Monitoring and optimizing the process variables using these tools either independently or in combination helps to bring out the improvements.
Following are the tools used to enhance process validation or manufacturing processes.
- Quality by Design
- Design of Experiments (DOE)
- Process Analytical Technology (PAT)
- Lean and Six Sigma
- Process Capability
- 5 Why Analysis
- Fault Tree Analysis
- Fish Bone Diagram / Ishikawa Diagram / Cause and Effect Diagram
- Gage R&R Study
FDA also encourages using these tools as risk reduction methods, right from the beginning of process development till commercial product through Quality by Design (QbD) approach.
Quality by Design (QbD)
There is always a scope of improvement for whatever we do and pharmaceuticals are not an exception to this.
Just like the statistical approach discussed earlier, Quality by Design comes out as a scientific approach to reduce the process variation and improve productivity.
Previously, testing and inspection were performed to assure the quality where the control was the biggest point of concern both time and cost-wise.
To overcome this, Quality by Design explains an entirely different approach i.e. Building the quality in the product right from the process design stage.
QbD helps to find out all the related CTQs and CPPs for the product in scope and determine the extent of process variation.
What is Quality by Design (QbD)?
A concept that quality should be built into a product with a thorough understanding of the product and process by which it is developed and manufactured along with a knowledge of the risks involved in the manufacturing of the product and how to best mitigate those risks.Pharmaceutical Quality for the 21st Century A Risk Based Approach Progress Report Department of Health and Human Services U.S. Food and Drug Administration May 2007
Before moving ahead, let’s see a few key terms used in Quality by Design.
Critical Quality Attributes (CQAs)
Critical Quality Attributes are the chemical, physical, and biological properties that shall be within appropriate limits ensuring desired product quality and performance.
Example for drying: Bulk Density, Moisture Content, and product uniformity are CQAs. CQAs sometimes also called CTQ (Critical-To-Quality) therefore, directly drive your Quality by Design approach.
Critical Process Parameters (CPPs)
The parameters whose variability affects the CQAs and hence must be monitored and controlled to ensure the desired product quality and performance are called Critical Process Parameters.
Example for drying: Drying time, Air Flow, and temperature are CPPs. These CPPs directly or indirectly impact CQAs thereby may affect Quality by Design.
Quality Target Product Profile (QTPP)
It is a synopsis of quality characteristics of a drug product that will be achieved to ensure the desired quality and quality by design.
Cause and Effect Analysis (C&E)
Tool to investigate the cause and effect relationship for a failed process or product. This is explained through Fishbone or Ishikawa diagram further.
How Quality by Design Covers Different Tools
Quality by Design Lifecycle Management
Lifecycle management means the management of phases from product development through its discontinuation.
Apart from the FDA, ICH (International Conference on Harmonization) and ISO (International Organization for Standardization) have described a Quality by Design approach.
- ICH Q8 – Pharmaceutical Development
- Defines Quality by Design as: “A systematic approach to development that begins with predefined objectives and emphasizes product and product understanding and process control based on sound science and risk management.”
- Introduces the concept of Design Space as: “The multidimensional combination and interaction of input variables and process parameters that have been demonstrated to provide assurance of quality.”
- ICH Q9 – Quality Risk Management
- Defines Risk as: “The combination of the probability of occurrence of harm and severity of that harm.”
- The goal is: “To ensure consistent quality throughout the product lifecycle.”
- Tools Suggested
- FMEA (Failure Mode Effect Analysis)
- FMECA (Failure Mode Effect and Criticality Analysis)
- Fault Tree Analysis
- Hazard Analysis and Critical Control Points (HACCP)
- Preliminary Hazard Analysis (PHA)
- Risk Rating etc.
- ICH Q10 – Pharmaceutical Quality System
- Defines how quality can be assured using a Quality Management System
- Requires QMS to cover the entire product lifecycle
- ISO 14971 – Medical Devices – Application of Risk Management to Medical Devices
Benefits of Performing Quality by Design (QbD)
- Helps in maintaining consistent product quality
- Optimize the productivity
- Helps in achieving zero market recalls
- Cost Optimization
- Helps to detect and control the variations
- Perceive the impact of CPPs on CQAs
Design of Experiments (DOE)
Quality by Design covers different tools to bring out the desired decisions.
DOE is an intelligent decision-making tool to identify high-risk factors either affecting a process or a product response. The target is to develop and implement design space.
This requires a step-by-step process from choosing the objective of performing DOE to conduct multiple runs until successful data collection, analysis, and the conclusion is made.
When to Perform DOE?
- During technology transfer or scaling up from lab-scale to commercial-scale manufacturing to identify and mitigate potential risks.
- When a better understanding of the process and product performance is required through Quality by Design.
Flow of activities in DOE
Following diagram shows the flow of different activities in a typical Design of Experiments.
- Create a Quality by Design plan
- Choose your DOE Approach
- Define the minimum number of experiments required to investigate the variations including LSL and USL combinations
- Conduct the experiments
- Collect the data
- Run the statistical analysis of variance
- Create a graphical representation of the analysis
- Record the observations and determine the design space
This systematic approach helps in gathering the information efficiently. Also, helps to optimize the process by testing the magnitude of the effects and their suitability with quality by design approach.
When the design space within DOE is successfully established, small components of that process are the next target for better process control, based on statistical results from DOE. This is called Control Space.
Control space is a smaller zone in the design space explaining the commercial operation limits. Changing the parameters within the control space considered acceptable for the desired product quality. See the diagram above, “Overall Quality by Design Approach”.
Although this seems a simple approach, it requires collaborative efforts from Subject Matter Experts and cross-functional team to taste the success in Quality by Design.
Process Analytical Technology (PAT)
This is another excellent approach to demonstrate Quality by Design. Unlike DOE, PAT provides a commitment of process control in real-time where ‘Analytical’ term accounts for Chemical, Physical, Biological, and Microbiological properties.
FDA states, “A desired goal of the PAT framework is to design and develop well-understood processes that will consistently ensure a predefined quality at the end of the manufacturing process. Such procedures would be consistent with the basic tenet of quality by design and could reduce risks to quality and regulatory concerns while improving efficiency.“
To achieve this testimonial, industries adapt following measures as improvements.
- Mitigating and preventing scraps/rejects/re-processing
- Improved process automation
- Improved production capacity
- Reduced process cycle times for better productivity
Process understanding is one of the aspects of PAT where a process is considered to be well-understood when:
- All critical sources of variations identified and described
- The process itself manages variability
- Product quality attributes in the design space are predicted accurately and reliably
Pharmaceutical manufacturing involves various operations at different stages of production to change either a physical or chemical attribute of the product. This assures and establishes the manufacturer with in-process sampling and testing.
While chemical attributes are easy to detect analytically for both product and raw materials, measuring physical attributes imposes challenges in Quality by Design. Out of all physical attributes, variability in only some of them may affect the end product quality.
To facilitate the process understanding and improvement, the FDA laid down 4 different approaches for the PAT framework:
- PAT Tools
- Multivariate Tool for Design, Data Acquisition and Analysis
- Process Analyzers
- Process Control Tools
- Continuous Improvement and Knowledge Management Tools
- Risk-Based Approach
- Integrated Systems Approach
- Real-Time Release
These approaches assist the manufacturer to propose and implement innovative manufacturing and quality assurance systems.
The FDA encourages this and has also developed a regulatory strategy to consider such proposals such as:
- A PAT Team approach for CMC (Chemistry, Manufacturing and Controls) Review and cGMP inspections
- Joint training and certification of PAT review, inspection and compliance staff
- Scientific and technical support for the PAT review, inspection and compliance staff
- Recommendations as per PAT guidance
The goal of these FDA’s PAT activities is provided in accordance with the needs of regulatory inspections that:
- Improve the scientific basis for establishing regulatory specifications
- Promote continuous improvement
- Improve manufacturing along with product quality
Let us discuss some of the tools that will help to enhance the QbD approach within your pharmaceutical industries.
Lean and Six Sigma
Lean and Six Sigma (6σ) can work independently or together. Also, they may be considered as part of Quality by Design or a separate entity ultimately improving the process.
Process variation is an undesired phenomenon.
Six Sigma (6σ) is a systematic approach to improve the process which uses statistical tools and techniques for analysis and process improvement. When added to a partnering method like Lean, Six Sigma (6σ) helps even more in eliminating the undesired operations systematically.
Sigma (σ) term indicates a mathematical approach, the standard deviation. It is a measure of variation.
The goal of Six Sigma (6σ) is to measure, minimize, and eliminate the variations or defects. Therefore, it gets even critical when dealing with the uncertainties in process design.
Once you measure, you identify. Once you identify, you improve. Once you improve, it gets recognized. Once recognized, you make it a habit.
- To create value and reduce wastes
- Improve product delivery
- Quality with productivity
- Reduce inventory
- Reduce Process Cycle Time
Tools and techniques that follow Lean principles:
- VSM (Value Stream Mapping) – The way of mapping the process that will enable the organization to identify and reduce the wastes which do not add value to either the product or the process.
- Typically depicts the flow of activities by operations, duration, and cost of each activity
- Kaizen (Kai: Change, Zen: Good) – Improvement to drive the overall effectiveness within the organization.
- 5s – A technique to improve housekeeping and working environment. Seiri (sorting), Seiton (set in order), Seiso (sweep), Seiketsu (standardize), Shitsuke (sustain).
- Gemba – Tool to pinpoint the process where the value is generated.
- Mistake Proofing (Poka-Yoke) – Fail-safe mechanism that prevents the process from generating defects.
- QFD (Quality Function Deployment) – Approach to deal with the customer needs to make them satisfactory. There are different QFD matrices for customer quality expectations in comparison with the organizational functions such as:
- Requirement Matrix – Customer Requirements
- Design Matrix – Design Requirements
- Product Characterization Matrix – Engineering Design
- Manufacturing Matrix – Product Characteristics
- Control Matrix – Manufacturing Operations
- Kanban – Inventory optimization technique based on simple parts movement depending on boxes from one work station to the other
- Moving to pull – A technique to work or manufacture as per market demand reducing overhead rate and storage costs.
It is all about philosophy, mindset, and culture.
Lean thinking has a set of principles used to create and deliver the maximum value to the customer with the least resource consumption.
Six Sigma (6σ)
- Controlling the input variations to reduce the output variations eventually delivering a consistent product quality.
- To fast-track the process improvements systematically without hampering customer relationships.
Tool – DMAIC
Let us see this in short.
- Define – Identify Opportunities
- Define the project and objectives
- Define CFT (Cross-Functional Team)
- Create a Process Map
- Recognize Customer Needs
- Define Priorities and update Project Charter
- Measure – Identify Key Outputs
- Recognize Key Process Output Variable
- Test the measurement system
- Find out the process behavior (eg. Stability)
- Determine Process Capability
- Define the target for measurement
- Analyze – Identify Critical Inputs
- Fishbone Diagram
- Root Cause Analysis
- Review and finalize the critical process inputs
- Improve – Optimize those Inputs
- Define the allowance in variation
- Create and follow an action plan
- Verify and review the potential improvement
- Control – Control those Inputs
- Draw systematic SOPs (Standard Operating Procedures)
- Train the working professionals
- Monitor the output
- Validate the action plan
- Close the project file
Though this looks simple to understand, it needs close heed while implementation. There is a very nice article explaining the details about the Six Sigma (6σ) principles you may want to go through.
Following are the Six Sigma (6σ) Qualification Levels:
- Champion – Management Leader
- Master Black Belt – Supporting function to Champion and who identifies the Project
- Black Belt – Full Time, Complex Role and a Key Project Driver
- Green Belt – Part-Time, Non-Complex supporting function to Black Belt
- Yellow Belt – Supporting team that collects data and perform basic analysis
- White Belt – Same role as Yellow Belt in training mode
Process-Failure Mode Effect Analysis (pFMEA)
Risk Management is also covered under Quality by Design but we’ll see it specifically here.
pFMEA is a Quality Risk Management (QRM) tool that allows the manufacturer to anticipate, identify, prevent, or address the failure even before its occurrence.
It is therefore a product life-cycle document in process validation that shows the roadmap for failure management.
The below matrix shows the example of pFMEA.
(Zoom-in OR Open image in new tab for easy reading)
There are 8 steps to apply pFMEA once we identify Severity (S), Occurrence (O), and Detectability (D).
Steps to apply pFMEA –
- Select a process to analyze
- Form a Cross-Functional Team
- Define a process description
- Identify potential risks
- Prioritize the issues that impact either process or product quality directly or indirectly
- Target the big issues first and bottlenecks prior to mitigation
- Propose and Implement risk mitigation actions
- Measure the success of the process (either during process validation stages or as applicable)
As described above, ISO 14971:2019 – Medical devices — Application of risk management to medical devices also follows the same pattern for risk identification and mitigation.
The Difficulty in pFMEA
See, the SOD is multiplied to give an RPN number which dictates the Risk Acceptance based on certain criteria. The higher the RPN, the more the risk.
Though this statement sounds logical, actually it is not. Because, two factors are dynamic in this process i.e. O and D. If the only D is reduced, then also RPN will decrease. Meaning, O is still at a risk that is undermined due to this RPN calculation.
When conducting FMEA studies especially in healthcare industries, one must look at all the possibilities that may happen by increasing or decreasing the RPN number and consider each component of SOD as it is to make conclusions.
Process Capability is a statistical study to expedite how all the measurements fall inside the pre-defined specification limits. It may fall under Quality by Design. The capability of a process generally measured in terms of Cp value.
For healthcare manufacturing, if Cp ≥1.33 (i.e. 4 Sigma) then a process is considered as capable to satisfy the customer needs.
Different terms are used while measuring Process Capability. Let’s see them in short.
Short-Term and Futuristic Approach
Process Capability (Cp)
Process Capability is a simple approach to show the process is capable to result in a product that will fall inside its pre-defined specification limits.
Cp = (USL – LSL) / 6σ
USL – Upper Specification Limit
LSL – Lower Specification Limit
6σ – Estimated noise of a stable process
Process Capability Index (Cpk)
Process Capability Index (Cpk) is an advanced approach to demonstrate that the process is capable of consistently falling inside its pre-defined specification limits and that too with precision.
Often, Cpk is a simple number. A common term for this approach is called process centering. Meaning, how well a process is able to establish its outcome within the center of its specification limits.
Cpk = min (CpkU, CpkL)
CpkU = (USL – Mean) / 3σ
CpkL = (Mean – LSL) / 3σ
Using Cp and Cpk together can dictate to us how capable a process is and whether it is centered or off-centered.
In pharmaceutical’s language, this approach resembles the Quality by Testing (QbT) approach.
Long-Term and Retrospective Approach
Process Performance Index (Pp)
Process Performance is a measure of overall process variability with respect to customer CTQs, unlike process capability.
Pp = (USL – LSL) / SD
where SD – overall variability (Standard Deviation for Common+Special Cause of Variations)
Min. Process Performance Index (Ppk)
This shows, how a process has performed in the past, irrespective of its stability.
Ppk = min (PpkU, PpkL)
PpkU = (USL – Mean) / 3σ
PpkL = (Mean – LSL) / 3σ
For all the above terminologies, k stands for centering factor.
Process Capability vs. Six Sigma (6σ)
|Cpk Value||Sigma Level||Sigma %||Rating|
|1||3||99.73||Kind of Capable|
Difference between Cpk and Ppk
|Short-term approach||Long Term Approach|
|How the process is capable in future||How the process was capable in the past|
|Requires a process in the state of control||State of control neither required nor possible as the process already occurred|
|Tells us about how the variation will affect the process capability in terms of customer CTQs||Tells us about the extent of process variations|
|Needs data sub-grouping such as machine, operators, shifts, floors etc.||It applies to the whole process|
5 Why Analysis
Often, the defects or failures that occur within a manufacturing environment attributed to one or various reasons.
Rather than thinking about what might be the cause for the issue, asking “why” would help in the systematic investigation.
5 Why Analysis is based on Lean thinking and is an event-based tool for digging out the root cause.
Though it contains the number 5, it does not mean that it must contain 5. The number of whys may be less or more depending on the magnitude of the event and you finding the root cause of an event.
The important point is how relevant your whys are and how effectively you investigate the root cause.
A common example would follow the 5 Why method as below. Consider an event of a fire that happened in a solvent tank farm where this tool would work as:
- Why the fire occurred? Because of flammable solvent
- Why the flammable solvent caught the fire? Because of static charge
- Why Static charge developed? Because earthing jumper on pipe-line was missing
- Why it was missing? Because it was incorrectly installed and might have been detached over the time
- Why it was installed incorrectly? Because the system was not in place to verify the occurrence.
The system can then be implemented such as Good-Save/Near-Miss to channelize the employees working in that area sharing the feedback and also the frequent preventive inspections for such issues.
Hence this tool helps to identify the opportunity to improve through systematic Q&A.
Sometimes, 5 Why Analysis doesn’t go as expected if used as the only tool to investigate the root cause and may need to be used in conjunction with another suitable tool.
Fault Tree Analysis
The Fault Tress Analysis is a graphical tool to investigate the roots of the occurred events such as defect, failure, deviation, incident, non-conformance, etc.
The structure of this tool resembles a tree above ground as an “event” while underground roots as “potential reasons” for the event. The layers of different investigative questions can lead to one or more reasons for the resultant event.
To understand how this tool can help in identifying the root cause of an event, let us see an example. Imagine an event where a batch has failed in its final moisture requirements post drying.
Fault tree diagram for this example could be developed something like this:
You see, the first layer of the tree has some critical parameters and the branches are developed along with the progress of the investigation.
Environmental and test instrument conditions appear to be normal. Whereas “Cycle Time” under “Drying trend” and “Initial Moisture” related investigations found to have loose ends. When investigated further, we can see the root cause can be allocated based on two aspects i.e. technical and documentation related. Technical in terms of the broken seal while for documentation, BMR failed to capture the moisture-related information before drying.
Once we conclude these root causes, corrective and preventive actions (CAPA) should be proposed targetting those issues and verifying that they will not occur in the future.
For a broken seal, document checks should be performed to ensure its validity and replacement timelines. While for BMR correction, initiating a quality event as per the local procedures would serve the purpose.
In this way, Fault Tree Analysis can help in identifying the root cause for such events. Sometimes, this tool is used along with other tools to streamline the investigation process.
Fishbone Diagram / Ishikawa Diagram / Cause and Effect Diagram
The cause and effect diagram has two sides. The side on the right categorized as Effects which records the burning issues while the left side categorized as Causes which records the major causes of those issues.
A Cross-Functional Team is formed to identify, expedite, chart out the potential causes. This team may be similar to the one defined in quality by design approach.
Major causes should occupy a major portion of the diagram. This diagram is best suitable for the management to effectively understand the real burning issues within their organization.
Once the dominant causes marked as bone of fish, next step is to identify and allocate the 6M’s i.e.
- Mother Nature
CFT then helps the facilitator to sort out those causes becoming the main bones of a fish. The below diagram represents a typical representation of the fishbone method.
Gage R&R Study
Gage R&R used in Measurement System Analysis (MSA) and is a quantitative assessment of variation in a measurement system compared to the total variation of the process in terms of repeatability and reproducibility.
Well, better if we differentiate them now.
|Short-term variability||Long-term variability|
|In-built precision of instrument or device itself||Variations are external conditions like environment, operators, etc.|
|This is a residual error||This is the sum of variations because of personnel-related changes|
Exactly speaking, Gage R&R performed for validating the measurement system to determine % of total variation with respect to gage and % of tolerance a gage variation has.
Gage R&R actually work-out needs exhaustive runs to get all the sources of variability with sufficient precision.
Hence, it becomes impossible to estimate the process variability at the development stage because of the small number of lots.
To elaborate the above in a simple way, Gage R&R enables the six sigma practitioners to label their process as Six Sigma by not producing the defects of more than 3.4 per million opportunities.
Gage R&R comes under the Measurement (M) Category according to the DMAIC tool in the six sigma methodology.
Gage R&R studies have been used to investigate chemical analytical methods for uniformity at different stages of pharmaceutical manufacturing i.e. APIs, Formulations, and Injectables.
Total Variability = Process Variability + Measurement Error (Gage Variability)
This method can be considered for new Test Method Validation as well as already validated measurement systems within the pharmaceutical industries. This includes both destructive and non-destructive testing.
Sometimes, this is not considered quality by design tool because certain measurements fall outside the QbD scope.
Gage R&R can be calculated by 3 different techniques.
- Average and Range Estimate (Long AIAG)
- Range OR Measurement Process Evaluation MPE (Short AIAG)
- Analysis of Variation (ANOVA)
Here, AIAG stands for Automotive Industry Action Group. If you are interested in additional information on this, you can visit SPC for Excel where they’ve got a plethora of information on Gage R&R.
Quality by Design is a vast space and hence requires a systematic approach while dealing with different aspects of it. Some tools may fall completely or partly under quality by design space depending upon the need and scope of your project.
This article is a summary of all the key tools that bring effectiveness in improving the productivity of any healthcare-related industry. To keep things simple, these were just fundamental explanations and do not include exhaustive calculations.