Quality metrics are quantifiable measurements used to assess if products, services, or processes meet defined targets. Quality metrics play a central role in quality management systems (QMS) because they monitor compliance, efficiency, timeliness, risks, and overall performance. Life science organizations, specifically, use quality metrics to maintain regulatory compliance, improve product or service quality, and drive operational excellence.
The main categories of quality metrics are listed below.
- 1. Document Control Metrics: Track document control compliance and timeliness using metrics such as document approval cycle time, number of overdue documents, and review schedule adherence.
- 2. Training Metrics: Measure employee training effectiveness, including completion rates, training time, and knowledge transfer success.
- 3. Non-Conformance and Deviation Metrics: Monitor the frequency and resolution of quality defects across batches and products.
- 4. Complaint and Customer Feedback Metrics: Evaluate how well customer complaints are handled, resolved, and prevented from recurring.
- 5. CAPA Metrics: Assess the performance of corrective actions and preventive actions (CAPA), using indicators like on-time closure and CAPA effectiveness.
- 6. Audit Metrics: Monitor the responsiveness and preparedness of the audit process, using the number of critical and major findings, observation closure time, and recurrence rate.
- 7. Change Control Metrics: Measure success, schedule adherence, and impact associated with implemented changes across the organization.
- 8. Supplier Quality Metrics: Evaluate supplier reliability and quality through delivery performance and supplier-related quality issues.
- 9. Risk Management Metrics: Track risk identification and mitigation effectiveness to ensure quality and operational risks are addressed.
- 10. Process Quality Metrics: Analyze process data, including metrics like right first time (RFT), yield, and process performance indices, to evaluate process performance.
A QMS software plays a vital role in capturing and reporting quality metrics. An electronic quality management system (eQMS) captures data across different quality processes, such as document control, CAPA, audit, training, and supplier management, providing reports and supporting the ability to make data-driven decisions.
1. Document Control Metrics
Document control metrics help track how effectively an organization creates, approves, reviews, and updates any controlled document, like SOPs, work instructions, or specifications.
Common document control metrics include the following.
- Document Approval Cycle Time: Measures the average time taken from document submission to final approval, reflecting workflow efficiency.
- Number of Overdue Documents: Tracks the count of controlled documents that have missed scheduled review, approval, or training deadlines, indicating process delays.
- Percentage of Documents Reviewed on Schedule: Shows the proportion of documents reviewed within the assigned timeframe, supporting continuous audit readiness and control.
Other document control metrics are document retrieval time, version control errors, and change request backlog. These document control metrics monitor access speed, accuracy in revisions, and pending updates in regulated environments.
Document Approval Cycle Time
Document approval cycle time monitors the total time taken for a controlled document to move from initial draft creation to final approval. Document approval cycle time captures workflow efficiency and identifies delays in the document lifecycle.
Key inputs for calculating the document approval cycle time include the document creation timestamp, durations of each review phase, and final approval time. Timestamps can be easily tracked in an electronic quality management system, such as SimplerQMS.
Document approval cycle time is calculated using the following formula.
Approval Time – Creation Time = Cycle Time
Shorter cycle times suggest streamlined processes; longer ones may indicate bottlenecks.
In practice, this metric helps quality teams monitor performance and review turnaround times. For organizations, minimizing approval delays reduces regulatory risk and accelerates the implementation of critical procedures.
Number of Overdue Documents
The number of overdue documents quantifies how many controlled documents have exceeded their scheduled review or revision due dates.
Information such as the document’s last review date, the defined review interval, and the current date is required to calculate the number of overdue documents.
The number of overdue documents is calculated using the following formula.
Sum of Documents where Current Date > (Last Review Date + Review Interval) = Number of Overdue Documents
The number of overdue documents indicates whether an organization is keeping its documentation current and compliant with internal procedures or regulatory timelines. A high number signals process neglect or resource gaps, while a low number indicates effective documentation maintenance.
Tracking the number of overdue documents helps prevent audit findings and reduces the risk of using outdated procedures.
Percentage of Documents Reviewed on Schedule
The percentage of documents reviewed on schedule measures the proportion of controlled documents that were reviewed by their assigned due dates.
The inputs to calculate the percentage of documents reviewed on schedule include the total number of documents due for review and the subset completed on time.
The percentage of documents reviewed on schedule is given by the formula below.
(Number of Documents Reviewed on or Before Due Date ÷ Total Documents Due for Review) × 100 = Percentage of Documents Reviewed on Schedule
Monitoring the percentage of documents reviewed on schedule helps identify review process reliability and team accountability. A high percentage of documents reviewed on time reflects adherence to quality processes.
The percentage of documents reviewed on time is directly related to the percentage of overdue documents. Documents reviewed on schedule support audit readiness and reduce the risk of using outdated procedures.
2. Training Metrics
Training metrics are used to evaluate how well an organization delivers, tracks, and benefits from employee training activities. Training metrics monitor if the personnel remain competent to perform their assigned tasks.
The main training metrics are listed below.
- Training Completion Rate: Monitors the percentage of training assignments that employees have completed, indicating compliance with internal procedures and regulatory requirements.
- Time to Training Completion: Measures the average time employees take to complete assigned training, reflecting efficiency in the training process.
- Training Effectiveness Score: Evaluates how well training transfers to actual job performance, often gathered through assessments or supervisor feedback.
Other common training metrics include training return on investment (ROI) and employee feedback and satisfaction. ROI assesses the financial return compared to the cost of training. The employee feedback and satisfaction captures learner perceptions and engagement through surveys.
Training Completion Rate
The training completion rate is an indicator of the proportion of assigned training courses completed by employees within the predefined period.
The training completion rate is based on the number of completed training assignments and the total number of assigned training.
The training completion rate is calculated using the formula below.
(Number of Trainings Completed ÷ Number of Trainings Assigned) × 100 = Training Completion Rate
A high training completion rate reflects workforce readiness, while a low rate signals potential compliance gaps or process inefficiencies. Training completion rate is used to monitor not only training schedule adherence, but also personnel qualification status.
Time to Training Completion
Time to training completion tracks the average duration employees take to complete assigned training, reflecting training process efficiency, accessibility of training material, and personnel accountability.
The main inputs used for the calculation of time to training completion are the training assignment date and the completion date for each employee. Shorter times indicate streamlined training processes, while longer durations may reveal training complexity, resource, or engagement issues.
The time to training completion is calculated based on the following formula.
Sum of Individual Training Completion Duration ÷ Number of Completed Trainings = Average Time to Training Completion
The time to training completion metric helps identify delays, assess onboarding speed, and optimize training schedules. Monitoring the time to training completion metric supports the timely workforce qualification.
Training Effectiveness Score
The training effectiveness score measures how well a training program improves employee knowledge and job performance. The training effectiveness score evaluates whether learning objectives are met and if the training translates into practical skills on the job.
The inputs needed for the training effectiveness score may include competency assessments, on-the-job evaluations, supervisor feedback, or observed performance improvements.
The training effectiveness score can be calculated using a weighted scoring system based on multiple inputs.
The training effectiveness score is used to identify any skill gaps or need for training program improvements. A high score indicates that employees learn effectively, reducing errors and improving quality outcomes.
Assessing the training effectiveness ensures that training investments lead to real operational improvements.
3. Non-Conformance and Deviation Metrics
Non-conformance and deviation metrics are indicators used to monitor and evaluate incidents where processes, products, or materials fail to meet defined specifications or requirements. Non-conformance and deviation metrics help identify process weaknesses, support corrective actions, and maintain compliance.
The most common non-conformance and deviation metrics are given below.
- Number of Non-Conformances or Deviations per Batch/Product: Tracks how many quality issues are recorded for a specific batch or product, signaling potential manufacturing or analytical process inconsistencies.
- Deviations/Non-Conformances Closed On Time: Measures the percentage of quality defects resolved within the defined due date, reflecting deviation or non-conformance management efficiency and procedure adherence.
- Percentage of Non-Conformances or Deviations Leading to CAPAs: Shows how many incidents require corrective action, linking quality incident management to the effectiveness of root cause analysis.
Additional non-conformance and deviation metrics are the recurrent non-conformance/deviation rate, the non-conformances/deviations attributed to human error, and the classification of non-conformance/deviation. Recurrent non-conformance/deviation rate highlights repeated issues over time. Non-conformances/deviations attributed to human error reveal training gaps, unclear procedures, or other underlying systematic weaknesses. The classification of non-conformance/deviation categorizes quality defects by severity or type.
Number of Non-Conformances or Deviations per Batch/Product
The number of non-conformances or deviations per batch or per product indicates the frequency of quality issues during the processing of the specific batch or product. The number of non-conformances or deviations metric reflects process reliability and product consistency.
The number of non-conformances or deviations per batch is the number of quality incidents that occurred during the manufacturing, analysis, or distribution of a specific batch.
The number of non-conformities or deviations per product counts the total number of incidents that occurred during a defined timeframe (e.g., yearly). To calculate the incident rate, divide the total number of incidents during a specific timeframe by the batches produced during the same timeframe.
The number of non-conformances or deviations per batch or product metric is used to compare quality trends across products, production lines, or time periods. A high count may signal poor process control, equipment malfunction, or supplier issues.
Monitoring the number of non-conformances or deviations per batch or per product helps identify quality defect trends early and enforce process discipline.
Deviations/Non-Conformances Closed On Time
The percentage of deviations or non-conformances closed on time is the percentage of recorded quality issues resolved within their assigned due dates.
The key inputs to calculate the percentage of deviations or non-conformances closed on time are the total number of deviations or non-conformances and the number resolved on or before their due dates. The due dates are assigned according to the respective procedure for deviation or non-conformance management.
The deviations or non-conformances closed on time are calculated based on the formula below.
(Number of Deviations or Non-Conformances Closed On Time ÷ Total Number of Deviations or Non-Conformances) × 100 = Percentage Deviations/ Non-conformances Closed On Time
A high percentage indicates effective deviation or non-conformance management and compliance discipline. A low percentage may signal delays, resource constraints, or process inefficiencies.
Tracking the number of deviations or non-conformances closed on time metric helps maintain control over quality defects and reduces compliance risks.
Percentage of Non-Conformances or Deviations Leading to CAPAs
The percentage of non-conformances or deviations leading to CAPAs measures how many quality incidents require corrective or preventive actions, indicating the depth of root cause analysis and severity of incidents.
Inputs for calculating the percentage of non-conformances or deviations that lead to CAPA include the total number of non-conformances or deviations closed within a specific timeframe (e.g., monthly) and the number that resulted in CAPA initiation.
The percentage of deviations or non-conformances leading to CAPAs is calculated by the following formula.
(Number of Non-Conformances or Deviations Leading to CAPAs ÷ Total Number of Non-Conformances or Deviations) × 100 = Percentage of Non-conformances/ Deviations Leading to CAPAs
A higher percentage of non-conformances or deviations leading to CAPAs suggests that significant issues are being addressed systematically, while a very low percentage may indicate insufficient root cause analysis or missed opportunities for improvement.
The percentage of deviations or non-conformances leading to CAPAs is used to ensure that critical issues are escalated appropriately and support continuous process improvement.
4. Complaint and Customer Feedback Metrics
Complaint and customer feedback metrics are quantifiable measures used to track, evaluate, and improve how an organization manages product complaints and overall customer satisfaction. Complaint and customer feedback metrics help companies enhance product or service quality and customer retention.
Commonly used complaint and customer feedback metrics are listed below.
- Complaints Closed On Time: Measures the percentage of customer complaints resolved within the defined timeframe.
- Complaints per Product/Service per Year: Tracks how many complaints are received annually for a specific product or service, indicating performance and customer experience.
- Recurrent Complaint Rate: Calculates how often the same issue is reported again after an initial resolution, highlighting unresolved root causes.
Other complaint and customer feedback metrics are first contact resolution rate, complaint volume by category, and the recall rate. First contact resolution measures the percentage of complaints resolved during the first interaction without requiring follow-up. Complaint volume by category groups issues by type to identify recurring issues and trends. Recall rate tracks the frequency of product recalls resulting from critical quality complaints.
Complaints Closed On Time
The percentage of complaints closed on time measures the percentage of customer complaints resolved within a specified timeframe, reflecting the complaint handling process adherence and efficiency.
The percentage of complaints closed on time metric is calculated using the following formula.
(Number of Complaints Closed On or Before Due Date ÷ Total Number of Complaints) × 100 = Percentage of Complaints Closed On Time
Practically, a high percentage reflects timely and efficient complaint management, while a low percentage suggests workflow delays or resource constraints.
Monitoring the percentage of complaints closed on time is important because it helps maintain customer trust and prevent the escalation of unresolved issues.
Complaints per Product/Service per Year
The complaints per product or service per year metric tracks the rate of complaints received for a specific product or service within 12 months, highlighting quality trends and potential performance issues.
Inputs for calculating the complaints per product or service per year include the number of complaints received and the number of dispatched batches of the product or service in a year.
Complaints per product or service per year are calculated using the following formula.
(Number of Complaints per Product or Service ÷ Total Units Delivered Annually) = Complaints per Product per Year
The complaints per product or service per year metric helps identify underperforming products or services. Consistent monitoring of complaints per product or service supports continuous improvement and helps meet regulatory surveillance requirements in the life sciences industries.
Recurrent Complaint Rate
Recurrent complaint rate measures the percentage of complaints that are repeated for the same process, product, or root cause after resolution.
The components of the recurrent complaint rate are the total number of complaints and the subset classified as recurrent issues.
The recurrent complaint rate is calculated by the following formula.
(Number of Recurrent Complaints ÷ Total Complaints) × 100 = Recurrent Complaint Rate
A low rate suggests effective complaint handling and resolution, while a high rate signals unresolved quality gaps.
The recurrent complaint rate helps companies detect failures in complaint investigations, inadequate CAPAs, or insufficient root cause analyses. Monitoring this rate strengthens product quality and safety and reduces regulatory and reputational risks.
5. CAPA Metrics
CAPA metrics are measurable indicators used to evaluate the performance of the corrective and preventive action process in the quality management system. CAPA metrics help assess how well an organization identifies, resolves, and prevents quality issues.
CAPA metrics typically include the following.
- Average CAPA Closure Time: Measures the average time taken to implement a CAPA from initiation to closure.
- CAPA Effectiveness Rate: Tracks the percentage of CAPAs that successfully prevent issue recurrence or improve a process after implementation.
- Percentage of Overdue CAPAs: Shows how many CAPAs remain open past their assigned due dates.
Other CAPA-related metrics are the ratio of corrective vs preventive actions, regulatory audit CAPA compliance, and open CAPA aging. The ratio of corrective vs preventive actions indicates whether the quality management system is reactive or proactive. Regulatory audit CAPA compliance measures the effectiveness of CAPAs specifically linked to inspection findings. Open CAPA aging monitors how long unresolved CAPAs remain open and assesses the overall effectiveness of the CAPA management process.
Average CAPA Closure Time
Average CAPA closure time tracks how long it typically takes to implement corrective or preventive actions, offering insight into the efficiency of quality incident resolution and process flow.
CAPA closure time is determined by calculating the number of days between the CAPA initiation date and the closure date for each CAPA. Shorter closure times reflect strong operational control, while longer durations may signal review delays, implementation issues, or resource constraints.
The average CAPA closure time is calculated using the formula below.
Sum of Individual CAPA Durations ÷ Total Number of Closed CAPAs = Average CAPA Closure Time
CAPA cycle time is used to identify bottlenecks, evaluate resource efficiency, and ensure timely responses to quality issues. The average CAPA closure time metric is an indicator of audit readiness and continuous improvement.
CAPA Effectiveness Rate
CAPA effectiveness rate evaluates the percentage of CAPAs that successfully resolved the identified issue without recurrence or measurably improved a process.
CAPA effectiveness rate inputs are the total number of implemented CAPAs within a specific time frame (e.g., a year) and the subset verified as successful or effective after follow-up.
CAPA effectiveness rate is calculated using the following formula.
(Number of Effective CAPAs ÷ Total Implemented CAPAs) × 100 = CAPA Effectiveness Rate
CAPA effectiveness rate is used to assess the quality of root cause investigations and the practical success of implemented action plans. A high percentage reflects a robust CAPA process and thorough root cause analysis, while a low rate points to weak analysis or incomplete solutions.
Tracking CAPA effectiveness helps sustain compliance and continuously improve the QMS.
Percentage of Overdue CAPAs
The percentage of overdue CAPAs tracks the share of actions not finalized within their assigned due dates, highlighting delays in the CAPA implementation process.
The percentage of overdue CAPAs measures the proportion of CAPAs that are not completed by their assigned due date within a specific timeframe, usually monthly.
The percentage of overdue CAPAs is calculated using the following formula.
(Number of Overdue CAPAs ÷ Total CAPAs) × 100 = Percentage of overdue CAPAs
A high or increasing percentage may indicate limited resources, poor prioritization, or process inefficiencies, while a lower value reflects timely follow-through and effective tracking.
Regular review of overdue CAPAs helps companies maintain compliance and ensures ongoing attention to unresolved quality concerns. The monitoring of overdue CAPAs supports the identification of backlogs, evaluation of prioritization practices, and effective workload distribution.
6. Audit Metrics
Audit metrics are quantifiable indicators used to assess the outcomes and responsiveness of quality audits. Audit metrics assist companies in monitoring compliance and audit readiness.
The key audit metrics are listed below.
- Number of Major and Critical Audit Findings per Audit: Measures the severity and the frequency of significant audit observations.
- Time to Close Audit Findings: Tracks how long it takes to address and resolve identified findings after the audit is completed.
- Recurrent Findings Rate: Measures the proportion of repeated issues from previous audits, reflecting the effectiveness of root cause investigation and corrective actions.
Other commonly used audit metrics include the inspection readiness score, the audit coverage rate, and the CAPA initiation time. The inspection readiness score evaluates the company’s preparedness for regulatory audits. The audit coverage rate measures the percentage of audited processes or departments. CAPA initiation time tracks how quickly an investigation is completed, and corrective actions are initiated following audit findings.
Number of Major and Critical Audit Findings per Audit
The number of major and critical audit findings per audit indicates how many significant quality issues are detected during each audit. The number of major and critical findings reflects the severity of compliance gaps or operational risks that require immediate attention.
The inputs used to calculate the number of major and critical audit findings include the total findings of an audit classified by severity.
A consistently high count of major and critical findings suggests systemic failures, compliance gaps, or ineffective remediation practices. Lower values indicate that processes are stable, and regulatory expectations are being met.
Tracking the number of major and critical findings helps organizations prioritize improvements, allocate resources more effectively to higher-risk areas, and prepare for regulatory inspections.
Time to Close Audit Findings
Time to close audit findings measures the cycle time between the identification of an audit observation and the closure of the corresponding remediation action, showing the speed and responsiveness of the audit follow-up process.
Key inputs of time to close audit findings metric are the date of each audit finding and the corresponding closure date recorded in the quality system.
The time to close audit findings is calculated using the following formula.
Sum of Individual Closure Durations for All Findings ÷ Total Number of Findings Closed = Average Time to Close Audit Findings
Measuring the audit findings cycle time helps identify delays in root cause analysis, corrective actions implementation, or the finalization of documentation. Extended closure times may indicate resource shortages or poor prioritization of audit observation resolution.
Consistently monitoring the audit findings cycle time supports inspection readiness and helps ensure compliance is restored in a timely manner.
Recurrent Findings Rate
Recurrent findings rate tracks the proportion of audit observations that recur across multiple audits, indicating unresolved systemic weaknesses.
To determine the recurrent audit findings rate, all audit findings are reviewed to identify the subset that matches previous issues based on root cause, process, or product. A high rate of recurrent findings signals that corrective actions are either inadequate or poorly implemented. A low rate reflects sustained compliance improvement and effective resolution of prior issues.
The recurrent findings rate is calculated using the following formula.
(Number of Repeat Findings ÷ Total Findings) × 100 = Recurrent Findings Rate
Monitoring the recurrent audit findings rate helps organizations verify the long-term effectiveness of CAPAs and reduce compliance risks.
7. Change Control Metrics
Change control metrics are measurable values used to evaluate the efficiency, compliance, and effectiveness of an organization’s process for managing modifications to products, processes, systems, or documentation. Change control metrics monitor how well changes are planned, approved, implemented, and reviewed for their effectiveness in regulated environments.
The most common change control metrics are outlined below.
- Change Control Effectiveness Rate: Measures the percentage of implemented changes that achieved their intended purpose without introducing new quality issues.
- Schedule Variance: Compares the planned timeline for change implementation against the actual completion date.
- Change Backout Rate: Tracks the proportion of changes that had to be reversed due to failure or adverse impact.
Additional change control metrics include unauthorized changes, change acceptance rate, and backlog aging. Unauthorized changes metric counts the number of changes made without proper approval. The change acceptance rate indicates the percentage of change requests that are formally approved after review. The backlog aging metric measures the length of time change requests remain open and unresolved.
Change Control Effectiveness Rate
Change control effectiveness rate quantifies how often implemented changes achieve their intended objectives without introducing new unmanaged risks.
The components of change control effectiveness rate are the total number of changes implemented and the number that were verified as successful through post-implementation review.
Change control effectiveness rate is calculated using the following formula.
(Number of Effective Changes ÷ Total Changes Implemented) × 100 = Change Control Effectiveness Rate
A high rate indicates well-controlled change management practices, including robust risk assessment and impact analysis. A low rate suggests gaps in risk evaluation or poor process understanding.
Regular monitoring of the change control effectiveness rate helps improve change management and reduce disruption due to unintended consequences of the change. In the pharmaceutical industry, a change control effectiveness review is required by ICH Q10 to demonstrate that all modifications are properly controlled.
Schedule Variance
Schedule variance measures the difference between the planned and actual completion dates of a change control process, indicating adherence to implementation timelines. The inputs to calculate the schedule variance include the baseline planned due date and the actual date the last required action to implement the change was completed.
The formula used to calculate the schedule variance is outlined below.
Actual Completion Date – Planned Completion Date = Schedule Variance
A positive value reflects a delay, while a negative value shows early completion.
In practice, small variances may be acceptable depending on complexity, but frequent or significant delays suggest poor planning, resource issues, or an ambiguous implementation plan.
Monitoring schedule variance helps improve the change implementation process by allocating resources effectively and maintaining control over project timelines.
Change Backout Rate
The change backout rate is the proportion of changes that had to be reversed during or after implementation due to failure or negative impact. The change backout rate reflects the effectiveness of change management, particularly during impact analysis and preparation of the implementation plan.
Key inputs to measure the change backout rate include the total number of implemented changes and the number that required rollback.
The formula used for the calculation of the change backout rate is given below.
(Number of Changes Rolled Back ÷ Total Changes Implemented) × 100 = Change Backout Rate
A low change backout rate indicates a well-managed change control process, while a high rate points to poor risk evaluation or insufficient change implementation planning.
The change backout rate complements the change control effectiveness rate and signals whether the change process introduces unintended risks. Monitoring change backouts helps organizations refine their change control process and build a robust change management system that minimizes disruption and ensures compliance.
8. Supplier Quality Metrics
Supplier quality metrics are measurable values used to evaluate supplier performance in delivering materials or services that meet predefined quality and compliance requirements. Supplier quality metrics help monitor supplier reliability, consistency, and their potential impact on the quality system.
The most typically applied supplier quality metrics are the following.
- Supplier Defect Rate: Tracks the percentage of received goods or components that fail inspection or do not meet specifications.
- On-Time Delivery Rate: Measures the percentage of shipments received by the agreed delivery dates.
- Number of Supplier-Related Non-Conformances: Counts the quality issues directly attributed to supplied materials or services.
Additional supplier-related metrics include lead time variability, compliance rate, and audit performance score. The lead time variability evaluates delivery consistency. The compliance rate measures adherence to regulatory and contractual requirements. The audit performance score reflects outcomes from supplier audits.
Supplier Defect Rate
Supplier defect rate measures the proportion of incoming materials or services from a specific supplier that fail to meet predefined requirements, indicating the supplier’s consistency and reliability.
Components of the supplier defect rate include the total number of batches received and the number of defective or rejected batches.
Supplier defect rate is calculated using the following formula.
(Number of Defective Batches ÷ Total Batches Received) × 100 = Supplier Defect Rate
Tracking the supplier defect rate enables a company to identify high-risk suppliers. A lower defect rate indicates a consistent and reliable supplier, while a higher defect rate may lead to production delays or product defects.
On-Time Delivery Rate
The on-time delivery rate tracks the percentage of supplier deliveries that arrive by the agreed delivery date, reflecting supply chain reliability and planning accuracy.
The inputs used to measure the on-time delivery rate include the total number of deliveries and the number received on or before the scheduled delivery date.
The on-time delivery rate is calculated by the formula below.
(On-Time Deliveries ÷ Total Deliveries) × 100 = On-Time Delivery Rate
A consistently high on-time delivery rate maintains efficient operations, while frequent delays may indicate systemic supplier issues that require corrective action.
Monitoring the on-time delivery rate helps reduce production disruptions and improve scheduling accuracy.
Number of Supplier-Related Non-Conformances
The number of supplier-related non-conformances monitors how often quality issues are traced back to materials or services provided by suppliers. The number of supplier-related non-conformances indicates the extent to which external sources contribute to deviations from specifications or processes and product non-conformities.
To accurately measure the number of supplier-related non-conformances, documented non-conformance records and their root cause analyses must clearly link issues to supplier performance.
The number of supplier-related non-conformances is calculated by counting all non-conformances attributed to a specific supplier over a defined period.
Frequent supplier-related issues should trigger corrective actions, including reassessment of supplier status. Monitoring the number of supplier-related quality defects supports effective supplier monitoring and guides audit planning.
9. Risk Management Metrics
Risk management metrics are measurable indicators used to track how well an organization identifies, evaluates, and mitigates potential threats to product or service quality, or regulatory compliance.
The main risk management metrics are described below.
- Number of Identified High-Risk Items: Counts the total risks rated as high severity, high likelihood, or low detectability, helping prioritize mitigation efforts.
- Effectiveness of Risk Mitigation: Measures the reduction in risk level after mitigation actions are implemented, reflecting risk management success.
- Risk Review Frequency Compliance: Tracks how consistently risk assessments are revisited according to defined review schedules.
Additional metrics include regulatory compliance rate, unresolved high-risk issues, and cost of risk management. Regulatory compliance rate assesses alignment with regulatory requirements. Unresolved high-risk issues highlight outstanding threats. The cost of risk management assesses the financial implications of identifying and mitigating risks.
Number of Identified High-Risk Items
The number of identified high-risk items indicates how many risks within a quality system are classified as high based on predefined evaluation criteria.
The risks are identified through formal assessments where severity, likelihood, and detectability are scored to calculate a risk priority number (RPN). The number of identified high-risk items is calculated by counting all items that exceed a threshold RPN or fall within a designated high-risk category.
A high count of high-risk items suggests exposure to unresolved threats, while a low number typically reflects proactive risk control and effective design of processes.
Tracking high-risk areas helps teams focus their risk mitigation efforts, allocate resources effectively, and prevent issues that may compromise product quality or patient safety.
Effectiveness of Risk Mitigations
The effectiveness of risk mitigation measures is the degree to which implemented controls reduce the overall risk level of identified threats. The effectiveness of risk mitigation evaluates whether mitigation actions successfully lower the severity, likelihood, or improve the detectability of a risk.
Inputs used for the calculation of the effectiveness of risk mitigations include pre- and post-mitigation risk ratings based on formal assessments using risk scoring criteria like RPN.
The effectiveness of risk mitigation is typically calculated using the following formula.
((Initial Risk Score – Residual Risk Score) ÷ Initial Risk Score) × 100 = Effectiveness of Risk Mitigations
A higher percentage indicates stronger mitigation performance.
In practice, the effectiveness of risk mitigation confirms whether risk-reduction strategies are working or need to be reassessed.
Risk Review Frequency Compliance
Risk review frequency compliance measures how consistently risk assessments are revisited according to defined intervals, helping ensure that risk mitigating controls remain effective, relevant, and up to date.
The components assessed are the scheduled review frequency for each risk item and the actual review completion dates documented in the quality system.
The risk review frequency compliance is calculated based on the formula below.
(Number of Risks Reviewed On Schedule ÷ Total Number of Risks Due for Review) × 100 = Risk Review Frequency Compliance
High compliance indicates strong oversight and control of evolving risks, while low compliance may expose the organization to undetected changes in risk levels.
Risk review frequency supports improvement and audit readiness by confirming that risks are continuously monitored.
10. Process Quality Metrics
Process quality metrics are quantifiable indicators used to evaluate how well a process performs against defined parameters, focusing on consistency and efficiency.
The most commonly used process quality metrics are the following.
- Process Capability Indices: Measure how well a process performs within specification limits, indicating its ability to consistently produce conforming output.
- Right First Time: Tracks the percentage of batches or processes completed correctly without rework or correction.
- Yield: Compares expected output to actual production to identify losses due to defects or inefficiencies.
Other process quality metrics are cycle time, changeover time, and overall equipment effectiveness. Cycle time measures how long it takes to complete a process. Changeover time evaluates the time needed to switch from one product or process setup to another. Overall equipment effectiveness, which combines availability and performance to assess equipment productivity.
Process Capability Indices
Process capability indices (Cp, Cpk, Pp, Ppk) are statistical metrics used to evaluate how well a process performs concerning its specification limits, focusing on both process spread and centering.
Process capability (Cp) measures the spread of process results, indicating the capability of the process to produce results that are within specification limits. Process capability index (Cpk) measures both the process spread and how centered the process is. Similarly, process performance (Pp) and process performance index (Ppk) assess overall (long-term) process performance, including variation due to parameters such as alternative suppliers or different production lines.
The inputs needed to measure the process capability indices include upper and lower specification limits, process data, and standard deviation.
The formulas used to calculate process capability indices are the following.
(USL – LSL) ÷ (6 × Standard Deviation) = Cp
min[(USL – Mean), (Mean – LSL)] ÷ (3 × Standard Deviation) = Cpk
Pp and Ppk use similar formulas, but with overall standard deviation instead of within-subgroup deviation.
In practice, values above 1.33 are generally considered capable, with higher numbers indicating better consistency.
Process capability indices help identify whether processes can reliably produce within specification limits, revealing process risks and guiding quality improvement and process control.
Right First Time
Right First Time (RFT) is the percentage of batches or processes completed correctly without any need for rework, corrections, or repeats. RFT reflects the degree of process control and standardization.
RFT is calculated based on the total number of batches produced and the number of batches in which a deviation or non-conformance occurred, identifying how many were successfully completed correctly on the first try.
The calculation of RFT is performed according to the following formula.
((Total Batches- Defective Batches) ÷ Total Batches) × 100 = RFT
A high RFT value indicates strong process control and effective training. In practice, this metric helps reduce waste, lower costs, and improve process efficiency.
Monitoring RFT is critical for ensuring consistent product or service quality in regulated environments.
Yield
Yield measures the efficiency of a process by comparing the expected output under ideal conditions to the real output achieved. The yield highlights material losses, process inefficiencies, or quality issues.
The key components to measure yield are the theoretical yield based on the quantity of raw materials and the actual quantity of product recorded.
The yield is calculated using the following formula.
(Actual Output ÷ Theoretical Output) × 100 = Yield
A lower-than-expected yield indicates high losses, rework, or an inconsistent process. High yield supports cost control, consistent product quality, and better resource utilization.
Monitoring yield helps companies identify where losses occur and guides improvement efforts in material usage, equipment performance, or workflow.
What Are Quality Metrics?
Quality metrics are quantifiable measurements used to evaluate how well products, services, or processes meet defined targets. Quality metrics measure aspects such as defect rates, compliance levels, timeliness, efficiency, and consistency.
For products and services, quality metrics track performance indicators like complaint rate, defect frequency, and customer satisfaction. For processes, quality metrics evaluate control, consistency, and effectiveness using metrics like CAPA closure time, training completion rate, process capability, and change implementation success.
Why Are Quality Metrics Important?
Quality metrics are important because they provide objective data to monitor, control, and improve products, services, and processes. Organizations use quality metrics to detect issues early, ensure regulatory compliance, reduce risk, and drive continuous improvement.
Tracking quality metrics supports informed decision-making, improves operational efficiency, and helps maintain customer trust and satisfaction.
What Are the Types of Quality Metrics?
The main types of quality metrics are listed below.
- Process Performance Metrics: Evaluate the efficiency, effectiveness, and consistency of operational workflows, such as cycle time and process yield.
- Quality System Effectiveness Metrics: Assess how well quality processes like CAPA and change control management are implemented across the organization.
- Product Quality Metrics: Measure product-specific attributes like non-conformance and deviation rate or right first time.
- Customer-Related Metrics: Track outcomes tied to customer experience, including complaint rate, customer satisfaction, and first contact resolution.
- Structural/Organizational Metrics: Focus on internal readiness, such as training completion, staffing levels, or audit coverage.
- Compliance Metrics: Monitor adherence to regulatory and internal requirements, including timely audit findings closure and the number of major and critical observations.
- Supply Chain & Supplier Metrics: Measure supplier performance using indicators like on-time delivery, defect rate, and supplier audit performance.
- Manufacturing Quality Metrics: Apply to production environments, downtime, and equipment effectiveness.
- Laboratory Quality Metrics: Apply to testing facilities and may include cycle time, laboratory error rate, and number of unconfirmed Out-Of-Specification (OOS) results.
What Is the Difference Between Quality Metrics and Quality KPIs?
The primary difference between quality metrics and quality key performance indicators (KPIs) is their purpose and connection to strategic goals. Quality metrics are general measurements used to monitor and improve quality-related activities across products, processes, or systems. Quality KPIs are specific, high-impact metrics that reflect how well the organization is achieving its strategic quality objectives and business goals.
Quality KPIs are critical indicators used for executive reporting and strategic goal tracking. While all KPIs are metrics, not all metrics qualify as KPIs. Quality KPIs represent the most essential metrics selected to guide leadership decisions and measure success against defined business targets.
What Is the Role of Quality Metrics in Quality Management Systems?
The role of quality metrics in QMS is to quantify quality performance and enable organizations to monitor, control, and improve their quality system in a structured and measurable way.
Quality metrics support the Plan-Do-Check-Act (PDCA) cycle by setting targets, monitoring the data, identifying gaps, and tracking progress and effectiveness of corrective actions. Quality metrics help to ensure compliance with regulatory requirements and align quality objectives across departments. Quality metrics support decision-making within a quality management system by providing data-driven insights.
A company that sets quality metrics establishes clear benchmarks and targets, facilitates objective reviews and accountability, and promotes transparency across teams. Metrics also play a critical role in meeting regulatory requirements by demonstrating process control and consistency, as well as continuous improvement.
What Are the Benefits of Monitoring Quality Metrics?
The benefits of quality metrics monitoring are outlined below.
- Enhanced Product and Service Quality: Detects risks early, ensuring that products or services will consistently meet specifications and customer expectations.
- Regulatory and Standards Compliance: Demonstrates adherence to life-science requirements and supports audit readiness.
- Informed Decision-Making: Enables data-driven actions based on actual performance trends.
- Stronger Customer Satisfaction: Addresses quality issues effectively and supports a positive customer experience.
- Increased Process Visibility & Control: Highlights performance gaps and supports team accountability.
- Improved Operational Efficiency: Identifies trends and bottlenecks, allowing teams to take corrective actions and streamline workflows.
- Better Risk Management: Identifies possible quality failures and supports timely corrective actions.
- Process Optimization and Continuous Improvement: Sets specific targets, driving long-term improvements.
How to Effectively Implement and Measure Quality Metrics?
To implement and measure quality metrics effectively, a structured and consistent approach is required.
The basic implementation steps for quality metrics are presented below.
- Define Objectives and Scope: Clarify quality objectives that need to be measured and which processes or areas they apply to, as per their scope. Align the purpose of each metric with quality and compliance goals.
- Select Appropriate Metrics: Choose metrics that are relevant, measurable, and actionable for the defined objectives. Prioritize metrics that offer clear insight into quality performance.
- Establish Data Collection Methods: Identify reliable data sources and define standardized procedures for gathering data. Establish clear quality metric calculation formulas to ensure consistency across teams.
- Set Benchmarks and Targets: Define expected performance levels based on historical data, industry standards, or regulatory requirements. Use achievable targets to evaluate success and identify trends.
- Train Teams and Assign Ownership: Ensure staff understand each metric’s purpose, how it is measured, and their role in achieving it. Assign clear responsibility for data reporting and metric performance.
- Monitor and Analyze Data Regularly: Track metrics at defined intervals to identify trends, detect issues, and assess progress. Use statistical tools to support analysis.
- Visualize and Communicate Results: Present data in clear formats such as charts or reports to support understanding across teams.
- Take Action Based on Insights: Use findings to implement corrective actions or optimize processes. Ensure follow-up actions are documented and assessed for impact.
- Review and Optimize Metrics Periodically: Evaluate the continued relevance and effectiveness of metrics over time, through the management review process. Refine or replace metrics to reflect evolving goals, regulations, or process changes.
How Does QMS Software Support the Capture and Reporting of Quality Metrics?
QMS software is a digital platform that centralizes quality management processes, enabling organizations to monitor, control, and improve quality performance. QMS software supports the capture, tracking, and reporting of quality metrics by optimizing data collection.
Quality management software allows companies to improve their quality metrics management by providing the following functions.
- Streamline Data Collection and Tracking: A QMS software supports teams to gather data from different processes like document control, CAPA, audit management, training management, and others, to ensure consistent metric tracking.
- Provides Dashboards and Reports: An electronic QMS with reporting capabilities helps visualize metrics and allows companies to have a better overview of trends, supporting data-driven decision-making.
- Standardizes Workflows: QMS software can systematically enforce workflows that generate consistent, measurable data across departments.
- Improves Traceability and Accountability: Within QMS software, metrics can be linked to specific actions, users, or documents, enhancing accountability.
SimplerQMS is a fully validated QMS software built specifically for life science companies. SimplerQMS integrates broad quality processes, such as change control, training management, CAPA, audit management, supplier management, and document control, among others, into a unified platform.
Simpler QMS supports capturing and analyzing quality metrics by generating reports. SimplerQMS helps life science organizations meet key requirements like ISO 9001, ISO 13485, FDA 21 CFR 820, EU MDR, EU IVDR, FDA 21 CFR Part 210-211, EU GMP, ICH Q10, and others, while helping maintain traceability and audit readiness across quality metrics management.
