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Project Risk
Tom R. Wielicki
Donald N. Stengel
其他書名
Concepts, Process, and Tools
出版
Business Expert Press
, 2014-07-15
主題
Business & Economics / Decision-Making & Problem Solving
Business & Economics / Project Management
ISBN
1606495062
9781606495063
URL
http://books.google.com.hk/books?id=SNfOmAEACAAJ&hl=&source=gbs_api
註釋
In this era of volatile economic conditions and rapid change, it is important that managers understand and account for risk. Project risk management is a body of knowledge and techniques for addressing these uncertainties as part of project planning and execution. Project risk management facilitates the identification of risks, the assessment of their likelihood and severity, and decisions whether to accept, mitigate, eliminate, or transfer those risks, as well as the creation of respective contingency plans. The Project Management Book of Knowledge (PMBOK), a compilation by the Project Management Institute (PMI), provides guidelines for these risk planning activities. We will cite and discuss those guidelines in this text. We will also provide short vignettes that illustrate successful and unsuccessful project risk management. Techniques for project risk management can be classified as qualitative or quantitative. Most good project managers employ qualitative techniques and this book will discuss some of the key methods for qualitative risk management, including brainstorming, prioritizing, Delphi, and cause-and-effect diagrams. These techniques are useful in identifying possible risk, the consequences of those risks, and actions that can be taken. We will also discuss practices for documenting these risks, such as a risk register. However, many project managers, even those with considerable experience, do not regularly employ quantitative risk management techniques. One of the main goals in this book is to convince readers that quantitative project risk management is doable and worthwhile. We will present 'classical' approaches to quantitative risk management, such as decision trees, PERT/CPM, failure mode and effects analysis, and financial engineering. We will also present simulation approaches, specifically Monte Carlo analysis. We will illustrate these techniques using Microsoft Project and Palisades @Risk software, focusing on managerial interpretations of statistical results delivered by project simulation experiments.
