Practical Schedule Risk Analysis

Practical Schedule Risk Analysis, contains a complete treatment of schedule risk analysis from basic to advanced concepts. Benefits of the process, relationship to traditional critical path method scheduling and to project management, the role of the corporate culture in ensuring success and the criticality of collecting risk data are emphasized for the benefit of the practitioner. The final chapter illustrates how schedule risk is related to cost risk and showing how to get the information out of the schedule, effectively predicting the second book in the series, practical cost risk analysis.The methods are introduced at the simplest level (e.g., Why is the duration of an activity uncertain? and How do we represent that uncertainty with a probability distribution?) and are progressively elaborated (e.g., How does uncertainty of activities along a path lead to more uncertainty of the path's completion date?) with simple examples illustrating the point at each step of the way. At the end of this progression, the most powerful and advanced capabilities available today in commercial software are discussed. This method of progressive elaboration has been proven successful in the classroom and lectures at conferences.The intent is to present an approach that is practical. People with all types of projects and in all industries should be able to apply it themselves. Schedule risk analysis is a process that is industry-independent, and examples from real projects (specifics not revealed) are referenced from construction, oil and gas, information systems, environmental restoration and aerospace / defense.One specific goal is to de-mystify the process. Some people are put off by the term 'quantitative schedule risk analysis', the use of statistical terms and the mention of tools such as Monte Carlo simulation. The fact is that practitioners will not need to know statistics in order to be successful schedule risk analysts. Any statistical concepts needed are explained in simple terms and the minimum of statistics are used here (e.g., What is a probability distribution and why should we use it? or How do I interpret the results of a Monte Carlo simulation?).