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This is an example of advanced criteria analysis during document editing.
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Assessing document quality in terms of ISO 9001
Review Structure and Content
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Regulatory Requirements: Ensure the document complies with relevant regulatory and statutory requirements.
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Quality assessment criteria
Category | Subcategory | Description | Yes/No |
Structure and Content | Quality Manual | Included and comprehensive | |
Quality Policy and Objectives | Clearly stated | ||
Procedures and Work Instructions | Detailed and complete | ||
Control of Documented Information | Approval | Document approved for use | |
Review and Update | Regularly reviewed and updated | ||
Identification and Distribution | Clearly identified and appropriately distributed | ||
Accessibility and Storage | Accessibility | Easily accessible to relevant personnel | |
Storage and Protection | Properly stored and protected | ||
Retention and Disposal | Retention | Retained for required period | |
Disposal | Proper disposal procedures in place | ||
Accuracy and Currency | Accuracy | Accurate and error-free | |
Currency | Up-to-date with recent changes | ||
Compliance | Regulatory Requirements | Compliant with relevant regulations |
Normalisation
Normalisation is a critical step in Document Criteria Analysis to ensure consistency and fairness when evaluating documents with different characteristics. It standardises raw scores across multiple criteria, making them comparable within a unified scale. This approach aligns with the methodology used in Document Weighting to provide balanced and reliable document assessments.
Purpose of Normalisation
Standardises Scores: Adjusts document criteria scores to a common scale.
Ensures Fair Comparisons: Allows documents of varying lengths, complexity, and relevance to be assessed equitably.
Enhances Consistency: Aligns document evaluations with the weighting model used in Document Weighting.
Normalisation Methodology
The normalised score for each document criterion is computed using the Min-Max normalisation formula:
Where:
is the raw score for a criterion.
and are the minimum and maximum scores across all documents for that criterion.
results in a value between 0 and 1, ensuring comparability.
Alternatively, Z-score normalisation may be used for datasets where distributions vary significantly:
Where:
is the mean score.
is the standard deviation.
This method ensures scores follow a standard normal distribution with a mean of 0 and a standard deviation of 1.
Integration with Document Weighting
The normalised criteria scores serve as inputs for Document Weighting, ensuring the weighted score computation remains consistent across document evaluations.
By normalising scores before weighting, the document evaluation process maintains robustness, preventing bias due to variations in document characteristics.
Example
Document | Raw Score (Criterion A) | Normalised Score (Min-Max) |
|---|---|---|
Doc 1 | 30 | 0.25 |
Doc 2 | 50 | 0.75 |
Doc 3 | 40 | 0.50 |
After normalisation, these values are used in the final document weighting calculations, ensuring consistent assessment criteria.
Conclusion
By incorporating normalisation into Document Criteria Analysis, Orthogramic ensures a structured and unbiased document evaluation process.