Tom Gauld on Kafka’s book for toddlers

I do enjoy some Kafka-adjacent humor on a Sunday morning.

Risk Assessments as part of Design and Verification

Facility design and manufacturing processes are complex, multi-stage operations, fraught with difficulty. Ensuring the facility meets Good Manufacturing Practice (GMP) standards and other regulatory requirements is a major challenge. The complex regulations around biomanufacturing facilities require careful planning and documentation from the earliest design stages.

Which is why consensus standards like ASTM E2500 exist.

Central to these approaches are risk assessment, to which there are three primary components:

An understanding of the uncertainties in the design (which includes materials, processing, equipment, personnel, environment, detection systems, feedback control)
An identification of the hazards and failure mechanisms
An estimation of the risks associated with each hazard and failure

Folks often get tied up on what tool to use. Frankly, this is a phase approach. We start with a PHA for design, an FMEA for verification and a HACCP/Layers of Control Analysis for Acceptance. Throughout we use a bow-tie for communication.

Aspect	Bow-Tie	PHA (Preliminary Hazard Analysis)	FMEA (Failure Mode and Effects Analysis)	HACCP (Hazard Analysis and Critical Control Points)
Primary Focus	Visualizing risk pathways	Early hazard identification	Potential failure modes	Systematically identify, evaluate, and control hazards that could compromise product safety
Timing in Process	Any stage	Early development	Any stage, often design	Throughout production
Approach	Combines causes and consequences	Top-down	Bottom-up	Systematic prevention
Complexity	Moderate	Low to moderate	High	Moderate
Visual Representation	Central event with causes and consequences	Tabular format	Tabular format	Flow diagram with CCPs
Risk Quantification	Can include, not required	Basic risk estimation	Risk Priority Number (RPN)	Not typically quantified
Regulatory Alignment	Less common in pharma	Aligns with ISO 14971	Widely accepted in pharma	Less common in pharma
Critical Points	Identifies barriers	Does not specify	Identifies critical failure modes	Identifies Critical Control Points (CCPs)
Scope	Specific hazardous event	System-level hazards	Component or process-level failures	Process-specific hazards
Team Requirements	Cross-functional	Less detailed knowledge needed	Detailed system knowledge	Food safety expertise
Ongoing Management	Can be used for monitoring	Often updated periodically	Regularly updated	Continuous monitoring of CCPs
Output	Visual risk scenario	List of hazards and initial risk levels	Prioritized list of failure modes	HACCP plan with CCPs
Typical Use in Pharma	Risk communication	Early risk identification	Detailed risk analysis	Product Safety/Contamination Control

At BOSCON this year I’ll be talking about this fascinating detail, perhaps too much detail.

NIIMBL Experience

Not sure how many students read this, but here is an exciting opportunity.

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Key program goals:

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Offer exposure to the biopharma manufacturing ecosystem

Student applications for all seven NIIMBL eXperience 2025 locations are now open. Explore your career possibilities in the biopharma industry. Apply now. The application deadline is February 7, 2025.

Apply for the NIIMBL eXperience

Handling Standard and Normal Changes from GAMP5

The folks behind GAMP5 are perhaps the worst in naming things. And one of the worse is the whole standard versus normal changes. Maybe when naming two types of changes do not use strong synonyms. Seems like good advice in general, when naming categories don’t draw from a list of synonyms.

Computer system changes and the GAMP5 framework

Based on the search results, here are the key differences between a standard change and a normal change in GAMP 5:

Standard Change

Pre-approved changes that are considered relatively low risk and performed frequently.
Follows a documented process that has been reviewed and approved by Change Management.
Does not require approval each time it is implemented.
Often tracked as part of the IT Service Request process rather than the GxP Change Control process.
Can be automated to increase efficiency.
Has well-defined, repeatable steps.

So a standard change is one that is always done the same way, can be proceduralized, and is of low risk. In exchange for doing all that work, you get to do them by a standard process without the evaluation of a GxP change control, because you have already done all the evaluation and the implementation is the same every single time. If you need to perform evaluation or create an action plan, it is not a standard change.

Normal Change

Any change that is not a Standard change or Emergency change.
Requires full Change Management review for each occurrence.
Raised as a GxP Change Control.
Approved or rejected by the Change Manager, which usually means Quality review.
Often involves non-trivial changes to services, processes, or infrastructure.
May require somewhat unique or novel approaches.
Undergoes assessment and action planning.

The key distinction is that Standard changes have pre-approved processes and do not require individual approval, while Normal changes go through the full change management process each time. Standard changes are meant for routine, low-risk activities, while Normal changes are for more significant modifications that require careful review and approval.

What About Emergency Changes

An emergency change is a change that must be implemented immediately to address an unexpected situation that requires urgent action to:

Ensure continued operations
Address a critical issue or crisis

Key characteristics of emergency changes in GAMP 5:

They need to be expedited quickly to obtain authorization and approval before implementation.
They follow a fast-track process compared to normal changes.
A full change control should be filed for evaluation within a few business days after execution.
Impacted items are typically withheld from further use pending evaluation of the emergency change.
They represent a situation where there is an acceptable level of risk expected due to the urgent nature.
Specific approvals and authorizations are still required, but through an accelerated process.
Emergency changes may not be as thoroughly tested as normal changes due to time constraints.
A remediation or back-out process should be included in case issues arise from the rapid implementation.
The goal is to address the critical situation while minimizing impact to live services.

The key difference from standard or normal changes is that emergency changes follow an expedited process to deal with urgent, unforeseen issues that require immediate action, while still maintaining some level of control and documentation. However, they should still be evaluated and fully documented after implementation.

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.