Toughest ISO 9001:2000 Requirements (8.4)

In an earlier newsletter, I identified twelve ISO 9001:2000 clauses as the toughest requirements to understand and meet with conforming practices. Clauses 4.1, 5.1, 5.4.1, 5.4.2, 6.2.2, 6.3, 7.3.1, 7.5.2, and 8.2.1 have been addressed in past newsletters. This article picks up with clause 8.4, Analysis of Data.

ISO 9001:2000, clause 8.4 :

The organization shall determine, collect, and analyze appropriate data to demonstrate the suitability and effectiveness of the quality management system and to evaluate where continual improvement of the effectiveness of the quality management system can be made. This shall include data generated as a result of monitoring and measurement and from other relevant sources.

The analysis of data shall provide information relating to:
a) customer satisfaction (see 8.2.1)
b) conformity to product requirements
c) characteristics and trends of processes and products including opportunities for preventive action, and
d) suppliers 

The importance of this requirement is highlighted by “data analysis” being one of the eight quality management principles:

       Effective decisions are based on the analysis of data and information

Data collection is meaningless unless it is analyzed and used for decisions and actions. If you want to improve your processes and products, analyzing data is essential. Organizations are expected to determine, collect, and analyze data from various sources. By evaluating performance against plans and objectives, management can identify areas needing improvement.

Management relies on factual information to make well-informed decisions. The results of the data analysis can be used, along with experience and intuition, to guide management actions. Analyzing the data may identify the root cause of the detected or potential problem, and help select the appropriate corrective or preventive action.

Data from all parts of the organization should be evaluated to improve system performance. This information can determine:

  • process performance deviations
  • training program effectiveness
  • customer complaint trends
  • equipment availability
  • scrap and rework rates
  • missed delivery dates
  • process cycle times
  • cost of poor quality
  • supplier performance ratings
  • customer satisfaction levels
  • attainment of quality objectives
  • quality system effectiveness

Your monitoring and measurement activities can collect significant amounts of data. Only gather data that you intend to use. The results of data analysis can be used as evidence of conformity to requirements and as input for:

  • management reviews (5.6)
  • corrective actions (8.5.2)
  • preventive actions (8.5.3)
  • internal audits (8.2.2)
  • customer satisfaction (8.2.1)
  • quality objectives (5.4.1)
  • supplier performance (7.2.1)

Use of statistical techniques to analyze data can be especially helpful for monitoring process (8.2.3) and product (8.2.4) trends. Quality objectives should be set for continual improvement, not just based on past performance.

ISO 9001:2000 identifies four specific areas for analysis.

a) customer satisfaction
b) conformity to product requirements
c) characteristics and trends of processes and products including opportunities for preventive action, and
d) suppliers

The specific information gathered for these areas may vary based on the size of the organization and its type of product. For customer satisfaction, the data may come from market surveys, focus groups, trip reports, customer questionnaires, product survey cards, post-transaction interviews, service reports, warranty reports, competitor benchmarks, advisory groups, trade associations, consumer organizations, as well as, complaints, returns, and other forms of customer feedback. See the article on customer satisfaction in our May 2003 newsletter.

Data from inspection and test results, design changes, service reports, and warranty returns may be used to analyze conformity to product requirements. Process and product measurements can be studied to determine trends. Information about manufacturing capability, types of defects, order entry error rates, and statistical process control can signal opportunities for preventive action. Supplier performance data can be analyzed to identify outstanding suppliers and poor suppliers.

Your quality policy must include a commitment to meet requirements and continually improve the effectiveness of your quality management system. Only through the analysis of data will an organization know if its quality policy is successful. How well the data analysis is being performed will ultimately be judged by continual improvement and higher levels of customer satisfaction.