Project Management modeling techniques



A key factor in good estimate accuracy is updating estimates through the entire system development life cycle. As you get closer to project completion, most unknowns are better understood, and estimates become more likely predictors of actual results. CIOs also should obtain several estimates -- ptimistic, most likely, and pessimistic -- to gain a sense of the potential project time and cost range.

If you have empirical data on similar completed projects, you already have some initial information to gauge the accuracy of PM estimates.

A project manager needs to use a good tool that will allow him or her to create an initial estimate of the project's physical and human asset costs and schedule, and this should be placed into a baseline in the project plan.


If you don't have previous data on project performance, there are some best practices that can lead to good cost estimates. Keep in mind that even if you do have historical data, it's not a good idea to use that data alone. Empirical data is not always a good predictor, but it can be useful for initial estimates.


Best practices in PM estimation call for accurate predictions of a project's time and costs. To begin this process, you must know what tasks or deliverables are expected in the project. After you know the tasks, then estimates can be made as to timeframes and associated costs.

The use of modeling techniques in the requirements-gathering phase of a project is the best method I've seen to drive out tasks. If your project team is creating context models, event models, and information modelS -- such as entity relationship diagrams (ERDs) -- your project team will likely uncover all end user requirements and be able to develop a viable task list.

CONTEXT MODEL: A good context model defines all of the project's activities and processes. It involves data flow diagrams, as well as internal and external inputs and outputs. It will show the entire scope of the project system and will greatly reduce the potential for scope creep.

EVENT MODEL: The event model details the requirements of a project from an end user's perspective, rather than internal system events. An event list is created describing all customer or end user interactions with the new project or system.

INFORMATION MODEL: The information model, which is graphically displayed by ERDs, shows all of the data that needs to be captured by the project system. This is also known as a data model. The information model is probably the most important because it ensures that all project tasks become known.


Once the WBS is known, the PM team needs to provide estimates for time and cost. The best time and cost processes I've found are generally referred to as the top-down approach, the bottom-up approach, and parametric modeling. These estimation models enable a project team to more accurately predict and forecast task costs and timeframes from the good WBS created by the best practices of task listing noted above.

TOP-DOWN MODELS: This generally involves the judgments and experiences of senior management based upon previous data and experience. It is then sent down to lower layers of management and staff to further define project costs.

BOTTOM-UP MODELING: As indicated, this is the reverse of the top-down approach. It begins at the lowest layers of enterprise staff, who offer insight into project costs initially, and then the model is sent up the management chain for refinement and changes.

PARAMETRIC MODELING: This approach uses mathematical models, such as the construction cost model (COCOMO). COCOMO utilizes lines of code (LOC) methodologies or function point analysis in software-related projects to ascertain project costs. However, parametric models may involve arbitrary assessments of costs that are not always realistic, and may in fact distort project estimates and forecasts.