Cmme process domain-ppm Process Performance Model

1. ppm associates the current behavior of the process or sub-process with the final result of the environment and process. PPM predicts the final results of the process through possible changes to actual factors (such as what-if ). In this prediction process, the model involves the process units and impact factors of one or more sub-processes.

2. The impact factors used in the PPM process model are preferably controllable, so that the project can take various corrective measures to influence the final process output. In nature, statistics and probability distribution are more than the only data to be determined. QPM quantize project management is to use statistical methods to explain deviations, the final result fluctuation range of the process is predicted by establishing a model when the impact factors in the process model change.

3. with regards to the PPM model, organizations with high maturity levels cannot only establish project cost and deviation forecasts based on Earned Value Management and measurement, instead, we need to establish a set of process performance models to predict whether the process performance and product quality objectives can be achieved. (Quality, cost, cycle, defect escape, customer satisfaction, and process effectiveness ). In particular, in order to be able to predict the project quality and process performance goals, we need to use various factor data in multiple sub-processes. These Sub-processes may involve planning, development, implementation and other stages of the project life cycle.

How to use the process performance model ppm, the main function of ppm is to predict the results, the process performance goals and quality objectives are affected by multiple other sub-process parameter factors in the process performance model. Therefore, with ppm, we can consider how to monitor and improve X to reach a specific target value Y. Only when X is controlled and improved can we reach the ideal target value. In this process, we need to use what-if analysis (using the crystal ball tool) to determine how to adjust and improve X. This is the method of Monte Carlo simulation. At the same time, X needs to be monitored during execution or the factors that affect X to ensure that the process is controlled and stable. When all factors are controlled and stable, naturally, we can reach our target value. All ppm is not only used to predict the results, but the model gives us a continuous improvement opportunity and how to improve X to achieve our desired goal.

Where is regression and correlation analysis used? It is used when building ppm. First, we can analyze which X may affect our target Result Y and check whether there is a correlation between these X and Y, select y to establish a regression model (Multiple Regression Model ). In addition, where is ANOVA Variance analysis used? The focus of variance analysis is to check whether there is a significant difference in mean between groups. Therefore, we need to use variance analysis when deciding whether to establish multiple ppm based on different types and conditions. For example, when we build a productivity model, we collect multiple groups of data on productivity in different development languages. If there are significant differences in productivity between different development languages, we should consider building multiple models.

How to use the system dynamics model, we know that Y = f (x1, x2, X3, X4 ,....), to achieve this goal, we need to optimize and adjust x, but this model does not express the interaction between X. For example, adjusting X1 may affect other X. Therefore, we need to establish a system model to identify the interaction and force between X, and perform what-if analysis and simulation.