ISO 9001:2015 Clause 9.1.3 Analysis and evaluation

The organization shall analyse and evaluate appropriate data and information arising from monitoring and measurement.
The results of analysis shall be used to evaluate:
a) conformity of products and services;
b) the degree of customer satisfaction;
c) the performance and effectiveness of the quality management system;
d) if planning has been implemented effectively;
e) the effectiveness of actions taken to address risks and opportunities;
f) the performance of external providers;
g) the need for improvements to the quality management system.
NOTE Methods to analyse data can include statistical techniques.

1) The organization shall analyse and evaluate appropriate data and information arising from monitoring and measurement.

Monitoring and measurements established within the organization will generate a lot of data and information. To fully utilize this information, analysis and evaluation of data is required to help the management in decision making. Just gathering and looking at numbers without any analysis and evaluation will just be a futile exercise that will take a lot of effort without any real value derived out of it. Let’s take an example to understand this better. If you are just tracking the number of returned pieces of your product and not analysing the trends over a period of time, you cannot improve your products or services to your customers. Benchmarking or setting up goals on the achievement of your objectives is a useful method that can be used to identify any red flags beforehand. If your goal (looking at your previous performance) indicates that you do not have more than 2 returns in a month, but suddenly in a month you get 4 return requests, it is an alarm for you to look into the issue and find out the root causes behind it. This will help you in taking timely action before the issue goes out of control and fetch you some bad reputation. It is, therefore, important that data is analysed, a conclusion drawn out of it, and plans and actions made whenever an unfavourable trend or condition is observed. That is why analysis and evaluation of data is important for any organization. This will help you seize all opportunities of improvement that exist.

Analyzing and evaluating data and information arising from monitoring and measurement is a systematic process that organizations can follow to ensure they make informed decisions and improve their operations. Here is a step-by-step guide on how to analyze and evaluate such data effectively:

1. Define Objectives and Key Performance Indicators (KPIs): Start by clearly defining your organization’s objectives and the specific KPIs or metrics that align with these objectives. This step ensures that you are collecting data that is relevant to your goals.
2. Collect Data: Implement data collection methods and systems to gather information related to the identified KPIs. This data may come from various sources, such as customer feedback, production processes, sales records, or website analytics.
3. Organize and Clean Data: Ensure that the collected data is organized, accurate, and free from errors. This may involve data cleaning and validation processes to eliminate outliers and inconsistencies.
4. Data Analysis: Use data analysis techniques to uncover patterns, trends, and insights. Depending on the nature of your data, you can employ statistical analysis, data visualization, or machine learning algorithms to extract meaningful information.
5. Bench-marking: Compare your organization’s performance data against industry benchmarks or historical data to gain context and identify areas that require attention. Bench-marking can provide valuable insights into competitiveness and efficiency.
6. Evaluate Performance: Assess the performance of your organization based on the analysis results. Determine whether you are meeting your targets and objectives or if there are areas of concern.
7. Identify Root Causes: If performance is not meeting expectations, investigate the root causes. Use tools like root cause analysis (RCA) to identify underlying issues that need to be addressed.
8. Make Informed Decisions: Based on the analysis and evaluation, make informed decisions about what actions to take. This may involve initiating corrective actions to address issues or implementing improvement initiatives.
9. Set Priorities: Not all issues or opportunities for improvement are of equal importance. Prioritize actions based on their potential impact on your organization’s objectives and the resources available.
10. Document Findings and Actions: Keep thorough records of your data analysis, evaluation results, and the decisions and actions taken. Proper documentation is crucial for accountability and future reference.
11. Implement Changes: If corrective or improvement actions are required, implement them effectively. Monitor and measure the impact of these changes to ensure they achieve the desired results.
12. Monitor Continuously: Establish a continuous monitoring and measurement process. Regularly revisit your KPIs, collect data, and repeat the analysis and evaluation cycle to drive ongoing improvement.
13. Communicate Findings:Share the findings, actions, and results with relevant stakeholders within the organization. Effective communication ensures that everyone is aware of the progress and can contribute to the improvement process.
14. Feedback Loop: Use the insights gained from your ongoing analysis and evaluation to refine your data collection methods, KPIs, and strategies. Continuously adapt to changing circumstances and opportunities.
15. Compliance and Reporting: If your organization operates within specific regulatory frameworks or standards, ensure that you comply with reporting requirements related to data analysis and evaluation.

By following these steps and maintaining a systematic approach, organizations can effectively analyze and evaluate data and information arising from monitoring and measurement to drive continuous improvement and achieve their objectives.This clause requires that an organization collects, analyses and evaluates Quality Management System data. Both analysis, as well as evaluation of data, is important; that means data analysis through statistical techniques, trend analysis, etc. and interpretation of the analysed data so that it can be used in an appropriate manner, for example- for decision making and action planning.

2) The results of analysis shall be used to evaluate conformity of products and services

The results of analysis should be used to evaluate the conformity of products and services. This is a fundamental step in quality management and ensuring that your organization meets its quality objectives and customer requirements. Here’s how the results of analysis are typically used to evaluate conformity:

1. The results of analysis are compared to established standards, specifications, or customer requirements. This involves checking whether the measured or analyzed data aligns with the defined criteria for product or service quality.
2. Determine whether the analyzed data indicates that the products or services conform to the specified requirements. If the results meet or exceed the defined standards, it signifies conformity.
3. If the analysis reveals any discrepancies or deviations from the standards or requirements, these are identified as non-conformities. Non-conformities can be categorized based on their severity and impact.
4. Evaluate the severity of non-conformities. Some non-conformities may be critical, posing serious risks or safety concerns, while others may be minor deviations.
5. Based on the evaluation of conformity or non-conformity, decisions are made regarding the acceptability of the products or services. This may involve determining whether a product/service can be released to customers or needs further attention.
6. If non-conformities are identified, organizations typically initiate corrective actions to address the root causes of the issues. The goal is to bring the products or services into conformity.
7. To prevent similar non-conformities from recurring in the future, organizations may also implement preventive actions. This proactive approach helps in maintaining long-term conformity.
8. Thoroughly document the results of analysis, conformity assessments, and any actions taken. Proper documentation is essential for traceability, accountability, and compliance.
9. It’s important to communicate the results of the analysis, conformity assessments, and actions taken to relevant stakeholders, including management, quality teams, and customers, as appropriate.
10. Use the insights gained from the analysis and evaluation to identify opportunities for continuous improvement in processes, products, or services. Regularly review and update procedures to enhance conformity.
11. Ensure that your organization complies with industry standards, regulations, and contractual obligations related to product and service conformity. Stay updated on changing requirements.
12. Continuously adapt your analysis methods and evaluation criteria based on the insights gained from ongoing assessments. This ensures that your organization remains responsive to changing circumstances and customer needs.

By using the results of analysis to evaluate conformity, organizations can ensure that their products and services meet quality standards, customer expectations, and regulatory requirements. This, in turn, enhances customer satisfaction, reduces the risk of defects or non-compliance, and contributes to the overall success of the organization.

3) The results of analysis shall be used to evaluate the degree of customer satisfaction

Evaluating the degree of customer satisfaction is a critical aspect of business operations, and using the results of analysis is essential to achieve this effectively. Here’s how organizations can use the results of analysis to evaluate the degree of customer satisfaction:

1. Start by collecting feedback from your customers through various channels such as surveys, online reviews, direct communication, and social media. This feedback serves as the primary source of data for analysis.
2. Analyze the collected customer feedback data systematically. Depending on the volume of feedback, you can use statistical analysis or sentiment analysis tools to uncover patterns and trends.
3. Segment customer feedback data based on different factors such as product or service types, customer demographics, or geographic locations. This helps identify specific areas that may require attention.
4. Define key performance indicators related to customer satisfaction, such as Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), or Customer Effort Score (CES). These KPIs will serve as benchmarks for evaluation.
5. Compare your organization’s customer satisfaction KPIs to industry benchmarks or your own historical data. Benchmarking provides context for understanding your performance relative to others.
6. If you identify areas of low customer satisfaction, perform root cause analysis to determine the underlying reasons. This helps in addressing the root issues.
7. Extract actionable insights from the analysis. Identify specific actions that can be taken to improve customer satisfaction based on the feedback and data.
8. Implement corrective actions based on the insights gained from the analysis. Address the issues that are negatively impacting customer satisfaction. These actions may involve process improvements, product enhancements, or better customer support.
9. Implement preventive actions to avoid similar customer satisfaction issues in the future. This proactive approach helps in maintaining high levels of satisfaction.
10. Continuously monitor customer satisfaction KPIs and gather ongoing feedback. Regularly analyze this data to ensure that the corrective and preventive actions are effective.
11. Document all the steps in the analysis and improvement process. Keep records of customer feedback, analysis results, actions taken, and their outcomes.
12. Communicate the results of the analysis and the actions taken to relevant stakeholders, including employees, management, and customer-facing teams. Transparency is essential.
13. Engage with customers to show that you are actively addressing their concerns and feedback. Keep customers informed about improvements and changes based on their input.
14. Use the insights gained from ongoing analysis to adapt and refine your customer satisfaction measurement and improvement strategies.
15. Ensure that your organization complies with any industry-specific regulations or standards related to customer satisfaction and feedback management.

By following these steps and using the results of analysis to drive improvements, organizations can effectively evaluate and enhance the degree of customer satisfaction. Satisfied customers are more likely to become loyal customers and advocates for your products or services, which can lead to long-term business success.

4) The results of analysis shall be used to evaluate the performance and effectiveness of the quality management system

Using the results of analysis to assess the performance and effectiveness of the quality management system (QMS) is a fundamental practice in quality management. This process helps organizations ensure that their QMS is functioning as intended and continuously improving. Here’s how organizations can use the results of analysis for this purpose:

1. Begin by collecting relevant data related to the QMS, which may include metrics, key performance indicators (KPIs), process data, and audit findings. Analyze this data to identify trends, patterns, and areas that require attention.
2. Define specific KPIs that align with the goals and objectives of the QMS. These KPIs may include measures of process efficiency, product quality, customer satisfaction, compliance, and more.
3. Compare your organization’s QMS performance against industry benchmarks or best practices. This provides context for understanding how your QMS measures up to others in your sector.
4. Evaluate whether the QMS is conforming to relevant standards, or meeting the organization’s internal quality objectives. Non-conformities or deviations should be identified and addressed.
5. If non-conformities or areas of poor performance are identified, conduct root cause analysis to determine the underlying reasons. Understanding the root causes is crucial for effective corrective action.
6. Implement corrective actions to address the identified non-conformities and improve QMS performance. Ensure that these actions are targeted at addressing the root causes and preventing recurrence.
7. Implement preventive actions to proactively address potential issues and continuously improve the QMS. This helps prevent non-conformities from occurring in the first place.
8. Use the insights gained from data analysis to identify opportunities for continuous improvement within the QMS. Regularly review and update processes and procedures to enhance effectiveness.
9. Continuously monitor and measure the effectiveness of the QMS. Assess whether the implemented corrective and preventive actions are achieving the desired results.
10. Maintain thorough records of all QMS-related data, analysis results, actions taken, and their outcomes. Proper documentation is essential for traceability and compliance.
11. Present the results of the analysis and the overall performance of the QMS to top management during management review meetings. This fosters leadership involvement and commitment to QMS improvements.
12. Communicate the results of the QMS analysis, improvement efforts, and their impact to relevant stakeholders within the organization. Transparency is crucial for driving QMS effectiveness.
13. Ensure that the QMS complies with relevant industry standards and regulatory requirements. Stay updated on changes to standards and adapt your QMS accordingly.
14. Use the insights gained from ongoing analysis to adapt and refine your QMS measurement and improvement strategies. Continuously engage with employees and stakeholders to gather feedback.

By using the results of analysis to assess the performance and effectiveness of the quality management system, organizations can maintain and enhance the quality of their products or services, improve operational efficiency, and achieve their quality objectives. This ultimately leads to increased customer satisfaction and competitiveness in the marketplace.

5) The results of analysis shall be used to evaluate if planning has been implemented effectively

Evaluating whether planning has been implemented effectively is a crucial part of organizational management and continuous improvement. Using the results of analysis can help ensure that plans are executed as intended and deliver the desired outcomes. Here’s how organizations can use analysis results to evaluate the effectiveness of planning:

1. Start by establishing clear objectives and targets for the planning process. These objectives should be specific, measurable, achievable, relevant, and time-bound (SMART).
2. Collect relevant data and information related to the planning process. This may include project timelines, budget allocation, resource allocation, and key performance indicators (KPIs).
3. Analyze the collected data to assess whether the planning process has been followed as outlined in the initial plan. Look for discrepancies or variations from the plan.
4. Define KPIs that measure the success of planning implementation. These may include project milestones, budget adherence, resource utilization, and other relevant metrics.
5. Compare the actual results and outcomes against the planned targets and benchmarks. Identify any gaps between what was planned and what was achieved.
6. Determine if the planning process conforms to organizational standards, best practices, or industry norms. Non-conformities should be identified and addressed.
7. If discrepancies or non-conformities are found, perform root cause analysis to understand why the planning process did not go as intended.
8. Implement corrective actions to address the identified issues and bring the planning process back in line with the original plan. Ensure that these actions target the root causes.
9. Implement preventive actions to proactively address potential issues that could disrupt planning in the future. This helps prevent recurrence of similar problems.
10. Use the insights gained from data analysis to identify opportunities for continuous improvement in the planning process. Regularly review and update planning procedures and guidelines.
11. Continuously monitor and measure the effectiveness of planning implementation. Assess whether the implemented corrective and preventive actions are achieving the desired results.
12. Maintain comprehensive records of all planning-related data, analysis results, actions taken, and their outcomes. Proper documentation ensures accountability and traceability.
13. Communicate the results of the analysis and any corrective or preventive actions taken to relevant stakeholders. This includes project teams, managers, and decision-makers.
14. Present the results of the analysis and the effectiveness of planning implementation to top management during management review meetings. This ensures leadership involvement in the improvement process.
15. Use the insights gained from ongoing analysis to adapt and refine planning processes and procedures. Continuously seek input and feedback from those involved in the planning process.

By using the results of analysis to evaluate the effectiveness of planning, organizations can ensure that their strategies and initiatives are executed successfully and that deviations from the plan are addressed promptly. This leads to improved efficiency, better resource allocation, and the achievement of organizational objectives.

6) The results of analysis shall be used to evaluate the effectiveness of actions taken to address risks and opportunities

Evaluating the effectiveness of actions taken to address risks and opportunities is a critical aspect of risk management and strategic planning. Using the results of analysis can help organizations determine whether their risk mitigation and opportunity exploitation efforts are achieving the desired outcomes. Here’s a structured approach on how to use analysis results for this purpose:

1. Begin by identifying and assessing risks and opportunities that are relevant to your organization’s objectives and context. This involves evaluating their potential impact and likelihood.
2. Develop action plans that outline how you intend to address identified risks and opportunities. These plans should be specific, measurable, achievable, relevant, and time-bound (SMART).
3. Collect relevant data and information related to the implementation of actions aimed at addressing risks and opportunities. This may include data on project progress, financial metrics, performance indicators, and any other relevant data sources.
4. Analyze the collected data to assess the effectiveness of the actions taken to address risks and opportunities. Look for trends, patterns, and deviations from expected outcomes.
5. Define KPIs that measure the success of risk mitigation and opportunity exploitation efforts. These KPIs may vary depending on the nature of the risks and opportunities.
6. Compare the actual results and outcomes against the planned targets and benchmarks. Identify any gaps between what was planned and what was achieved.
7. Determine if the actions taken conform to organizational standards, best practices, or industry norms. Non-conformities should be identified and addressed.
8. If discrepancies or non-conformities are found, perform root cause analysis to understand why the actions did not produce the expected results.
9. Implement corrective actions to address the identified issues and improve the effectiveness of risk and opportunity management efforts. Ensure that these actions target the root causes.
10. Implement preventive actions to proactively address potential issues that could hinder the effectiveness of risk and opportunity management in the future. This helps prevent recurrence.
11. Use the insights gained from data analysis to identify opportunities for continuous improvement in risk and opportunity management processes. Regularly review and update these processes and procedures.
12. Continuously monitor and measure the effectiveness of actions taken to address risks and opportunities. Assess whether the implemented corrective and preventive actions are achieving the desired results.
13. Maintain comprehensive records of all data related to risk and opportunity management, analysis results, actions taken, and their outcomes. Proper documentation ensures accountability and traceability.
14. Communicate the results of the analysis and any corrective or preventive actions taken to relevant stakeholders. This includes project teams, risk management teams, and decision-makers.
15. Use the insights gained from ongoing analysis to adapt and refine risk and opportunity management processes and procedures. Continuously seek input and feedback from those involved in the process.

By using the results of analysis to evaluate the effectiveness of actions taken to address risks and opportunities, organizations can ensure that their risk management and strategic planning efforts are informed by data and lead to positive outcomes. This proactive approach helps organizations minimize risks, seize opportunities, and achieve their objectives.

7) The results of analysis shall be used to evaluate the performance of external providers

Evaluating the performance of external providers is a crucial aspect of supplier management and ensuring that they meet your organization’s quality and service expectations. Using the results of analysis can help organizations assess the effectiveness and reliability of their external suppliers. Here’s how to use analysis results for this purpose:

1. Start by defining clear criteria and expectations for selecting and evaluating external providers. This includes setting quality standards, delivery schedules, cost expectations, and any other relevant factors.
2. Define key performance indicators (KPIs) and metrics that will be used to measure the performance of external providers. Common metrics include on-time delivery, product quality, lead times, cost-effectiveness, and responsiveness.
3. Collect data related to the performance of external providers. This data may include supplier performance reports, quality control data, delivery records, and feedback from internal stakeholders.
4. Analyze the collected data to assess the performance of external providers. Look for trends, patterns, and deviations from established KPIs and criteria.
5. Compare the actual performance of external providers against the agreed-upon standards and benchmarks. Identify any gaps between the expected and actual performance.
6. Determine if the external providers are conforming to the terms and conditions of their contracts or agreements. Non-conformities or discrepancies should be identified and addressed.
7. If performance issues or non-conformities are identified, perform root cause analysis to understand why the issues occurred.
8. Implement corrective actions to address the identified performance issues and non-conformities. Ensure that these actions target the root causes and are aligned with your organization’s objectives.
9. Implement preventive actions to proactively address potential performance issues with external providers. This helps prevent recurrence of similar problems.
10. Use the insights gained from data analysis to identify opportunities for continuous improvement in supplier management processes. Regularly review and update supplier evaluation procedures.
11. Continuously monitor and measure the performance of external providers to ensure that corrective and preventive actions are effective in improving their performance.
12. Maintain comprehensive records of all data related to supplier performance, analysis results, actions taken, and their outcomes. Proper documentation ensures accountability and traceability.
13. Communicate the results of the analysis and any corrective or preventive actions taken to relevant stakeholders, including procurement teams, quality teams, and management.
14. Use the insights gained from ongoing analysis to adapt and refine your supplier evaluation and management strategies. Continuously engage with suppliers to gather feedback and improve collaboration.
15. Ensure that external providers comply with any industry-specific regulations or standards related to their products or services. Regularly assess and update your supplier management processes to maintain compliance.

By using the results of analysis to evaluate the performance of external providers, organizations can ensure that their supplier relationships are aligned with their strategic goals, maintain product and service quality, and drive continuous improvement in their supply chain operations. This helps organizations mitigate risks and maximize the value provided by their external suppliers.

8) The results of analysis shall be used to evaluate the need for improvements to the quality management system.

Using the results of analysis to evaluate the need for improvements to the quality management system (QMS) is a fundamental aspect of quality management. This process helps organizations identify areas that require enhancement and ensure that the QMS continues to be effective. Here’s how organizations can use analysis results for this purpose:

1. Collect data and information related to the QMS, which may include performance metrics, audit findings, customer feedback, non-conformities, and process data. Analyze this data to identify trends, patterns, and areas that may require improvement.
2. Define KPIs that measure the performance of the QMS. These KPIs should be aligned with organizational objectives and relevant to the QMS processes.
3. Compare the performance of your QMS against industry benchmarks, best practices, or your own historical data. Benchmarking provides context for understanding your QMS’s performance relative to others.
4. Determine if the QMS is conforming to relevant standards, such as ISO 9001, or meeting the organization’s internal quality objectives. Non-conformities or deviations should be identified and addressed.
5. If non-conformities or areas of poor performance are identified, perform root cause analysis to determine the underlying reasons. Understanding the root causes is crucial for effective corrective action.
6. Implement corrective actions to address the identified non-conformities and improve the performance of the QMS. Ensure that these actions target the root causes and prevent recurrence.
7. Implement preventive actions to proactively address potential issues that could hinder the effectiveness of the QMS in the future. This helps prevent similar problems from occurring.
8. Use the insights gained from data analysis to identify opportunities for continuous improvement within the QMS. Regularly review and update processes and procedures to enhance effectiveness.
9. Continuously monitor and measure the effectiveness of the QMS and the implemented corrective and preventive actions. Assess whether these actions are achieving the desired results.
10. Maintain thorough records of all QMS-related data, analysis results, actions taken, and their outcomes. Proper documentation ensures traceability and accountability.
11. Present the results of the analysis and the effectiveness of QMS improvements to top management during management review meetings. This fosters leadership involvement in the improvement process.
12. Communicate the results of the analysis and any corrective or preventive actions taken to relevant stakeholders within the organization. Transparency is crucial for driving QMS improvements.
13. Use the insights gained from ongoing analysis to adapt and refine QMS measurement and improvement strategies. Continuously engage with employees and stakeholders to gather feedback.
14. Ensure that your QMS complies with relevant industry standards, regulations, and customer requirements. Stay updated on changes to standards and adapt your QMS accordingly.

By using the results of analysis to evaluate the need for improvements to the QMS, organizations can ensure that their quality processes remain effective, efficient, and aligned with their strategic objectives. This leads to improved product or service quality, customer satisfaction, and overall business performance.

9) Methods to analyse data can include statistical techniques.

Analyzing data often involves the use of statistical techniques to extract meaningful insights, identify patterns, and draw conclusions from the data. Statistical methods provide a systematic and quantitative approach to data analysis, helping organizations make informed decisions. Here are some common statistical techniques used for data analysis:

1. Descriptive Statistics: Descriptive statistics provide a summary of the main characteristics of a dataset. Common measures include mean (average), median (middle value), mode (most frequent value), standard deviation (measure of data spread), and percentiles.
2. Inferential Statistics: Inferential statistics are used to make inferences or predictions about a population based on a sample of data. Techniques include hypothesis testing, confidence intervals, and regression analysis.
3. Hypothesis Testing: Hypothesis testing is used to determine whether there is a statistically significant difference between groups or conditions. Common tests include t-tests, chi-squared tests, and analysis of variance (ANOVA).
4. Correlation Analysis: Correlation analysis examines the strength and direction of relationships between variables. It helps identify whether changes in one variable are associated with changes in another. Pearson correlation and Spearman rank correlation are commonly used methods.
5. Regression Analysis: Regression analysis is used to model the relationship between a dependent variable and one or more independent variables. Linear regression, logistic regression, and multiple regression are examples of regression techniques.
6. Time Series Analysis: Time series analysis is used to analyze data collected over time, such as stock prices, sales, or temperature readings. Techniques include autoregressive integrated moving average (ARIMA) modeling and exponential smoothing.
7. Cluster Analysis: Cluster analysis is used to group similar data points or objects together based on their characteristics. K-means clustering and hierarchical clustering are common methods.
8. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique used to identify patterns and relationships in multivariate data. It helps simplify complex datasets by transforming them into a smaller set of uncorrelated variables called principal components.
9. Factor Analysis: Factor analysis is used to identify underlying factors or latent variables that explain the correlations between observed variables. It is often used in psychology and social sciences to understand constructs like intelligence or personality.
10. ANOVA (Analysis of Variance): ANOVA is used to compare means across multiple groups or categories to determine whether there are statistically significant differences between them.
11. Chi-Squared Test: The chi-squared test is used to assess the independence of categorical variables in a contingency table. It is often used in hypothesis testing for categorical data.
12. Nonparametric Tests: Nonparametric tests, such as the Wilcoxon rank-sum test or the Kruskal-Wallis test, are used when data do not meet the assumptions of parametric tests like t-tests or ANOVA.
13. Bayesian Analysis: Bayesian analysis uses Bayes’ theorem to update beliefs about a parameter or hypothesis based on new evidence. It is particularly useful for problems involving uncertainty and probability.
14. Machine Learning Algorithms: Machine learning techniques, including decision trees, random forests, support vector machines, and neural networks, can also be used for data analysis, especially for predictive modeling and pattern recognition.

The choice of statistical technique depends on the nature of the data, the research question or problem at hand, and the goals of the analysis. Often, a combination of techniques may be used to gain a comprehensive understanding of the data and extract valuable insights.

Documented Information required:

This clause does not have a mandatory requirement for Documented Information. . When it comes to documentation and records required for compliance with this clause, you should consider the following:

1. Quality Manual or QMS Documentation: Your quality manual or quality management system (QMS) documentation should outline the organization’s approach to analysis and evaluation of data and information. It should provide an overview of the processes and methods used for analysis and evaluation.
2. Quality Policy: Your organization’s quality policy should emphasize the importance of data analysis and evaluation as part of your commitment to continuous improvement.
3. Procedure for Data Analysis and Evaluation: Develop a documented procedure that outlines how data and information will be collected, analyzed, and evaluated. This procedure should describe the methods, responsibilities, and timeframes for these activities.
4. Data Collection Records: Maintain records of the data collected from various sources within your organization. This may include product/service quality data, customer feedback, process performance data, and any other relevant information.
5. Analysis Methods and Tools: Document the methods and statistical or analytical tools used for data analysis. This can include statistical software, spreadsheets, or specific analytical techniques.
6. Records of Analysis: Keep records of the results of data analysis, including any trends, patterns, or significant findings. This documentation should demonstrate the effectiveness of your analysis process.
7. Evaluation Criteria: Define clear criteria and standards against which the data and information will be evaluated. These criteria can include product/service conformity, customer satisfaction, process performance, and compliance with relevant standards.
8. Records of Evaluation: Maintain records of the evaluations performed based on the analysis results. Document any decisions or actions taken as a result of the evaluations.
9. Corrective and Preventive Action Records: If non-conformities or areas for improvement are identified through analysis and evaluation, maintain records of the corrective and preventive actions taken to address them.
10. Management Review Records: Document the outcomes of management review meetings where analysis and evaluation results are discussed and decisions on improvements are made.
11. Internal Audit Records: If internal audits are used to assess the effectiveness of your analysis and evaluation processes, maintain records of these audits, including findings and corrective actions.
12. Training Records: Keep records of training provided to employees involved in data analysis and evaluation to demonstrate competence in these activities.
13. Communication Records: Document communication of analysis and evaluation results to relevant stakeholders, including management, employees, and other interested parties.
14. Evidence of Continuous Improvement: Document evidence of continuous improvement resulting from the analysis and evaluation activities. This can include updated processes, improved product/service quality, and enhanced customer satisfaction.

Remember that the level of documentation and record-keeping should be proportionate to the size and complexity of your organization and the criticality of the processes being analyzed and evaluated. The goal is to ensure that you have the necessary information to demonstrate compliance with ISO 9001 and to drive continual improvement in your quality management system.