Key Practices of CMMI High Maturity

For organizations that intend to achieve CMMI High Maturity, they should be able to demonstrate the following key practices. Declaration of Process Performance Objectives in Quantitative Terms The objectives must be set-up for at least the "critical" processes and sub-processes. The method to do this effectively is to ascertain or determine the organizations' business goals and identify the processes and sub-processes critical to business success in terms of achievement of business goals. And then define parameters and metrics for measuring the process performance and finally assign performance targets or goals for the measurement parameters.  Clear Quantitative Understanding of the Performance of Processes and Sub-processes Selected as "Critical" This understanding must necessarily be in terms of statistical properties like central tendency (mean, etc.) and dispersion (standard deviation, etc.). It may be a good idea to also gain an understanding of both the extent of how symmetric the distribution is (skewness) and how peaked the distribution is (kurtosis), at least for the "super-critical" processes and sub-processes. This statistical characterization represents the "Process Performance Baselines". Process Composition based on Quantitative Understanding of the Performance of the Available Processes and Sub-processes Selection of processes and sub-processes for a specific project should be based on comparing the performance objectives that must be achieved. This needs to be viewed against what will, with a good likelihood, be achieved by following a selected set of processes and sub-processes. Usage of Prediction Models (or "Process Performance Models") These are statistical models in the form Y = f(x) which need to be used for projecting or predicting the likely outcome that will be achieved by performing a process.  The predicted performance of the selected parameter (dependent Y factor) will be based on actual values of some influencing parameters (independent X factors).  When actual data on the X factors becomes available as the project moves forward, the projected or the predicted value of the Y factor should be progressively calibrated and refined. Usage of Control Charts (SPC Theory) Control charts are useful for monitoring and controlling the process performance on a real-time basis. They provide the ability to detect changes in either mean or the variance of the process. Usage of Statistical Tools such as Hypothesis Testing (t-test, etc.) These statistical tests are useful for making decisions based on solid quantitative understanding of variation. The tests can be used to statistically confirm the "significance" of changes in either mean or the variance of the process. Planning and Execution of Improvement Projects using Statistical Tools and Techniques The process improvement projects need to demonstrate quantitative impact.  They can be planned and executed using a multi-step approach as explained below: Establish in quantitative terms the current level of performance Determine the statistical significance of the difference between the current and the desired level of performance.  If the difference is significant, statistically speaking, plan and implement improvement actions; otherwise, no action is required. In case improvement actions are taken, establish in quantitative terms the achieved level of performance Determine the statistical significance of the difference between the achieved and the desired level of performance.  If the difference is significant, statistically speaking, plan and implement further improvement actions; otherwise, stop.