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.
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.
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
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.
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:
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:
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:
Investigations of a Dog 