About Data Analysis and Reporting

The manner in which reliability data is analyzed and reported will largely have to be tailored to the specific circumstance or organization. However, it is possible to break down the general methods of analysis/reporting into two categories: non-parametric analysis and parametric analysis. Overall, it will be necessary to tailor the analysis and reporting methods by the type of data as well as to the intended audience. Managers will generally be more interested in actual data and non-parametric analysis results, while engineers will be more concerned with parametric analysis. Of course this is a rather broad generalization and if the proper training has instilled the organization with an appreciation of the importance of reliability engineering, there should be an interest in all types of reliability reports at all levels of the organization. Nevertheless, managers are usually more interested in the "big picture" information that non-parametric analyses generally tend to provide, while not being particularly interested in the level of technical detail that parametric analyses provide. On the other hand, engineers and technicians are usually more concerned with the close-up details and technical information that parametric analyses provide. Both of these types of data analysis have a great deal of importance to any given organization, and it is merely necessary to apply the different types in the proper places.

Non-Parametric Analysis
Data conducive to non-parametric analysis includes information that has not or cannot be rigorously processed or analyzed. Usually, it is simply straight reporting of information, or if it has been manipulated, it is usually by simple mathematics, with no complex statistical analysis. In this respect, many types of field data lend themselves to the non-parametric type of analysis and reporting. In general, this type of information will be of most interest to managers as it usually requires no special technical know-how to interpret. Another reason it is of particular interest to managers is that most financial data falls into this category. Despite its relative simplicity, the importance of non-parametric data analysis should not be underestimated. Most of the important decisions that are made concerning the business are based on non-parametric analysis of financial data.

Non-Parametric Reliability Analysis
Although many of the non-parametric analyses that can be performed based on field data are very useful for providing a picture of how the products are behaving in the field, not all of this information can be considered "hard-core" reliability data. As was mentioned earlier, many such data types and analyses are just straight reporting of the facts. However, it is possible to develop standard reliability metrics, such as product reliability and failure rates, from the non-parametric analysis of field data. A common example of this is the "diagonal table" type of analysis that combines shipping and field failure data in order to produce empirical measures of defect rates.