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:

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.

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:

  1. 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.
  2. Performance: Anything that causes the manufacturing process to run at less than the maximum possible efficiency when it is running.
  3. 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:

  1. It provides a comprehensive view of manufacturing productivity.
  2. It helps identify losses and areas for improvement.
  3. It serves as a benchmark for comparing performance across different equipment or production lines.
  4. 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

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

Improving OEE

To improve OEE, manufacturers can:

  1. Implement preventive maintenance programs
  2. Optimize changeover procedures
  3. Enhance operator training
  4. Use real-time monitoring systems
  5. Analyze root causes of downtime and quality issues
  6. 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:

AttributeMeaningScoreWhat this means in My Organization
RelevanceHow strongly does this metric connect to business objectives?5Empirically Direct – Data proves the metric directly supports at least one business objective – the ability to meet client requirements
MeasurabilityHow much effort would it take to track this metric?3Medium – 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.
PrecisionHow often and by what margin does the metric change?5Once we agree on the metric and how to measure it, it should be Highly Predictable
ActionabilityCan we clearly articulate actions we would take in response to this metric?4Some consensus on action, and capability currently exists to take action. This metric will be used to drive consensus.
Presence of BaselineDoes internal or external baseline data exist to indicate good/poor performance for this metric?3Baseline 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

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

Improving Equipment Availability

To increase equipment availability, consider the following strategies:

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

Relationship to Other Metrics

Equipment availability is closely related to other important manufacturing metrics:

  1. It’s one of the three components of OEE, alongside Performance and Quality.
  2. It’s distinct from but related to equipment reliability, which measures the probability of failure-free operation.
  3. 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:

  1. Batch Changeovers: Time allocated for switching between different product batches must be accounted for as planned downtime.
  2. Equipment Setup: The time required to configure machinery for each new batch should be included in planned downtime.
  3. 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.
  4. Cleaning Procedures: Time for cleaning equipment between batches is typically considered planned downtime.
  5. 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.