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Full Description
To manage projects, you must not only control schedules and costsmust also manage growing operational uncertainty. Today's powerful analytics tools and methods can help you do all of this far more successfully. In Project Management Analytics, Harjit Singh shows how to bring greater evidence-based clarity and rationality to all your key decisions throughout the full project lifecycle.Singh identifies the components and characteristics of a good project decision and shows how to improve decisions by using predictive, prescriptive, statistical, and other methods. You'll learn how to mitigate risks by identifying meaningful historical patterns and trends; optimize allocation and use of scarce resources within project constraints; automate data-driven decision-making processes based on huge data sets; and effectively handle multiple interrelated decision criteria.Singh also helps you integrate analytics into the project management methods you already use, combining today's best analytical techniques with proven approaches such as PMI PMBOK (R) and Lean Six Sigma.Project managers can no longer rely on vague impressions or seat-of-the-pants intuition. Fortunately, you don't have to. With Project Management Analytics, you can use facts, evidence, and knowledge-and get far better results.Achieve efficient, reliable, consistent, and fact-based project decision-makingSystematically bring data and objective analysis to key project decisionsAvoid "garbage in, garbage out"Properly collect, store, analyze, and interpret your project-related data Optimize multi-criteria decisions in large group environmentsUse the Analytic Hierarchy Process (AHP) to improve complex real-world decisions Streamline projects the way you streamline other business processesLeverage data-driven Lean Six Sigma to manage projects more effectively
Contents
Part 1Chapter 1: Project Management Analytics 1Chapter 2: Data-Driven Decision-Making 25Part 2: Project Management FundamentalsChapter 3: Project Management Framework 45Part 3: Introduction to Analytics Concepts, Tools, and TechniquesChapter 4: Chapter Statistical Fundamentals I: Basics and Probability Distributions 77Chapter 5: Statistical Fundamentals II: Hypothesis, Correlation, and Linear Regression 117Chapter 6: Analytic Hierarchy Process 151Chapter 7: Lean Six Sigma 183Part 4: Applications of Analytics Concepts, Tools, and Techniques in Project Management Decision-MakingChapter 8: Statistical Applications in Project Management 229Chapter 9: Project Decision-Making with the Analytic Hierarchy Process (AHP) 265Chapter 10: Lean Six Sigma Applications in Project Management 291Part 5: AppendicesAppendix A: z-Distribution 321Appendix B: t-Distribution 325Appendix C: Binomial Probability Distribution (From n = 2 to n = 10) 327Index 329
