Manage the work using statistical and other quantitative techniques to determine whether or not the quality and process performance objectives for the work will be satisfied.
This specific practice uses multiple inputs to predict if the quality and process-performance objectives for the work will be satisfied. Based on this prediction, risks associated with not meeting the quality and process performance objectives are identified and managed, and actions to address deficiencies are defined as appropriate.
Key inputs to this analysis include the individual subprocess stability and capability data derived from the previous specific practice, as well as performance data from monitoring other subprocesses, risks, and suppliers’ progress.
Example Work Products
- Predictions of results to be achieved relative to the quality and process performance objectives of the work
- Graphical displays and data tabulations for other subprocesses, which support quantitative management
- Assessment of risks of not achieving the quality and process performance objectives of the work
- Actions needed to address deficiencies in achieving work objectives
1. Periodically review the performance of subprocesses.
Stability and capability data from monitoring selected subprocesses, as described in SP2.1, are a key input into understanding the work group’s overall ability to meet quality and process performance objectives.
In addition, subprocesses not selected for their impact on work objectives can still create problems or risks for the work and thus some level of monitoring for these subprocesses may be desired as well. Analytic techniques involving the use of graphical displays can also prove to be useful to understanding subprocess performance.
2. Monitor and analyze suppliers’ progress toward achieving their quality and process performance objectives.
3. Periodically review and analyze actual results achieved against established interim objectives.
4. Use process performance models calibrated with project data to assess progress toward achieving the quality and process performance objectives of the work.
Process performance models are used to assess progress toward achieving objectives that cannot be measured until a future phase in the work lifecycle. Objectives can either be interim objectives or overall objectives.
Calibration of process performance models is based on the results obtained from performing the activities described in the previous subpractices and specific practices.
5. Identify and manage risks associated with achieving the quality and process performance objectives of the work.
- Subprocesses having inadequate performance or capability
- Suppliers not achieving their quality and process performance objectives
- Lack of visibility into supplier capability
- Inaccuracies in the process performance models used for predicting performance
- Deficiencies in predicted process performance (estimated progress)
- Other identified risks associated with identified deficiencies
6. Determine and implement actions needed to address deficiencies in achieving the quality and process performance objectives of the work.
The intent of this subpractice is to identify and implement the right set of actions, resources, and schedule to place the work group back on a path toward achieving its objectives.
- Changing quality and process performance objectives so that they are within the expected range of the defined process
- Improving the implementation of the defined process
- Adopting new subprocesses and technologies that have the potential for satisfying objectives and managing associated risks
- Identifying the risk and risk mitigation strategies for deficiencies
- Terminating the work
Some actions can involve the use of root cause analysis, which is addressed in the next specific practice.
When corrective actions result in changes to attributes or measures related to adjustable factors in a process performance model, the model can be used to predict the effects of the actions. When undertaking critical corrective actions in high risk situations, a process performance model can be created to predict the effects of the change.