We're a ML3 company and we'd like to move up to ML4. There is a lot of argument about the meaning of OPP. Could you shed some light on this?
Absolutely! Almost all organizations struggle with the four "High Maturity" Process Areas, and Organizational Process Performance, or OPP, is the one they struggle with the most.
It's impossible to claim you are performing at "high maturity" without OPP. It's a foundational process area that provides an infrastructure that allows you to use the other HM process areas (as well as perform better with the ML2/3 PAs). It exists to establish and maintain (sound familiar?) the basic statistical data you need to continuously improve your projects and your organization.
SG1: Establish Performance Baselines and Models
The practices that lead to achieving this goal are practices that support the selection of processes to statistically monitor, establishing organizational objectives, establishing measures to use, creating baselines of process performance, and creating process performance"models" to help predict the outcome of a set of processes being performed (and to assist projects in the selection of sub-processes from the organizations standard set of processes - QPM)
So, let's say that our goal is for all projects to have a customer satisfaction rating of 10. Can we achieve it? What does history tell us? Is that realistic? What were the causes when project's did NOT achieve this? And what might we do with our projects to achieve this goal specification?
These are the questions that OPP is designed to support.
SP1.1 Select Processes
Exactly which sub-processes (from the standard set of processes) will we be including in our analysis effort? You often see process elements related to Peer Reviews, Defect discovery, engagement (TeamScore) and the like.
SP1.2 Establish Process Performance Measures
Which process attributes are we going to measure? If you were to select engagement, you might measure actual participation vs. planned participation. For peer reviews you might measure preparation time for each peer review. These are measures of some attributes of the sub-process.
SP1.3 Establish quality and process performance objectives
These objectives come from various sources. Some come from the business ("we need to increase sales by 5%) or from the engineering process itself (reduce defects by 12%). If we have enough data to establish an analysis of the process performance variation, and we can calculate the natural bounds of process performance, then this will have to be our objective for the process - as it's all the process is capable of delivering ("Voice of the Process")! In other words, the process is telling you"STOP! You can't reach this objective!"
SP1.4 Establish Process Performance Baselines
These baselines are the data that represent actual process performance (and it's variation). We typically will plot these data using some type of process performance (control) chart or histogram. We are trying to establish what the sub-process is capable of - these natural bounds are called the "voice of the process" and they establish that, if the process remains the same, here is what will likely occur (within limits).
So, using engagement as an example, we can measure customer engagement's effect on customer satisfaction by measuring their engagement at different points in the process, and comparing that to historical customer satisfaction results.
SP1.5 Establish Process Performance Models
Models are used to estimate the value of a process performance measure (the "result") if a given set of sub-processes is performed.
There are many techniques available for this - simulation for instance - but in the end this is a "what if" analysis using the historical data to estimate an outcome with a high-degree of probability. This is useful for projects as they attempt to predict the outcome, as opposed to just guessing.
You've probably noticed that there is nothing in OPP that actually says "fix the problems."
That's OK - there are other process areas for this (CAR and OID).
OPP is a "capability." It gives us the data we need to make better decisions about the use of the process - and it tells us exactly what we're capable of.
Whether we like it or not!