We're ML3 and we believe we have world-class project management capabilities. We do all of the things in PP, PMC, RSKM, SAM, and IPM really well and we must be performing "at the next level." Doesn't that just make us ML4?
Hmmmm. uh .. .hem. Let's take a look.
You're right in that being ML4 requires you to have a solid foundation with proven performance from the ML2 and ML3 process areas - so you get points for that. Without that you're not even in the game.
But QPM is different than "advanced project management." It's not necessarily "better" project management (although I would argue that it would make managing projects "better") because "better" is subjective. But it is different.
It's different in that it depends upon the use of statistical techniques, as well as the presence of process-performance baselines and models, to be useful.
SG1: Manage the Project Quantitatively
The word "quantitatively" is a special word in the CMMI that infers the use of statistical methods. The practices supporting this goal all depend on the use of these methods to succeed.
SP1.1 Establish the project's objectives
These objectives are for quality and for process performance. They need to be based on what can actually be accomplished, and that information is only available if you're performing OPP successfully. In the case of a "mandate" from management (a quality "specification") you ALSO will need OPP to identify the risks associated with the mandate, and the corrective actions that will need to be taken to achieve it.
SP1.2 Compose the defined process
Using the data from OPP as your guide (baselines and models), and within the context of the objectives from SP1.1, select the appropriate sub-processes from the set of standard processes (OPD) that will enable you to achieve your objective. This is a little like a more granular, data-focused way of performing IPM SP1.1 and SP1.2.
SP1.3 Select sub-processes that will be statistically managed
Which sub-processes will you need to manage / monitor in order to understand if you are going to achieve your objectives? Again, OPP can help here.
SP1.4 Manage Project Performance
Using the aforementioned monitoring, manage the performance of the project, taking corrective action as needed. Why do I have data points outside of my control limits ("assignable causes")? Time to find out (you can use CAR for this).
SG2: Statistically manage sub-process performance
Use statistical methods to understand variation and take corrective action when necessary. Some of these practices will probably be performaned along with SG1 (above).
SP2.1: Select measures and analytic techniques
What measures am I going to use to steer the ship, and what techniques (e.g.; process performance charts, histograms, XmR charts, etc) am I going to use to understand variation?
SP2.2 Use statistical methods to understand variation
Use the techniques and methods to identfy assignable causes of variation (e.g.; outside of control limits).
SP2.3 Monitor performance of selected subprocesses
Understand how the various sub-processes you have selected are performing, so you can understand progress towards achieving the objectives set in SG1.
SP2.4 Record statistical management data
We need to update the baselines created by the OPP capability with actual process-performance data - and this is where we do that. This way the next poor schmuck that comes along will learn from MY screwups.
Whew! Well, there is a lot here, and it's way different than PP/PMC. Is it worth doing?
Well, let me ask you this. Do you want to identify and eliminate defects earlier? Do you want the next project to be better than the current one, and the next one after that even better? Do you want to spend less time on drudgery and more time on engineering?