Timely Equipment/Facility Upgrades

One of the many fascinating items in the recent Warning Letter to Sanofi is the FDA’s direction to provide a plan to perform “timely technological upgrades to the equipment/facility infrastructure.” This point drives home the point that staying current with technological advancements is crucial for maintaining compliance, improving efficiency, and ensuring product quality. Yet, I think it is fair to say we rarely see it this bluntly put as a requirement.

One of the many reasons this Warning Letter stands out is that this is (as far as I can tell) the same facility that won the ISPE’s Facility of the Year award in 2020. This means it is still a pretty new facility, and since it is one of the templates that many single-use biotech manufacturing facilities are based on, we had best pay attention. If a failure to maintain a state-of-the-art facility can contribute to this sort of Warning Letter, then many companies had best be paying close attention. There is a lot to unpack and learn here.

Establishing an Ongoing Technology Platform Process

To meet regulatory requirements and industry standards, facilities should implement a systematic approach to technological upgrades.

1. Conduct Regular Assessments

At least annually, perform comprehensive evaluations of your facility’s equipment, systems, and processes. This assessment should include:

Review of equipment performance and maintenance, including equipment effectiveness
Analysis of deviation reports and quality issues
Evaluation of current technologies against emerging industry standards
Assessment of facility design and layout for potential improvements

This should be captured as part of the FUSE metrics plan and appropriately evaluated as part of quality governance.

2. Stay Informed on Industry Trends

Keep abreast of technological advancements in biotech manufacturing at minimum by:

Attending industry conferences and workshops
Participating in working groups for key consensus standard writers, such as ISPE and ASTM
Subscribing to relevant publications and regulatory updates
Engaging with equipment vendors and technology providers

3. Develop a Risk-Based Approach

Prioritize upgrades based on their potential impact on product quality, patient safety, and regulatory compliance. Utilize living risk assessments to get a sense of where issues are developing. These should be the evolution of the risk management that built the facility.

4. Create a Technology Roadmap

Develop a long-term plan for implementing upgrades, considering:

Budget constraints and return on investment
Regulatory timelines for submissions and approvals
Production schedules and potential downtime
Integration with existing systems and processes

5. Implement Change Management Procedures

Ensure there is a robust change management process in place to ensure that upgrades are implemented safely and effectively. This should include:

Detailed documentation of proposed changes
Impact assessments on product quality and regulatory compliance
Appropriate verification for new and changed equipment and systems
Training programs for personnel

6. Appropriate Verification – Commissioning, Qualification and Validation

Conduct thorough verification activities to demonstrate that the upgraded equipment or systems meet predetermined specifications and regulatory requirements.

7. Monitor and Review Performance

Continuously monitor the performance of upgraded systems and equipment to ensure they meet expectations and comply with cGMP requirements. Conduct periodic reviews to identify any necessary adjustments or further improvements. This is all part of Stage 3 of the FDA’s process validation model focusing on ongoing assurance that the process remains in a state of control during routine commercial manufacture. This stage is designed to:

Anticipate and prevent issues before they occur
Detect unplanned deviations from the process
Identify and correct problems

Leveraging Advanced Technologies

To stay ahead of regulatory expectations and industry trends, consider incorporating advanced technologies into your upgrade plans:

Single-Use Systems (SUS): Implement disposable components to reduce cleaning and validation requirements while improving flexibility.
Modern Microbial Methods (MMM): Implement advanced techniques used in microbiology that offer significant advantages over traditional culture-based methods
Process Analytical Technology (PAT): Integrate real-time monitoring and control systems to enhance product quality and process understanding.
Data Analytics and Artificial Intelligence: Implement advanced data analysis tools to identify trends, predict maintenance needs, and optimize processes.

Conclusion

Maintaining a state-of-the-art biotech facility requires a proactive and systematic approach to technological upgrades. By establishing an ongoing process for identifying and implementing improvements, facilities can ensure compliance with FDA requirements, align with industry standards, and stay competitive in the rapidly evolving biotech landscape.

Remember that the goal is not just to meet current regulatory expectations but to anticipate future requirements and position your facility at the forefront of biotech manufacturing excellence. By following this comprehensive approach and staying informed on industry developments, you can create a robust, flexible, and compliant manufacturing environment that supports the production of high-quality biopharmaceutical products.

Health of the Validation Program

In the Metrics Plan for Facility, Utility, System and Equipment that is being developed a focus is on effective commissioning, qualification, and validation processes.

To demonstrate the success of a CQV program we might brainstorm the following metrics.

Deviation and Non-Conformance Rates

Track the number and severity of deviations related to commissioned, qualified and validated processes and FUSE elements.
The effectiveness of CAPAs that involve CQV elements

Change Control Effectiveness

Measure the number of successful changes implemented without issues
Track the time taken to implement and qualify validate changes

Risk Reduction

Quantify the reduction in high and medium risks identified during risk assessments as a result of CQV activities
Monitor the effectiveness of risk mitigation strategies

Training and Competency

Measure the percentage of personnel with up-to-date training on CQV procedures
Track competency assessment scores for key validation personnel

Documentation Quality

Measure the number of validation discrepancies found during reviews
Track the time taken to approve validation documents

Supplier Performance

Monitor supplier audit results related to validated systems or components
Track supplier-related deviations or non-conformances

Regulatory Inspection Outcomes

Track the number and severity of validation-related observations during inspections
Measure the time taken to address and close out regulatory findings

Cost and Efficiency Metrics

Measure the time and resources required to complete validation activities
Track cost savings achieved through optimized CQV approaches

By tracking these metrics, we might be able to demonstrate a comprehensive and effective CQV program that aligns with regulatory expectations. Or we might just spend time measuring stuff that may not be tailored to our individual company’s processes, products, and risk profile. And quite frankly, will they influence the system the way we want? It’s time to pull out an IMPACT key behavior analysis to help us tailor a right-sized set of metrics.

The first thing to do is to go to first principles, to take a big step back and ask – what do I really want to improve?

The purpose of a CQV program is to provide documented evidence that facilities, systems, equipment and processes have been designed, installed and operate in accordance with predetermined specifications and quality attributes:

To verify that critical aspects of a facility, utility system, equipment or process meet approved design specifications and quality attributes.
To demonstrate that processes, equipment and systems are fit for their intended use and perform as expected to consistently produce a product meeting its quality attributes.
To establish confidence that the manufacturing process is capable of consistently delivering quality product.
To identify and understand sources of variability in the process to better control it.
To detect potential problems early in development and prevent issues during routine production.

The ultimate measure of success is demonstrating and maintaining a validated state that ensures consistent production of safe and effective products meeting all quality requirements.

Focusing on the Impact is important. What are we truly concerned about for our CQV program. Based on that we come up with two main factors:

The level of deviations that stem from root causes associated with our CQV program
The readiness of FUSE elements for use (project adherence)

Reducing Deviations from CQV Activities

First, we gather data, what deviations are we looking for? These are the types of root causes that we will evaluate. Of course, your use of the 7Ms may vary, this list is to start conversation.

Means	Automation or Interface Design Inadequate/Defective	Validated machine or computer system interface or automation failed to meet specification due to inadequate/defective design.
Means	Preventative Maintenance Inadequate	The preventive maintenance performed on the equipment was insufficient or not performed as required.
Means	Preventative Maintenance Not Defined	No preventive maintenance is defined for the equipment used.
Means	Equipment Defective/Damaged/Failure	The equipment used was defective or a specific component failed to operate as intended.
Means	Equipment Incorrect	Equipment required for the task was set up or used incorrectly or the wrong equipment was used for the task.
Means	Equipment Design Inadequate/Defective	The equipment was not designed or qualified to perform the task required or the equipment was defective, which prevented its normal operation.
Media	Facility Design	Improper or inadequate layout or construction of facility, area, or work station.
Methods	Calibration Frequency is Not Sufficient/Deficiency	Calibration interval is too long and/or calibration schedule is lacking.
Methods	Calibration/Validation Problem	An error occurred because of a data collection- related issue regarding calibration or validation.
Methods	System / Process Not Defined	The system/tool or the defined process to perform the task does not exist.

Based on analysis of what is going on we can move into using a why-why technique to look at our layers.

Why 1	Why are deviations stemming from CQV events not at 0% Because unexpected issues or discrepancies arise after the commissioning, qualification, or validation processes Success factor needed for this step: Effectiveness of the CQV program Metric for this step: Adherence to CQV requirements
Why 2 (a)	Why are unexpected issues arising after CQV? Because of inadequate planning and resource constraints in the CQV process. Success Factor needed for this step: Appropriate project and resource planning Metric for this Step: Resource allocation
Why 3 (a)	Why are we not performing adequate resource planning? Because of the tight project timelines, and the involvement of multiple stakeholders with different areas of expertise Success Factor needed for this step: Cross-functional governance to implement risk methodologies to focus efforts on critical areas Metric for this Step: Risk Coverage Ratio measuring the percentage of identified critical risks that have been properly assessed and and mitigated through the cross-functional risk management process. This metric helps evaluate how effectively the governance structure is addressing the most important risks facing the organization.
Why 2 (b)	Why are unexpected issues arising after CQV? Because of poorly executed elements of the CQV process stemming from poorly written procedures and under-qualified staff. Success Factor needed for this step: Process Improvements and Training Qualification Metric for this Step: Performance to Maturity Plan

There were somethings I definitely glossed over there, and forgive me for not providing numbers there, but I think you get the gist.

So now I’ve identified the I – How do we improve reliability of our CQV program, measured by reducing deviations. Let’s break out the rest.

	Parameters	Executed for CQV
IDENTIFY	The desired quality or process improvement goal (the top-level goal)	Improve the effectiveness of the CQV program by taking actions to reduce deviations stemming from verification of FUSE and process.
MEASURE	Establish the existing Measure (KPI) used to conform and report achievement of the goal	Set a target reduction of deviations related to CQV activities.
Pinpoint	Pinpoint the “desired” behaviors necessary to deliver the goal (behaviors that contribute successes and failures)	Drive good project planning and project adherence. Promote and coach for enhanced attention to detail where “quality is everyone’s job.” Encourage a speak-up culture where concerns, issues or suggestions are shared in a timely manner in a neutral constructive forum.
ACTIVATE the CONSEQUENCES	Activate the Consequences to motivate the delivery of the goal (4:1 positive to negative actionable consequences)	Organize team briefings on consequences Review outcomes of project health Senior leadership celebrate/acknowledge Acknowledge and recognize improvements Motivate the team through team awards Measure success on individual deliverables through a Rubric
TRANSFER	Transfer the knowledge across the organization to sustain the performance improvement	Create learning teams Lessons learned are documented and shared Lunch-and-learn sessions Create improvement case studies

From these two exercises I’ve now identified my lagging and leading indicators at the KPI and the KBI level.

Equipment Effectiveness – a KPI with a few built in KBIs

A key KPI for a FUSE program is Overall Equipment Effectiveness (OEE) which measures the efficiency and productivity of equipment and production processes.

Definition of OEE

OEE is a percentage that represents the proportion of truly productive manufacturing time. It takes into account three main factors:

Availability: The ratio of Run Time to Planned Production Time. It takes into account any events that stop planned production for an appreciable length of time.
Performance: Anything that causes the manufacturing process to run at less than the maximum possible efficiency when it is running.
Quality: Manufactured material that do not meet quality standards, including materialthat require rework and reprocessing.

The formula for calculating OEE is:

OEE = Availability × Performance × Quality

Components of OEE

Availability

Availability measures the percentage of scheduled time that the equipment is available to operate. It accounts for downtime losses.

Availability = Run Time / Planned Production Time

Performance

Performance compares the actual output of equipment to its theoretical maximum output at optimal speed.

Performance = (Ideal Cycle Time × Total Count) / Run Time

Quality

Quality represents the percentage of released material produced out of the total material produced.

Quality = Good Count / Total Count

Importance of OEE

OEE is crucial for several reasons:

It provides a comprehensive view of manufacturing productivity.
It helps identify losses and areas for improvement.
It serves as a benchmark for comparing performance across different equipment or production lines.
It supports continuous improvement initiatives.

Interpreting OEE Scores

While OEE scores can vary by industry, generally:

100% OEE is perfect production
85% is considered world-class
60% is fairly typical
40% is low but not uncommon for companies just starting to measure OEE

Benefits of Tracking OEE

Identifies hidden capacity in manufacturing operations
Reduces manufacturing costs
Improves quality control
Increases equipment longevity through better maintenance practices
Enhances decision-making with data-driven insights

Improving OEE

To improve OEE, manufacturers can:

Implement preventive maintenance programs
Optimize changeover procedures
Enhance operator training
Use real-time monitoring systems
Analyze root causes of downtime and quality issues
Implement continuous improvement methodologies

By focusing on OEE, manufacturers can significantly enhance their productivity, reduce waste, and improve their bottom line. It’s a powerful metric that provides actionable insights for optimizing manufacturing processes.

The Effectiveness of the OEE Metric

Utilizing the rubric:

Attribute	Meaning	Score	What this means in My Organization
Relevance	How strongly does this metric connect to business objectives?	5	Empirically Direct – Data proves the metric directly supports at least one business objective – the ability to meet client requirements
Measurability	How much effort would it take to track this metric?	3	Medium – Data exists but in a variety of spreadsheets systems, minor collection or measurement challenges may exist. Will need to agree on what certain aspects of data means.
Precision	How often and by what margin does the metric change?	5	Once we agree on the metric and how to measure it, it should be Highly Predictable
Actionability	Can we clearly articulate actions we would take in response to this metric?	4	Some consensus on action, and capability currently exists to take action. This metric will be used to drive consensus.
Presence of Baseline	Does internal or external baseline data exist to indicate good/poor performance for this metric?	3	Baseline must be based on incomplete or directional data. Quite frankly, the site is just qualified and there will be a rough patch.

This tells me this is a strong metric that requires a fair amount of work to implement. It is certainly going into the Metrics Plan.

A Deeper Dive into Equipment Availability

Equipment availability metric measures the proportion of time a piece of equipment or machinery is operational and ready for production compared to the total planned production time. It is a key component of Overall Equipment Effectiveness (OEE), along with Performance and Quality.

This metric directly impacts production capacity and throughput with a high availability indicating efficient maintenance practices and equipment reliability. This metric helps identify areas for improvement in operations and maintenance.

Definition and Calculation

Equipment availability is expressed as a percentage and calculated using the following formula:

Availability (%) = (Actual Operation Time / Planned Production Time) × 100

Where:

Actual Operation Time = Planned Production Time – Total Downtime
Planned Production Time = Total Time – Planned Downtime

For example, if a machine is scheduled to run for 8 hours but experiences 1 hour of unplanned downtime:

Availability = (8 hours – 1 hour) / 8 hours = 87.5%Types of Availability Metrics

Inherent Availability

This metric is often used by equipment designers and manufacturers. It only considers corrective maintenance downtime.

Inherent Availability = MTBF / (MTBF + MTTR)

Where:

MTBF = Mean Time Between Failures
MTTR = Mean Time To Repair

Achieved Availability

This version includes both corrective and preventive maintenance downtime, making it more useful for maintenance teams.

Achieved Availability = MTBM / (MTBM + M)

Where:

MTBM = Mean Time Between Maintenance
M = Mean Active Maintenance Time

Factors Affecting Equipment Availability

Planned downtime (e.g., scheduled maintenance, changeovers)
Unplanned downtime (e.g., breakdowns, unexpected repairs)
Equipment reliability
Maintenance strategies and effectiveness
Operator skills and training

Improving Equipment Availability

To increase equipment availability, consider the following strategies:

Implement preventive and predictive maintenance programs.
Optimize changeover procedures and reduce setup times.
Enhance operator training to improve equipment handling and minor maintenance skills.
Use real-time monitoring systems to quickly identify and address issues.
Analyze root causes of downtime and implement targeted improvements.
Incorporate fault tolerance at the equipment design stage.
Create asset-specific maintenance programs.

Relationship to Other Metrics

Equipment availability is closely related to other important manufacturing metrics:

It’s one of the three components of OEE, alongside Performance and Quality.
It’s distinct from but related to equipment reliability, which measures the probability of failure-free operation.
It impacts overall plant efficiency and productivity.

By focusing on improving equipment availability, manufacturers can enhance their overall operational efficiency, reduce costs, and increase production capacity. Regular monitoring and analysis of this metric can provide valuable insights for continuous improvement initiatives in manufacturing processes.

To generate an equipment availability KPI in process manufacturing, you should follow these steps:

Calculate Equipment Availability

The basic formula for equipment availability is:

Availability = Run Time / Planned Production Time

Where:

Run Time = Planned Production Time – Downtime
Planned Production Time = Total Time – Planned Downtime

For example, if a machine is scheduled to run for 8 hours, but has 1 hour of unplanned downtime:

Availability = (8 hours – 1 hour) / 8 hours = 87.5%

Track Key Data Points

To calculate availability accurately, you need to track:

Total available time
Planned downtime (e.g. scheduled maintenance)
Unplanned downtime (e.g. breakdowns)
Actual production time

Implement Data Collection Systems

Use automated data collection systems like machine monitoring software or manufacturing execution systems (MES) to capture accurate, real-time data on equipment status and downtime.

Analyze Root Causes

Categorize and analyze causes of downtime to identify improvement opportunities. Common causes include:

Equipment failures
Changeovers/setups
Material shortages
Operator availability

Set Targets and Monitor Trends

Set realistic availability targets based on industry benchmarks and your current performance
Track availability over time to identify trends and measure improvement efforts
Compare availability across equipment and production lines

Take Action to Improve Availability

Implement preventive and predictive maintenance programs
Optimize changeover procedures
Improve operator training
Address chronic equipment issues

Use Digital Tools

Leverage technologies like IoT sensors, cloud analytics, and digital twins to gain deeper insights into equipment performance and predict potential failures.

Planned Production Time

Planned production time is the total amount of time scheduled for production activities, excluding planned downtime. It represents the time during which equipment or production lines are expected to be operational and producing goods. It can be rather tricky to agree on the exact meaning.

Calculation

The basic formula for planned production time is:

Planned Production Time = Total Time – Planned Downtime

Where:

Total Time is the entire time period being considered (e.g., a shift, day, week, or month)
Planned Downtime includes scheduled maintenance, changeovers, and other planned non-productive activities

Components of Planned Production Time

Total Time

This is the full duration of the period being analyzed, such as:

A single 8-hour shift
A 24-hour day
A 7-day week
A 30-day month

Planned Downtime

This includes all scheduled non-productive time, such as:

Preventive maintenance
Scheduled breaks
Shift changes
Planned changeovers between batches
Cleaning and sanitation procedures

Considerations for Batch Manufacturing

In batch production, several factors affect planned production time:

Batch Changeovers: Time allocated for switching between different product batches must be accounted for as planned downtime.
Equipment Setup: The time required to configure machinery for each new batch should be included in planned downtime.
Quality Checks: Time for quality control procedures between batches may be considered part of planned production time or planned downtime, depending on the specific process.
Cleaning Procedures: Time for cleaning equipment between batches is typically considered planned downtime.
Material Handling: Time for loading raw materials and unloading finished products between batches may be part of planned production time or downtime, based on the specific process.

Example Calculation

Let’s consider a single 8-hour shift in a batch manufacturing facility:

Total Time: 8 hours
Planned Downtime:
Scheduled breaks: 30 minutes
Equipment setup for new batch: 45 minutes
Cleaning between batches: 15 minutes

Planned Production Time = 8 hours – (0.5 + 0.75 + 0.25) hours
= 8 hours – 1.5 hours
= 6.5 hours

In this example, the planned production time for the shift is 6.5 hours.

Metrics Scoring

As I develop metrics for FUSE, it is important to have a method rating a metric for effectiveness. Here’s the rubric I’ll be using.

	Relevance	Measurability	Precision	Actionability	Presence of Baseline
Rating Scale	How strongly does this metric connect to business objectives?	How much effort would it take to track this metric?	How often and by what margin does the metric change?	Can we clearly articulate actions we would take in response to this metric?	Does internal or external baseline data exist to indicate good/poor performance for this metric?
5	Empirically Direct – Data proves the metric directly supports at least one business objective	Almost None – Data already collected and visualized in a centralized system	Highly Predictable -– Metric fluctuates narrowly and infrequently	Clear consensus on action, and capability currently exists to take action	Baseline can be based on both internal and external data
4	Logically Direct – Clear logic shows how the metric directly supports at least one business objective	Low – Data collected and measured consistently, but not aggregated in central system.	Somewhat Predictable – Metric fluxtuates either narrowly or infrequently	Some consensus on action, and capability currently exists to take action	Baseline can be based on either internal or external data
3	Empirically Indirect – Data proves the metric indirectly supports at least one business objective	Medium – Data exists but in local systems, minor collection or measurement challenges may exist.	Neither Volatile or Predictable	Some consensus on action, and capability to take action expected in the future	Baseline must be based on incomplete or directional data
2	Logically Indirect – Clear logic shows how the metric indirectly supports at least one business objective	High – Inconsistent measurements across sites, data not being collected regularly.	Somewhat Volatile – Metric fluctuates either widely or frequently	Some consensus on action, but no current or expected future capability to take action	No data exists to establish baseline, but data can be generated within six months
1	Unclear – Connection to business objective is unclear	Potentially Prohibitive – No defined measurement or collection method in place.	Highly Volatile – Metric fluctuates widely and frequently	No consensus on action	No data exists to establish baseline, and data needed will take more than a year to generate
Weights	25%	20%	20%	25%	10%

Metrics Plan for Facility, Utility, System and Equipment

As October rolls around I am focusing on 3 things: finalizing a budget; organization design and talent management; and a 2025 metrics plan. One can expect those three things to be the focus of a lot of my blog posts in October.

Go and read my post on Metrics plans. Like many aspects of a quality management system we don’t spend nearly enough time planning for metrics.

Metrics Plan

So over the next month I’m going to develop the strategy for a metrics plan to ensure the optimal performance, safety, and compliance of our biotech manufacturing facility, with a focus on:

Facility and utility systems efficiency
Equipment reliability and performance
Effective commissioning, qualification, and validation processes
Robust quality risk management
Stringent contamination control measures

Following the recommended structure of a metrics plan, here is the plan:

Rationale and Desired Outcomes

Implementing this metrics plan will enable us to:

Improve overall facility performance and product quality
Reduce downtime and maintenance costs
Ensure regulatory compliance
Minimize contamination risks
Optimize resource allocation

Metrics Framework

Our metrics framework will be based on the following key areas:

Facility and Utility Systems
Equipment Performance
Commissioning, Qualification, and Validation (CQV)
Quality Risk Management (QRM)
Contamination Control

Success Criteria

Success will be measured by:

Reduction in facility downtime
Improved equipment reliability
Faster CQV processes
Decreased number of quality incidents
Reduced contamination events

Implementation Plan

Steps, Timelines & Milestones

Develop detailed metrics for each key area (Month 1)
Implement data collection systems (Month 2)
Train personnel on metrics collection and analysis (Month 3)
Begin data collection and initial analysis (Month 4)
Review and refine metrics (Month 9)
Full implementation and ongoing analysis (Month 12 onwards)

This plan gets me ready to evaluate these metrics as part of governance in January of next year.

Management Review – a Structured Analysis of Reality

In October I will breakdown some metrics, explaining them and provide the rationale, and demonstrate how to collect. I’ll be striving to break these metrics into key performance indicators (KPI), key behavior indicators (KBI) and key risk indicators (KRI).