Processes comprise of a functions which operate on inputs to produce outputs. Data associated with the inputs and outputs can be used to further enhance the process quality. The data related to the process outputs could include important elements that when fed back to the input within a solid analytical framework could offer valuable insights and impacts on the process improvement.
Several key steps in designing the appropriate data collection, analysis and feedback system are summarized below:
1. Decide where to collect the data
Data can be collected at the input, various processing points, decision-points, at the output or any combination of all of these locations.
2. Decide what measurement systems to use
Various techniques exist to collect and measure data. Decisions need to be made on best statistical approaches, summarization techniques, collection methods and the levels of accuracy and precision needed.
3. Decide how to analyze the data
Approaches of data analysis need to be determined. It is important to determine cause and effects, root causes, correlation, regression dependencies and other relationships among the various variables and factors.
4. Decide on how to use the information resulting from the data analysis
Decisions related to the application of the information learned is important. For example will information be applied in real-time, or after process reengineering is brainstormed.
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