Book description
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
Table of contents
- About This E-Book
- Title Page
- Copyright Page
- Dedication Page
- Table of Contents
- Acknowledgments
- About the Author
-
Part 1: Approach
-
1. Project Management Analytics
- What Is Analytics?
- Why Is Analytics Important in Project Management?
- How Can Project Managers Use Analytics in Project Management?
- Project Management Analytics Approach
- Summary
- Key Terms
- Case Study: City of Medville Uses Statistical Approach to Estimate Costs for Its Pilot Project
- Case Study Questions
- Chapter Review and Discussion Questions
- Bibliography
-
2. Data-Driven Decision-Making
- Characteristics of a Good Decision
- Decision-Making Factors
- Importance of Decisive Project Managers
- Automation and Management of the Decision-Making Process
- Data-Driven Decision-Making
- Data-Driven Decision-Making Process Challenges
- Garbage In, Garbage Out
- Summary
- Key Terms
- Case Study: Kheri Construction, LLC
- Case Study Questions
- Chapter Review and Discussion Questions
- Bibliography
-
1. Project Management Analytics
-
Part 2: Project Management Fundamentals
-
3. Project Management Framework
- What Is a Project?
- How Is a Project Different from Operations?
- Project versus Program versus Portfolio
- Project Management Office (PMO)
- Project Life Cycle (PLC)
- Project Management Life Cycle (PMLC)
- A Process within the PMLC
- Work Breakdown Structure (WBS)
- Systems Development Life Cycle (SDLC)
- Summary
- Key Terms
- Case Study: Life Cycle of a Construction Project
- Case Study Questions
- Chapter Review and Discussion Questions
- Bibliography
-
3. Project Management Framework
-
Part 3: Introduction to Analytics Concepts, Tools, and Techniques
-
4. Statistical Fundamentals I: Basics and Probability Distributions
- Statistics Basics
-
Probability Distribution
- Random Variable
- Discrete versus Continuous Random Variables
- Mean of a Discrete Probability Distribution
- Variance of a Discrete Probability Distribution
- Standard Deviation of a Discrete Probability Distribution
- Expected Value of a Random Variable
- Mean, Deviation, Variance, and Standard Deviation of the Population
- Deviation of Each Data Value of the Population
- Mean, Deviation, Variance, and Standard Deviation of the Sample
- Standard Deviation Empirical Rule (or 68 – 95 – 99.7 Rule)
- Standard Score (or Z-Score)
- Mean, Variance, and Standard Deviation of a Binomial Distribution
- Poisson Distribution
- Normal Distribution
- Confidence Intervals
- Summary
- Key Terms
- Solutions to Example Problems
- Chapter Review and Discussion Questions
- Bibliography
-
5. Statistical Fundamentals II: Hypothesis, Correlation, and Linear Regression
- What Is a Hypothesis?
- Statistical Hypothesis Testing
- Rejection Region
- The z-Test versus the t-Test
- Correlation in Statistics
- Linear Regression
- Predicting y-Values Using the Multiple Regression Equation
- Summary
- Key Terms
- Solutions to Example Problems
- Chapter Review and Discussion Questions
- Bibliography
- 6. Analytic Hierarchy Process
-
7. Lean Six Sigma
- What Is Lean Six Sigma?
- How LSS Can Improve the Status Quo
- Lean Six Sigma Tools
- Summary
- Key Terms
- Case Study: Ropar Business Computers (RBC) Implements a Lean Six Sigma Project to Improve Its Server Test Process
- Select PDSA Cycles Explained
- Case Questions
- Chapter Review and Discussion Questions
- Bibliography
-
4. Statistical Fundamentals I: Basics and Probability Distributions
-
Part 4: Applications of Analytics Concepts, Tools, and Techniques in Project Management Decision-Making
-
8. Statistical Applications in Project Management
- Statistical Tools and Techniques for Project Management
- Probability Theory
- Probability Distributions
- Central Limit Theorem
- Critical Path Method (CPM)
- Critical Chain Method (CCM)
- Program Evaluation and Review Technique (PERT)
- Graphical Evaluation and Review Technique (GERT)
- Correlation and Covariance
- Predictive Analysis: Linear Regression
- Confidence Intervals: Prediction Using Earned Value Management (EVM) Coupled with Confidence Intervals
- Earned Value Management (EVM)
- Summary
- Key Terms
- Chapter Review and Discussion Questions
- Bibliography
- 9. Project Decision-Making with the Analytic Hierarchy Process (AHP)
-
10. Lean Six Sigma Applications in Project Management
- Common Project Management Challenges and LSS Remedies
- Project Management with Lean Six Sigma (PMLSS)—A Synergistic Blend
- PMLC versus LSS DMAIC Stages
- How LSS Tools and Techniques Can Help in the PMLC or the PMBOK Process Framework
- The Power of LSS Control Charts
- Agile Project Management and Lean Six Sigma
- Role of Lean Techniques in Agile Project Management
- Role of Six Sigma Tools and Techniques in the Agile Project Management
- Lean PMO: Using LSS’s DMEDI Methodology to Improve the PMO
- Summary
- Key Terms
- Case Study: Implementing the Lean PMO
- Case Questions
- Chapter Review and Discussion Questions
- Bibliography
-
8. Statistical Applications in Project Management
- Part 5: Appendices
- Index
Product information
- Title: Project Management Analytics: A Data-Driven Approach to Making Rational and Effective Project Decisions
- Author(s):
- Release date: November 2015
- Publisher(s): Pearson
- ISBN: 9780134190501
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