionally connected via a connectivity between an organization’s QRM and KM practices. The six guiding principles (

) provide “target conditions” throughout the cycle, which can be qualitatively assessed for alignment of an organization’s practices.

 can also be applied as a detailed process model to operationalize the 

 via deeper assessment of QRM and KM practices and their interdependency, resulting in the identification of gaps and recommended deployment actions to better connect QRM and KM, and thus risk and knowledge. Efforts by the authors are ongoing to develop detailed 

 operationalization guidance. Several preliminary recommendations include the following:

Create a “knowledge map” for QRM (node 1 on 

 A knowledge map is a KM practice used to understand the knowledge necessary for a given activity, in this case, a quality risk assessment (or risk review). An illustrative case study of a knowledge map for the installation of an autoclave has been documented (20), for which the industry participants provided overwhelmingly positive feedback on the knowledge-mapping experience, with 97% of the attendees agreeing that the knowledge mapping exercise helps them think more expansively about knowledge needed for QRM (inclusive of know-how, expertise, and other tacit knowledge). The insights from knowledge maps can be used as requirements to inform KM practices (or improve current practices).

Recognize and manage the knowledge outputs from QRM (node 3 on 

 In addition to the routine outputs of a quality risk assessment (e.g., results of risk ranking) is sufficient tacit knowledge captured to improve future risk assessment and review activities, such as decisions and supporting rationale, alternatives considered, what was known at the time? Other knowledge outputs might include important experiential knowledge surfaced by subject-matter experts that should be codified for broader dissemination, gaps in control strategies, knowledge to inform supply chain, and other types of risks, gaps in knowledge where specific knowledge must be acquired, etc. Organizations can take steps to define expectations for where and how this knowledge is managed, ideally for use in the future (e.g., risk review) or across similar risk assessments.

Inventory existing KM practices (nodes 4, 5, and 6 on 

 Regardless of whether an organization has a formal KM program, understand what KM or “KM-like” practices exist in the categories of content management, search, expertise location, communities of practice, after action review/lessons learned, tacit knowledge transfer, and critical knowledge retention. These KM practices should be catalogued and made visible broadly across the organization. A more comprehensive (and complex) opportunity would be to perform a KM capability assessment (21) to understand an organization’s relative KM maturity and potential actions to improve.

Conduct KM awareness training for QRM practitioners (nodes 1, 2, and 3 on 

 Build KM competency for QRM practitioners via basic training that includes introductory KM concepts (e.g., definitions, principles), mindsets and behaviors, and awareness of existing KM practices at an organization. In the opinion of the authors, the more awareness QRM practitioners have about KM and the opportunity for KM to support QRM, the greater the opportunity to recognize challenges and opportunities, and create a “pull” from QRM to improve KM—and from KM to improve QRM.

Develop and deploy simple KM practices (nodes 4, 5, and 6 on 

 These practices should be based on known challenges and/or insights from QRM knowledge mapping (i.e., the first recommendation on this list). Such practices could be simple steps to provide guidance on where content should be stored (and not stored) and create lists of subject matter experts and decisions or could be more involved, such as creating communities of practice or deploying search tools.

The role of knowledge in enhancing realization of ICH Q9(R1).

 Considering the six revision topics of ICH Q9(R1) and how each invokes a dependency on knowledge availability and application (

), ICH Q9(R1) further reinforces this relationship between knowledge and risk. It is perhaps obvious to say each of the six topics—subjectivity, product availability risks, formality, risk-based decision-making, risk review, and hazard identification—would each benefit by having the “maximum and collective knowledge of the organization available on demand,” as is the intent of the 

The authors anticipate further evolution of KM practices (and, therefore, improved knowledge access and availability) can benefit several of these targeted revision topics.For example:

 Consider the use of a “hazard library” (or “risk bank”) to identify common hazards for a given risk scenario. Such lists could reduce subjectivity by minimizing variability across different teams of experts in identifying common hazards. Furthermore, other KM processes such as lessons learned could funnel new hazards into such standardized lists.

 In addition to such a “hazard library,” processes such as directed recognition (22), aim to reduce “unknown-unknowns.” Many of the processes, such as those described in the referenced article, focused on reducing unwelcome surprises (e.g., checklists and interviews) and are, in fact, KM-like processes to surface, connect, and transfer tacit knowledge.

 Research into risk-based decision-making in the pharmaceutical industry has identified 21 characteristics of risk-based decision-making in high-reliability organizations (e.g., aerospace, nuclear) (23). Many of these characteristics—eight in total—have a dependency on KM practices, including concepts of data and knowledge sources, data and knowledge availability, and taxonomy.

 In addition to the recommendations above involving knowledge capture from QRA to inform future risk review, consider the use of “risk registers” as a KM tool to monitor and track actions identified during risk assessments.

Knowledge as the currency of managing risk

Knowledge is a fundamental enabler to pharmaceutical quality. QRM should strive to apply the collective knowledge of the organization to recognize and understand risk and, in turn, to inform risk-based decisions that can have direct impact to patient safety and product availability. Said differently, knowledge is the currency of managing risk. Investing in the practice of knowledge management can provide an intentional, standard, and repeatable “how” for knowledge to be more effectively managed.

Enhancing knowledge via KM practices:a smart investment.

 Indeed, the opportunity is that knowledge is central to managing risk, making decisions, and ensuring product quality. Ensuring that knowledge is effectively managed (e.g., on-demand access and availability) via KM practices is a smart investment in the interest of patients. Further connecting QRM and KM with a framework such as the 

 can help the “system” perform as intended while further improving the outcomes of each QRM and KM practices.

Ask oneself: “What would I expect as a patient?”Would you not expect the best possible knowledge to be available to support risk assessments, risk control, risk review, and risk communication if you were waiting on a pharmaceutical company to deliver you a new lifesaving therapy? Would you not expect that pharmaceutical organizations “know what they know” and “can find and apply what they know” to deliver the best possible outcomes to you as a customer?

Beyond the framework: opportunities for biopharma as a knowledge industry.

 It is easy for organizations and the people employed by them to become consumed with day-to-day business and lose sight of the broader system to which their functional work contributes.Organizational structures, differing priorities, divergent governance processes, and countless other factors can contribute to “silos” in the business where people become narrowly focused. Systems thinking is a means to counter this trend, where one can “zoom out” to understand how the system works as a whole and how the major elements interrelate. It was such an opportunity the authors recognized in proposing the 

—that QRM and KM generally were focused on excellence in their respective domains—but little attention was paid to how they worked together to enable the PQS.

When one expands that aperture even further, it is possible to see a broad-knowledge “ecosystem,” where knowledge is connected and enables a great many processes in pharmaceutical organizations, including technology transfer, change management, deviation management and investigations, process validation, and others. In fact, if one were to zoom out to see the entire business unit or enterprise mapped at a process level, one could envision the relationships between these processes and the knowledge generated and consumed by each, and how that knowledge must flow between these various processes.

Multiple such frameworks exist, including the Process Classification Framework (PCF) tailored for the pharmaceutical industry, developed by the American Productivity & Quality Center (APQC) and IBM (24), which provides a detailed breakdown of processes and activities to run a business. At the highest level, this consists of process categories such as operating processes (e.g., develop vision and strategy, deliver products) and support processes (e.g., develop and manage human capital, manage information technology). One can then drill down into deeper levels of the PCF to reveal process groups, processes, and activities. The current version of the APQC PCF for the pharmaceutical industry in fact contains over 1200 rows across the four-tier structure to fully define the activities associated with operating a pharmaceutical business. With such an understanding of the business, one can envision developing and deploying holistic KM practices to enable the business at large (notably, such a foundational capability is called out by the APQC PCF as a support process).

Yet a further opportunity is to recognize and exploit the strengths and synergies of related practices in supporting the overall mission of the business, including the practices of knowledge management, operational excellence, organizational change management, learning, and development, digital, and human performance, at a minimum. Each of these aims to drive improvements of some sort in how business is conducted and can be co-deployed for enhanced impact and benefit.

KM practice is still highly variable within pharmaceutical organizations, many of which treat KM as discretionary even though it is called out as a key enabler to an effective PQS in ICH Q10 (5). Yet, expectations for managing knowledge appear throughout regulatory guidance (and also in industry guidance which was not discussed in this paper) and can be directly linked to effectiveness in risk management and risk-based decision-making. ICH Q9(R1) provides a renewed opportunity for the industry to recognize the foundational role knowledge and KM must play in support of risk management and risk-based decision making. Devoting energy and resources toward managing knowledge can clearly deliver worthwhile results.

Beyond the ICH Q9(R1) driver to recognize KM practice, as a society, we face a time of rapid and unprecedented change. Considering product complexity (e.g., advanced therapy medicinal products) cost pressure, continued realignment of supply chains, societal expectation for accelerated development, advancement of new technologies, continued pressure on innovation, and a constantly evolving regulatory and economic landscape, never before has it been more important to integrate risk and knowledge to benefit patients and, indeed, our businesses and other stakeholders.

The views expressed in this article are those of the authors and are not necessarily those of any organizations with which the authors are affiliated.

, Final concept paper (2020).
4. TechTarget, What is Systems Thinking? 

www.techtarget.com/searchcio/definition/systems-thinking

, Step 4 version (2008).
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7. Cambridge Dictionary, Explicit Knowledge. 

; APQC, 2022.
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A Lifecycle Approach to Knowledge Excellence in the Biopharmaceutical Industry

, 1st ed.; Calnan, N.; Lipa, M. J.; Kane, P.; Menezes, J. C., Eds.; Taylor & Francis, 2018; pp. 181–224. DOI: 10.1201/9781315368337
10. ICH, 

. www.ich.org/page/quality-guidelines (accessed April 23, 2023).
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Knowledge Management Systems—Requirements

; ISO 30401:2018; ISO: Geneva, Switzerland, 2018. https://www.iso.org/standard/68683.html(accessed April 23, 2023).
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 (2), 3. DOI: 10.21427/2jdq-jq09
13. ICH, 

Q13 Continuous Manufacturing of Drug Substances and Drug Products

, Draft version (2022).
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, 100010. DOI: 10.1016/j.ijpx.2019.100010.
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en.wikipedia.org/wiki/Knowledge_industries 

(accessed June 04, 2022).
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Martin J. Lipa*, PhD, martin.lipa@prst.ie, is senior research fellow, Pharmaceutical Regulatory Science Team. Anne Greene, PhD, is professor, Technological University Dublin, and director, Pharmaceutical Regulatory Science Team.

*To whom all correspondences should be addressed.

Articles

Recent research and perspectives shed light on an opportunity to better connect risk and knowledge through improved integration of systems.

Knowledge as the Currency of Managing Risk: A Smart Investment for Patients

Developed in the 1950s as a means to survive and compete against the giants of the automotive sector, lean manufacturing helped Toyota evolve from a small-volume producer (with little capital) to become a high-volume manufacturer in a process-rich environment. Toyota achieved this by using developments such as total production maintenance (TPM), just-in-time (JIT), Kanban, value stream mapping and Kaizen events.

  A summary of some of the lean terminology is shown in Table 1. 

 The pharmaceutical industry, manufacturing in a current good manufacturing practice (cGMP) environment, has been slow to adopt lean manufacturing — unlike many sectors where it has been successfully deployed. This article attempts to compare these two manufacturing environments and identify areas of overlap, together with challenges pharma may face in moving towards lean manufacturing. 

GMP has evolved gradually, however the recent scientific risk-based framework and the process analytical technology (PAT) initiatives, developed by regulatory authorities to support innovation and efficiency in a cGMP environment, suggest a new way of thinking for the 21st century.

Since 2001, regulatory authority policies have promoted initiatives designed to increase the availability of new and affordable medicines. This new thinking should help the pharmaceutical industry move towards innovation in manufacturing and alleviate the fear of lean improvement. These fears will only be removed when manufacturers are confident that a successful lean implementation in a cGMP environment can have both regulatory approval and be technically dependable.  

A comparison of cGMP with lean manufacturing (Table 2) might suggest that they belong to two conflicting families. 

Table 2 A comparison of lean and GMP manufacturing.			

While cGMP focuses on manufacturing as a means to produce safe and effective products for the patient, lean focuses on manufacturing as a location for improvement and value creation from a customer's perspective. For example, the public's expectation for an aspirin tablet has changed very little in comparison with their expectation for a personal computer during the last thirty years. They still expect the tablet to be safe and effective, while they expect the computer to have improved and provide greater value in return for the price paid. 

Lean's dual objectives, to reduce or eliminate waste and to create value, differ from cGMP's objective, which is to ensure that controls are in place to deliver a safe and effective medicinal product. Perhaps where cGMP and lean do overlap is in a shared history in the control of the manufacturing environment. To see the degree of overlap, a new perspective of lean, referred to as lean pharma, will be discussed.  

Lean pharma can best be viewed by looking across the lean landscape from a cGMP perspective. To do this, one has to define in simple terms what "lean" means.  

 In 1999, Spear and Bowen identified four rules that describe the make-up of the lean manufacturing system

 Standard work — all work must be specified as to content, sequence, timing and outcomes. 

 Clear relationships and communications — every internal customer-supplier connection must be direct with unambiguous ways to send requests and receive responses. 

 Simple flow — the pathway for every product and service must be simple, direct and followed. 

 Scientific method — improvement should be made using scientific methodology, under the guidance of a teacher and at the lowest possible level in the organization.

They identified the lean manufacturing paradox; the existence of rigidly documented processes that operate in a flexible and adaptable environment. It is the combination of being able to perform the simple things right, while also having the ability to change and adapt to customers' demands, which makes lean such a successful improvement methodology and business survival tool. 

For the purpose of this article, it will be assumed that these four rules must be followed for a traditional manufacturing culture to reach the tipping point to transform into a lean manufacturing culture.  

 One of the characteristics of a cGMP manufacturing environment is the abundance of documented processes such as standard operating procedures (SOPs), testing methods, environmental controls and training programmes. This documentation can be divided into technical standards and operational procedures. 

Technical standards, such as product specifications, validated settings and production conditions, can only change following a change control exercise. Operational procedures, such as the way people interact with equipment and the way that product flows, are based on custom and experience, and will change regularly in response to deviation or safety concerns. 

The essence of lean pharma is determining how current operational procedures can be modified to support short-term improvement, while maintaining the technical standards — thus ensuring no risk to the product. An attempt is made to do this, from a cGMP perspective, for each of the four rules defined here. 

Standard work in lean manufacturing is an agreed set of work procedures that establish the best and most reliable methods and sequences for each process and employee.

  It is a detailed instruction on how a task can best be performed today, with the understanding that it can be improved tomorrow. 

Standard work procedures are communicated simply and easily; more chart and display than paragraphs and pages. The employees who work with the process write the standard work procedures and their descriptions reflect what actually happens in the work place rather than what might happen. It is written after the manufacturing step has been optimized to ensure product quality is reproducible and product flow is continuous. 

Standard work is desired because if you can standardize a process then you can control it; and if you control it, then you can improve it. This is the essence of this rule. 

 In lean manufacturing, every step in every operation is performed in a predictable manner. The time, sequence, outcomes and inventory levels for each step are specified. The work time to perform a task is independent of the operator, so if it changes (increases or decreases) a problem has occurred. This immediately alerts the team leader to a problem as it happens, thus ensuring real–time control. 

Looking at the pharmaceutical industry, it may appear that standard work is already 

, with SOPs, manufacturing instructions, testing methods and validation protocols. However, these documents are often written by team leaders or scientists following validation and are only updated in response to a deviation, safety or quality issue.  

In the cGMP environment, critical specifications and technical standards are very well defined, deviations from which are obvious and thus action can be taken. Operational procedures, however, are often light on detail and as a result, variations in these may not be detected. For example, the way an operator sets up a workstation may vary with respect to time and outcomes. A check sheet may ensure that critical steps have been completed, but the manner in which the end-result was achieved is not obvious. It is then possible for work variation to enter if people use slightly different methods to achieve the same result. Typical finished dose manufacturing instructions may include "charge the blender...", "set up take-off plate".  

In a lean pharma plant, SOPs and other manufacturing instructions would be viewed as a means to expose problems and encourage improvement. Technical standards will be identified and fixed in line with regulatory requirements; all other operational procedures would be reviewed on a systematic basis and standardized with respect to time, sequence, content and outcome. For example, the batch manufacturing instruction could still contain the term "charge the blender", but the work standard for the blender would be written by the people who perform the action and would appear in a separate form, possibly as a chart or on-line display.  

The challenge for the pharmaceutical industry in moving towards lean is to design new operational procedures that comply with all external regulatory requirements, but at the same time support continuous improvement. 

In the lean manufacturing environment, every customer-supplier connection is direct with unambiguous ways to communicate. There is a clear and agreed way to send requests and receive responses. Product and information flows from one department to another and the barriers between departments are reduced. The overall goal is to keep product flowing. Consistent cycle time is an indicator of good internal supplier-customer relationships.  

In the cGMP environment, product cycle time is quality driven; it may take longer to release a batch than it does to produce it. Different departments usually have clear individual responsibilities and objectives. Departments often have separate responsibilities and objectives, resulting in teams working in isolation or in conflict with one another. Individual departments are often unaware of the impact that their delays or problems have on overall product flow.  

In the lean pharma environment, cycle time and quality would be of equal importance. This overlaps with FDA's PAT framework of reducing production cycle time. When a deviation in cycle occurs, it may indicate potential quality issues. For example, if it typically takes an operator 40 minutes to set up a tablet press but a batch takes longer, this indicates a problem with that batch. In lean pharma, a request for help would be made by the operator at 41 minutes, thus commencing an investigation. 

The need to ask for help when product flow is interrupted may lead to the identification of quality issues that otherwise might have been hidden from view or only detected during final release. For example, when an operator in a traditional pharma plant is required to produce a tablet to a specified disintegration time, the time it takes to set up the tablet press to produce tablets meeting the required technical standard is not controlled. In a lean pharma plant, the operator would have a work time standard. If it is not possible to produce the tablet to the required disintegration specification within this time, a call for help would be made. This may expose a problem in the upstream process, in this case granulation, which would otherwise be masked.  

The challenge for pharma in moving towards lean is to reduce the grey zones of responsibility, slow response and late calls for help when problems occur, and move towards an environment where problems are immediately identified, shared and resolved across the plant. 

In lean manufacturing, the pathway for every product and service must be simple and direct. Simple in that there is only one way to move forward and direct in that there are no loops, forks or fast paths.  

In the cGMP environment, the product path tends to be direct but far from simple because of batch manufacturing. This batch production method is inherent in cGMP to prevent cross-contamination. Each process step usually has a holding time for work in process, thus encouraging intermittent flow. Typically, a pharmaceutical facility operates with surplus capacity to keep the product moving, rather than flowing; however, this operational mode obstructs simple flow. For example, when a batch of tablets is scheduled to run on a blister pack line and three lines are available, surplus capacity at packaging exists. This will hide the reasons for any excessive downtime and slow changeover on the lines.  

Having available surplus capacity does not encourage continuous improvement or problem identification. From the customer's perspective, the fact that a pharmaceutical plant is running with surplus capacity should have little consequences in the short-term. The product will still be delivered on time, at the right quality and in accordance with cGMP, but the plant with surplus capacity available has higher operating costs.  

In the lean pharma plant, product or services would not flow to the next available person or tool, but to a specified person or item of equipment. This rule enforces the economies of repetition — the more times you take a certain path the more familiar you are with that path. Each time the product takes the same path, an experiment will occur to uncover variation and expose problems. This encourages continuous learning over the life cycle of a product. 

A batch moving from manufacturing to finished goods would only slow down for a value adding step. There would be a continuous programme to reduce batch queues and a move towards small-scale equipment to improve efficiency and manage variability. In lean pharma, the focus would be to reduce your batch size, move towards single unit flow, and thus improve responsiveness to changes in customers demand.  

The challenge for pharma in moving towards lean is to introduce simple flow for products and services and expose areas where improvement in flow will reduce cycle time and cost. To move forward from batch manufacturing towards single unit flow.  

The first three rules are rules of design, which show how to set up operations as experiments, with a view to controlling the process and ensuring it is reproducible. The fourth is the rule on improvement. Once you can do the job consistently every time, then you can improve. Attempting to improve a process that has too much variation often makes the process slower or reduce the effect of any improvements.  

Developed in the 1950s as a means to survive and compete against the giants of the automotive sector, lean manufacturing helped Toyota evolve from a small-volume producer (with little capital) to become a high-volume manufacturer in a process-rich environment. Toyota achieved this by using developments such as total production maintenance (TPM), just-in-time (JIT), Kanban, value stream mapping and Kaizen events.1 A summary of some of the lean terminology is shown in Table 1.

Lean Manufacturing practice in a cGMP environment

Introducing lean manufacturing into the pharma sector.