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To manage projects, you must not only control schedules and costs: you must 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® 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-making
Systematically bring data and objective analysis to key project decisions
Avoid “garbage in, garbage out”
Properly collect, store, analyze, and interpret your project-related data
Optimize multi-criteria decisions in large group environments
Use the Analytic Hierarchy Process (AHP) to improve complex real-world decisions
Streamline projects the way you streamline other business processes
Leverage data-driven Lean Six Sigma to manage projects more effectively
Introduction to Project Management Analytics: A Data-Driven Approach to Making Rational and Effective Project Decisions
Download the sample pages (includes Chapter 1 and Index)
Part 1: Approach
Chapter 1: Project Management Analytics 1
Chapter 2: Data-Driven Decision-Making 25
Part 2: Project Management Fundamentals
Chapter 3: Project Management Framework 45
Part 3: Introduction to Analytics Concepts, Tools, and Techniques
Chapter 4: Chapter Statistical Fundamentals I: Basics and Probability Distributions 77
Chapter 5: Statistical Fundamentals II: Hypothesis, Correlation, and Linear Regression 117
Chapter 6: Analytic Hierarchy Process 151
Chapter 7: Lean Six Sigma 183
Part 4: Applications of Analytics Concepts, Tools, and Techniques in Project Management Decision-Making
Chapter 8: Statistical Applications in Project Management 229
Chapter 9: Project Decision-Making with the Analytic Hierarchy Process (AHP) 265
Chapter 10: Lean Six Sigma Applications in Project Management 291
Part 5: Appendices
Appendix A: z-Distribution 321
Appendix B: t-Distribution 325
Appendix C: Binomial Probability Distribution (From n = 2 to n = 10) 327