Duration: 2 days
Analyzing the time to complete a project using project planning tools nearly always underestimates the time to completion. It is not the fault of the software or of the analysts, but of the use of 'best guess' values for task durations, etc. in the project plan. Risk analysis will allow you to avoid systematically underestimating project costs and durations.
The project risk analysis course is designed to help those who wish to apply quantitative risk analysis modeling to project planning problems.
A project is defined as any set of tasks involving resources (human, machine, time, financial) with well-defined goals. Project risk analysis aims at identifying the risks and uncertainties that threaten the achievement of those goals or the efficiency with which the project can be carried out. The techniques are very general and intuitive and require little mathematical knowledge, but reward the project manager with a clear understanding of the risks being faced and efficient ways of managing those risks.
The course looks at resource, strategy and communication issues that management faces in risk assessment. It gets the participants used to the risk analysis modeling environment (in this case Crystal Ball with MS Excel and Crystal Ball with MS Project or @RISK with MS Excel and @RISK with MS Project, but the lessons apply equally well to other modeling environments).
This course is suited to those already familiar with project planning, who have some modeling experience and who are interested in developing these abilities further. The course content will provide the participants with tools to produce realistic, professional quality risk models. It is designed to encourage the modeler to develop creative problem solving skills to ensure that the risks being addressed are modeled accurately, efficiently and in a manner that provides decision makers with the clearest and most helpful input.
All lecture notes are provided as pdf files. A CD of the course files and all model files produced for the course are provided to each participant. Printed handouts are also provided. Any extra models developed during the course are downloadable from a private page on this website dedicated to the course.
The course runs from 09:00 to 17:00 each day. Morning and afternoon coffee, and lunch are provided. Optional evening workshops on the first and second days allow extra time for running through example models and exercises.
Who should attend?
The course is ideally suited to project managers, project analysts, management consultants, and corporate planners, indeed anyone evaluating the schedule and cost risks associated with any type of project from building construction to software development.
All models are developed using MS Excel and MS Project and Crystal Ball or @RISK. It is important that participants have a basic working knowledge of Excel. However, no prior experience using @RISK, Crystal Ball, or other simulation tools is required. Trial versions of @Risk can be found from Palisade's website here and trials of Crystal Ball can be downloaded from Oracle's website. As the trial licenses last 15 days, participants should install it only a few days before the course.
In addition to @Risk/CB and MS Excel, participants are required to bring laptops with Microsoft Word, Microsoft Project, pdf reader software, and with a functional CD drive.
ModelAssist is a comprehensive risk analysis training reference and is free of cost. This reference tool provides an in-depth explanation of all of the risk analysis concepts, techniques and methods introduced in this course and greatly complements the course material. ModelAssist can be downloaded directly from our website here.
All of our courses aim to help the participants understand risk analysis from the bottom-up, which is achieved through a relaxed, informal and interactive environment using plenty of examples and hands-on exercises where students apply and adapt what they have learned.
Introduction to Crystal Ball/Excel or @RISK/Project and risk modeling
- Why risk models give more realistic targets than single point estimate models
- Planning and structuring a model
- Distributions to use to reflect uncertainty From fitting to data From expert opinion
Presentations of model outputs
- Standard graphics: relative and cumulative plots, tornado charts
- Standard statistics: mean percentiles, critical indices, etc.
- Exporting results to Excel for further analysis
- Probabilistic branching
- If/Then branching
- External influence variables
- Probabilistic calendars
- Determining milestones
- Common pitfalls
- Linking and analyzing cost and schedule models together
- Tracking cash flow