Prediction Models in CMMI High Maturity

One of the most difficult (supposedly so) concept in CMMI High Maturity is the development of "valid" and "usable" process performance models or PPMs. These PPMs are useful for predicting and hence quantitatively managing the outcome of a process. These are otherwise commonly known as Prediction or Predictive Models. Developing Prediction Models - Connecting Ys to Xs The outcome of a process can be measured in terms of certain variables of interest. For example, the quality of work performed can be measured in terms of the metrics ‘rework effort as a %age of total effort spent on performing the work’. This metrics can hence be taken as a variable of interest. Knowing the “future” value of this variable right at the beginning of the work can be effectively used for quantitative management. In such a case it would serve as a proactive act of defect prevention. Computing the metrics at the end of the work getting completed provides lagging indicators of the variables of interest and as such leaves limited or no scope for effective proactive actions. The ability to predict the “future” value of the variables of interest gives the power to quantitatively manage the process towards predefined desired goals.  Using Prediction Models Prediction Model, as the name also suggests, is meant to be used before the associated process has been performed to a significant degree of completion. At the beginning, the variable of interest has an unknown value and hence prediction makes business sense. After the process reaches a significant degree of completion, it may not be cost-effective to do predictions and hence no prediction should be carried out. Hence, a prediction model should operate in two stages: Stage 1: Right at the starting point It should provide an initial predicted value. Stage 2: At intermediate points till the end It should provide progressively refined predicted value as actual data on certain variables becomes known. The refinement should also consider any non-random events that may occur so that the prediction can be relied upon.