In a CMMI ML 4 process, organization comes up with Process Performance Baselines (PPBs), define Business Objectives (BO) and Process Performance Objectives (PPO), derive Process Performance Models (PPMs). Project uses these data and information to compose the project quantitatively. Project is managed quantitatively and statistically using PPMs.

In CMMI ML4 organization targets can be same as that of current baselines. There has to be a quantitative linkage between BO and PPO. A PPM is not at all necessary to substantiate the relationships between BO and PPO, simple mathematical expressions will do. For example, BO can be a compound growth percent of PPO itself for ‘n’ number of years.

PPOs could be measured at the time of project closure only. Then how the probability of success could be monitored intermittently ..? In order to monitor this we need to define some prediction models (PPMs) with dependent parameters as PPO and the parameters which affect PPOs as independent parameters.

These can be summarised as,

 

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Business Objectives (BOs) should be traceable from top level to bottom level. BOs are normally linked to the entire organization. So from there it should be broken down to the project level for run time monitoring rather than a final check at the end of the year/after a defined frequency.
BO can be in terms of profitability, time to market etc. Then based on the BO, a second layer of objectives are defined (Process Performance Objectives- PPOs, in CMMI terms). For example, to improve profitability, productivity can be the critical parameter. So profitability will become the parameter for BO and Productivity as the parameter for PPO. There can be multiple parameters as PPOs also. Based on these PPOs, sub processes which are critical are defined and measured. Targets are set for the sub process measures also. Statistical process Control should be applied at the project level as well as at the organizational level to ensure the probability of meeting the targets.