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Project Management Analytics: A Data-Driven Approach to Making Rational and Effective Project Decisions

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

  1. About This E-Book
  2. Title Page
  3. Copyright Page
  4. Dedication Page
  5. Table of Contents
  6. Acknowledgments
  7. About the Author
  8. Part 1: Approach
    1. 1. Project Management Analytics
      1. What Is Analytics?
        1. Analytics versus Analysis
      2. Why Is Analytics Important in Project Management?
      3. How Can Project Managers Use Analytics in Project Management?
      4. Project Management Analytics Approach
        1. Statistical Approach
        2. Lean Six Sigma Approach
        3. Analytic Hierarchy Process (AHP) Approach
      5. Summary
      6. Key Terms
      7. Case Study: City of Medville Uses Statistical Approach to Estimate Costs for Its Pilot Project
        1. Determine Target Age Group for Initial Project Pilot
        2. Estimate Project Costs for the Target Age Group
      8. Case Study Questions
      9. Chapter Review and Discussion Questions
      10. Bibliography
    2. 2. Data-Driven Decision-Making
      1. Characteristics of a Good Decision
      2. Decision-Making Factors
      3. Importance of Decisive Project Managers
        1. Time Is of the Essence
        2. Lead by Example
        3. Establish Credibility
        4. Resolve Conflicts and Other Project Problems
        5. Avoid Analysis Paralysis
      4. Automation and Management of the Decision-Making Process
      5. Data-Driven Decision-Making
      6. Data-Driven Decision-Making Process Challenges
      7. Garbage In, Garbage Out
      8. Summary
      9. Key Terms
      10. Case Study: Kheri Construction, LLC
        1. Background
        2. Problem
        3. Initial Investigation
        4. Further Root Cause Analysis (RCA)
        5. Decision-Making
        6. Action Plan
        7. Results
      11. Case Study Questions
      12. Chapter Review and Discussion Questions
      13. Bibliography
  9. Part 2: Project Management Fundamentals
    1. 3. Project Management Framework
      1. What Is a Project?
        1. Characteristics
        2. Constraints
        3. Success Criteria
        4. Why Projects Fail
      2. How Is a Project Different from Operations?
      3. Project versus Program versus Portfolio
        1. Project
        2. Program
        3. Portfolio
      4. Project Management Office (PMO)
      5. Project Life Cycle (PLC)
        1. Conceptual Stage
        2. Definition Stage
        3. Planning Stage
        4. Execution Stage
        5. Closing Stage
        6. Evaluation Stage
      6. Project Management Life Cycle (PMLC)
        1. Initiating Stage
        2. Planning Stage
        3. Executing and Controlling Stage
        4. Closing Stage
      7. A Process within the PMLC
      8. Work Breakdown Structure (WBS)
      9. Systems Development Life Cycle (SDLC)
        1. Feasibility Study
        2. Requirements Analysis and Planning
        3. Design
        4. Development
        5. Integration and Testing
        6. Implementation
        7. Operations and Maintenance
        8. Evaluation
      10. Summary
      11. Key Terms
      12. Case Study: Life Cycle of a Construction Project
        1. Background
        2. Challenge
        3. Project Concept and Definition
        4. Planning
        5. Execution
        6. Closing
        7. Evaluation
      13. Case Study Questions
      14. Chapter Review and Discussion Questions
      15. Bibliography
  10. Part 3: Introduction to Analytics Concepts, Tools, and Techniques
    1. 4. Statistical Fundamentals I: Basics and Probability Distributions
      1. Statistics Basics
        1. Terms to Know
        2. Classical or Theoretical Probability
        3. Empirical or Statistical Probability
        4. Probability Range
        5. Conditional Probability
        6. Designing a Statistical Study
        7. Measures of Central Tendency
        8. Range
      2. Probability Distribution
        1. Random Variable
        2. Discrete versus Continuous Random Variables
        3. Mean of a Discrete Probability Distribution
        4. Variance of a Discrete Probability Distribution
        5. Standard Deviation of a Discrete Probability Distribution
        6. Expected Value of a Random Variable
        7. Mean, Deviation, Variance, and Standard Deviation of the Population
        8. Deviation of Each Data Value of the Population
        9. Mean, Deviation, Variance, and Standard Deviation of the Sample
        10. Standard Deviation Empirical Rule (or 68 – 95 – 99.7 Rule)
        11. Standard Score (or Z-Score)
      3. Mean, Variance, and Standard Deviation of a Binomial Distribution
      4. Poisson Distribution
      5. Normal Distribution
        1. Standard Normal Distribution
        2. The Central Limit Theorem
      6. Confidence Intervals
        1. Point Estimate versus Interval Estimate
        2. Level of Confidence
        3. Identifying Confidence Intervals
      7. Summary
      8. Key Terms
      9. Solutions to Example Problems
      10. Chapter Review and Discussion Questions
      11. Bibliography
    2. 5. Statistical Fundamentals II: Hypothesis, Correlation, and Linear Regression
      1. What Is a Hypothesis?
      2. Statistical Hypothesis Testing
        1. Hypothesis Test
        2. Hypothesis Testing
      3. Rejection Region
      4. The z-Test versus the t-Test
        1. The z-Test
        2. The t-Test
      5. Correlation in Statistics
        1. Types of Correlation
        2. Correlation Coefficient
        3. Correlation and Causation
      6. Linear Regression
        1. The Regression Line Equation
        2. Using a Regression Equation to Predict y-Values
        3. Prediction Error
        4. Prediction Intervals
      7. Predicting y-Values Using the Multiple Regression Equation
      8. Summary
      9. Key Terms
      10. Solutions to Example Problems
      11. Chapter Review and Discussion Questions
      12. Bibliography
    3. 6. Analytic Hierarchy Process
      1. Using the AHP
        1. Determine the Evaluation Criteria
        2. Develop the Decision Hierarchy
        3. Perform the Analysis
        4. Synthesize and Rank Priorities
        5. Make a Decision
      2. AHP Pros and Cons
      3. Summary
      4. Key Terms
      5. Case Study: Topa Technologies Uses the AHP to Select the Project Manager
        1. Determine the Evaluation Criteria
        2. Develop the Decision Hierarchy
        3. Perform the Analysis
        4. Synthesize and Rank Priorities
        5. Make a Decision
      6. Conclusion
      7. Case Questions
      8. Chapter Review and Discussion Questions
      9. Bibliography
    4. 7. Lean Six Sigma
      1. What Is Lean Six Sigma?
        1. Lean
        2. Six Sigma
        3. Lean versus Six Sigma: Side-by-Side Comparison
      2. How LSS Can Improve the Status Quo
        1. DMAIC
        2. The PDSA Cycle
      3. Lean Six Sigma Tools
        1. Define Phase
        2. Measure Phase
        3. Analyze Phase
        4. Improve Phase
        5. Control Phase
      4. Summary
      5. Key Terms
      6. Case Study: Ropar Business Computers (RBC) Implements a Lean Six Sigma Project to Improve Its Server Test Process
        1. Define Project and Identify Project Team Members
        2. Define Improvement Objectives
        3. PDSA Cycles
      7. Select PDSA Cycles Explained
        1. PDSA 5: Root Cause Analysis (RCA)
        2. PDSA 7: Validating the Improvement Solution Effectiveness
        3. Conclusion
      8. Case Questions
      9. Chapter Review and Discussion Questions
      10. Bibliography
  11. Part 4: Applications of Analytics Concepts, Tools, and Techniques in Project Management Decision-Making
    1. 8. Statistical Applications in Project Management
      1. Statistical Tools and Techniques for Project Management
      2. Probability Theory
      3. Probability Distributions
      4. Central Limit Theorem
      5. Critical Path Method (CPM)
        1. Calculating Float for an Activity
        2. Shortening the Critical Path
      6. Critical Chain Method (CCM)
      7. Program Evaluation and Review Technique (PERT)
      8. Graphical Evaluation and Review Technique (GERT)
      9. Correlation and Covariance
      10. Predictive Analysis: Linear Regression
      11. Confidence Intervals: Prediction Using Earned Value Management (EVM) Coupled with Confidence Intervals
        1. Cost (Budget) Performance Measurement
        2. Time (Schedule) Performance Measurement
        3. Cost and Schedule Forecasting with Statistics
      12. Earned Value Management (EVM)
        1. Example EVM Problem
      13. Summary
      14. Key Terms
      15. Chapter Review and Discussion Questions
      16. Bibliography
    2. 9. Project Decision-Making with the Analytic Hierarchy Process (AHP)
      1. Project Evaluation and Selection
        1. 1. Determine Evaluation Criteria
        2. 2. Develop the Decision Hierarchy
        3. 3. Perform the Analysis
        4. 4. Synthesize and Rank Priorities
        5. 5. Make the Decision
      2. More Applications of the AHP in Project Management
        1. Project Complexity Estimation
        2. Project Risk Assessment
        3. Project Change Requests Prioritization
      3. Summary
      4. Key Terms
      5. Chapter Review and Discussion Questions
      6. Bibliography
    3. 10. Lean Six Sigma Applications in Project Management
      1. Common Project Management Challenges and LSS Remedies
      2. Project Management with Lean Six Sigma (PMLSS)—A Synergistic Blend
      3. PMLC versus LSS DMAIC Stages
        1. Initiating
        2. Planning
        3. Executing
        4. Monitoring and Controlling
        5. Closing
      4. How LSS Tools and Techniques Can Help in the PMLC or the PMBOK Process Framework
        1. Initiating
        2. Planning
        3. Executing
        4. Monitoring and Controlling
        5. Closing
      5. The Power of LSS Control Charts
      6. Agile Project Management and Lean Six Sigma
        1. What Is Agile?
        2. Agile versus Lean
      7. Role of Lean Techniques in Agile Project Management
      8. Role of Six Sigma Tools and Techniques in the Agile Project Management
      9. Lean PMO: Using LSS’s DMEDI Methodology to Improve the PMO
      10. Summary
      11. Key Terms
      12. Case Study: Implementing the Lean PMO
        1. Current State
        2. Target State
        3. Gap Analysis
        4. Filling the Gap
        5. Conclusion
      13. Case Questions
      14. Chapter Review and Discussion Questions
      15. Bibliography
  12. Part 5: Appendices
    1. A. z-Distribution
    2. B. t-Distribution
    3. C. Binomial Probability Distribution (From n = 2 to n = 10)
  13. Index