Analysis of Data

ISO 9001 requires organizations to analyze data to demonstrate the suitability and effectiveness of the QMS, and to indicate where improvements can be made.  There are many sources of data to analyze, and the organization is at liberty to decide what data to study.

Typical data analysis might include these types of data:

• Outputs from manufacturing processes, such as inspection results, warranty claims, etc.
• Outputs from the corrective action process
• Business process data such as on time delivery, customer satisfaction surveys, etc.
• Vendor performance data such as on time delivery and quality performance

Data analysis can be complex, requiring sophisticated computer programs, or can be quite simple.  Pareto analysis, which separates the critical few from the trivial many, is a simple but powerful technique for determining where to focus improvement efforts.  This simple technique is best shown in a bar chart.  I like it because the results are clear and easily understood by senior managers. 

Information from statistical process control charts such as Shewhart control charts and many variants can be a powerful tool for pointing out processes in need of improvement, and for bringing manufacturing processes into statistical control.  It is not difficult to create spreadsheets that perform these analyses.

Trend charts can indicate those processes which are moving in an unwanted direction.  For example, charting warranty claims monthly can be an indicator that quality of product in the field is deteriorating.

More complicated analytical techniques such as analysis of variance, regression analysis, and designed experiments are useful for understanding the effect of process variables on process outputs.  These analyses can sometimes be done in a spreadsheet, but most often, more sophisticated software is employed.   They require a trained statistical analyst.  Most often, they are applied when troubleshooting process problems.

Organizations should identify those measurements that are important indicators of QMS performance.  Typically, data that is in alignment with the organizations quality objectives will be measured against established goals. 

The output of the data analysis processes can become the input to the preventive action and management review processes.  Using measurement data in this way keeps the organization in line with its objectives and allows it to adjust the QMS as necessary to meet the objectives that have been set.  When selecting measurements, and measurement methods, keep in mind the organization’s established objectives and the audience that will view the results. 

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