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

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  • Formaat: 352 pages
  • Ilmumisaeg: 05-Nov-2015
  • Kirjastus: Pearson FT Press
  • Keel: eng
  • ISBN-13: 9780134190488
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Project Management Analytics: A Data-Driven Approach to Making Rational and Effective Project DecisionsSuurem pilt
  • Formaat: 352 pages
  • Ilmumisaeg: 05-Nov-2015
  • Kirjastus: Pearson FT Press
  • Keel: eng
  • ISBN-13: 9780134190488
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To manage projects, you must not only control schedules and costs: you must also manage growing operational uncertainty. Todays 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. Youll 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 todays 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 dont have to. With Project Management Analytics, you can use facts, evidence, and knowledgeand 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

Muu info

>Project Management Analytics reveals what these tools can do, why theyre so valuable, and how any project manager can start using them. The first guide of its kind, it shows how analytics bridges the gap between raw data and effective decision-making in even the most challenging project environments.

 

Through real-world examples and case studies, Harjit Singh helps you gain comfort with modern analytics tools, technologies, and processes, and integrate them with approaches such as PMBOK, Lean Six Sigma DMAIC, and Demings PDSA.

 

Singh walks you through collecting data, analyzing it, and gaining stakeholder buy-in for analytics-driven approaches to PM decision-making. He illuminates opportunities to apply or improve your use of statistical probability distributions, decision trees, EVM, dashboards, scorecards, KPIs, PERT/CPM, regression analysis, NPV, cost-benefit analysis, SWOT analysis, and more. By the time youre done, youll have all the tools you need to gain the best possible knowledge and make the best possible decisions.
Part 1 Approach
Chapter 1 Project Management Analytics
1(24)
What Is Analytics?
2(2)
Why Is Analytics Important in Project Management?
4(1)
How Can Project Managers Use Analytics in Project Management?
4(4)
Project Management Analytics Approach
8(12)
Summary
20(1)
Key Terms
21(1)
Case Study: City of Medville Uses Statistical Approach to Estimate Costs for Its Pilot Project
21(2)
Case Study Questions
23(1)
Chapter Review and Discussion Questions
23(1)
Bibliography
24(1)
Chapter 2 Data-Driven Decision-Making
25(20)
Characteristics of a Good Decision
26(1)
Decision-Making Factors
27(1)
Importance of Decisive Project Managers
28(2)
Automation and Management of the Decision-Making Process
30(1)
Data-Driven Decision-Making
31(2)
Data-Driven Decision-Making Process Challenges
33(1)
Garbage In, Garbage Out
34(1)
Summary
34(1)
Key Terms
35(1)
Case Study: Kheri Construction, LLC
36(7)
Case Study Questions
43(1)
Chapter Review and Discussion Questions
43(1)
Bibliography
44(1)
Part 2 Project Management Fundamentals
Chapter 3 Project Management Framework
45(32)
What Is a Project?
46(6)
How Is a Project Different from Operations?
52(1)
Project versus Program versus Portfolio
53(2)
Project Management Office (PMO)
55(1)
Project Life Cycle (PLC)
55(5)
Project Management Life Cycle (PMLC)
60(5)
A Process within the PMLC
65(1)
Work Breakdown Structure (WBS)
66(1)
Systems Development Life Cycle (SDLC)
67(3)
Summary
70(2)
Key Terms
72(1)
Case Study: Life Cycle of a Construction Project
72(2)
Case Study Questions
74(1)
Chapter Review and Discussion Questions
75(1)
Bibliography
75(2)
Part 3 Introduction to Analytics Concepts, Tools, and Techniques
Chapter 4
Chapter Statistical Fundamentals I: Basics and Probability Distributions
77(40)
Statistics Basics
78(9)
Probability Distribution
87(6)
Mean, Variance, and Standard Deviation of a Binomial Distribution
93(2)
Poisson Distribution
95(1)
Normal Distribution
96(3)
Confidence Intervals
99(2)
Summary
101(2)
Key Terms
103(1)
Solutions to Example Problems
103(10)
Chapter Review and Discussion Questions
113(2)
Bibliography
115(2)
Chapter 5 Statistical Fundamentals II: Hypothesis, Correlation, and Linear Regression
117(34)
What Is a Hypothesis?
118(1)
Statistical Hypothesis Testing
119(6)
Rejection Region
125(2)
The z-Test versus the t-Test
127(4)
Correlation in Statistics
131(3)
Linear Regression
134(6)
Predicting y-Values Using the Multiple Regression Equation
140(2)
Summary
142(1)
Key Terms
143(1)
Solutions to Example Problems
143(5)
Chapter Review and Discussion Questions
148(1)
Bibliography
149(2)
Chapter 6 Analytic Hierarchy Process
151(32)
Using the AHP
152(10)
AHP Pros and Cons
162(1)
Summary
163(1)
Key Terms
164(1)
Case Study: Topa Technologies Uses the AHP to Select the Project Manager
164(15)
Conclusion
179(1)
Case Questions
180(1)
Chapter Review and Discussion Questions
180(1)
Bibliography
180(3)
Chapter 7 Lean Six Sigma
183(46)
What Is Lean Six Sigma?
184(5)
How LSS Can Improve the Status Quo
189(5)
Lean Six Sigma Tools
194(20)
Summary
214(1)
Key Terms
214(1)
Case Study: Ropar Business Computers (RBC) Implements a Lean Six Sigma Project to Improve Its Server Test Process
215(4)
Select PDSA Cycles Explained
219(6)
Case Questions
225(1)
Chapter Review and Discussion Questions
225(1)
Bibliography
226(3)
Part 4 Applications of Analytics Concepts, Tools, and Techniques in Project Management Decision-Making
Chapter 8 Statistical Applications in Project Management
229(36)
Statistical Tools and Techniques for Project Management
230(1)
Probability Theory
231(1)
Probability Distributions
231(1)
Central Limit Theorem
232(1)
Critical Path Method (CPM)
232(3)
Critical Chain Method (CCM)
235(2)
Program Evaluation and Review Technique (PERT)
237(2)
Graphical Evaluation and Review Technique (GERT)
239(2)
Correlation and Covariance
241(4)
Predictive Analysis: Linear Regression
245(6)
Confidence Intervals: Prediction Using Earned Value Management (EVM) Coupled with Confidence Intervals .
251(3)
Earned Value Management (EVM)
254(4)
Summary
258(2)
Key Terms
260(1)
Chapter Review and Discussion Questions
260(2)
Bibliography
262(3)
Chapter 9 Project Decision-Making with the Analytic Hierarchy Process (AHP)
265(26)
Project Evaluation and Selection
267(16)
More Applications of the AHP in Project Management
283(4)
Summary
287(1)
Key Terms
288(1)
Chapter Review and Discussion Questions
288(1)
Bibliography
288(3)
Chapter 10 Lean Six Sigma Applications in Project Management
291(30)
Common Project Management Challenges and LSS Remedies
292(1)
Project Management with Lean Six Sigma (PMLSS)-A Synergistic Blend
293(1)
PMLC versus LSS DMAIC Stages
294(4)
How LSS Tools and Techniques Can Help in the PMLC or the PMBOK4 Process Framework
298(8)
The Power of LSS Control Charts
306(1)
Agile Project Management and Lean Six Sigma
307(1)
Role of Lean Techniques in Agile Project Management
308(2)
Role of Six Sigma Tools and Techniques in the Agile Project Management
310(1)
Lean PMO: Using LSS's DMEDI Methodology to Improve the PM0
310(2)
Summary
312(1)
Key Terms
313(1)
Case Study: Implementing the Lean PMO
313(5)
Case Questions
318(1)
Chapter Review and Discussion Questions
318(1)
Bibliography
319
Part 5 Appendices
Appendix A z-Distribution
321(4)
Appendix B t-Distribution
325(2)
Appendix C Binomial Probability Distribution (From n = 2 to n = 10)
327(2)
Index 329
Harjit Singh earned his MBA from University of Texas and his masters degree in Computer Engineering from California State University, Sacramento. He is a Certified Scrum Master, Lean Six Sigma professional, and holds PMP (Project Management Professional) credentials. He has more than 25 years of experience in the private and public sector as an information technology engineer, project manager, and educator. Currently, he is working as a data processing manager III at the State of California. In addition, he is also a visiting professor/adjunct faculty at Keller Graduate School of Management, DeVry University and Brandman University, where he teaches project management, business management, and information technology courses. Prior to this, he worked at Hewlett-Packard for 15 years as a systems software engineer and technical project manager. He is also a former member of the Board of Directors for the Sacramento Valley Chapter of the Project Management Institute (PMI) where he served in the capacity of CIO and vice president of relations and marketing.
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