Project Risk Assessment using @RISK for Project is designed to introduce project managers to the concepts and methods necessary for developing a defensible risk analysis. The software used is @RISK for Project and Microsoft Project. Students will discover how to translate known information about a project’s risk into a model that can be used to quantify exposure and test mitigation strategies. Throughout the course, several examples will be presented to the students as class exercises. These examples will demonstrate various modeling techniques, as well as the interpretation of the risk analysis results.

Palisade Corporation is a PMI Registered Education Provider

Project Management Professionals attending this course can earn 4 PMI Professional Development Units.
Upon completion of this course, students will have been exposed to a wide range of modeling methods and will have the necessary prerequisites for performing a risk analysis on schedules using Microsoft Project and @RISK for Project.

1. Introduction to Project Risk Analysis
2. Introduction to Probability, Background Statistics, Monte Carlo Simulation, and Common Distributions used in Project  Risk Analysis
3. Getting Started with @RISK for Project  
   • The @RISK Interface: menus, icons, windows and dialogs
   • Developing a simple schedule model
   • Adding uncertainty and variation to the Model
   • Simulating a project schedule
4. Interpretation of Risk Analysis Results
   • Determining the probability of finishing a project on time
   • Determining and allocating schedule contingencies
   • Performing a sensitivity analysis to determine key drivers affecting total duration
   • Generating simple reports of result

1. Using Historical Data to perform Distribution Fitting
2. Incorporating Expert Opinion in risk analysis models
3. Advanced @RISK Modeling Techniques:
   • Creating a probabilistic Gantt charts
   • Probabilistic and conditional branching
   • Determining how critical  project activities are (critical index)
   • Simulating cost-loaded schedules
4. Correlating variables in the schedule model