CALUMS Data Driven Decision Making in Project Management Questions

1.)

What are the advantages of using data-driven decision-making in project management? Discuss some challenges associated with the data-driven decision-making process.

(for 1st question, please write minimum of 850–950 words answer and also include the references.)

2.)

Read the case study, Data-Driven Decision-Making, and provide your response to the following questions: What data analytics tools did Rick Albany use to capture and analyze the data in this case? What is fishbone analysis? How does it help in decision-making? How effective was data-driven decision-making in this case?   Project Management
Analytics
A Data-Driven Approach to Making
Rational and Effective
Project Decisions
Harjit Singh, MBA, PMP, CSM
Data Processing Manager III, State of California
Publisher: Paul Boger
Editor-in-Chief: Amy Neidlinger
Executive Editor: Jeanne Glasser Levine
Development Editor: Natasha Wollmers
Cover Designer: Chuti Prasertsith
Managing Editor: Kristy Hart
Project Editor: Elaine Wiley
Copy Editor: Paula Lowell
Proofreader: Chuck Hutchinson
Indexer: MoJo’s Indexing and Editorial Services
Compositor: Nonie Ratcliff
Manufacturing Buyer: Dan Uhrig
© 2016 by Harjit Singh
Published by Pearson Education, Inc.
Old Tappan, New Jersey 07675
For information about buying this title in bulk quantities, or for special sales opportunities (which may include electronic versions; custom cover designs; and content particular to your business, training goals, marketing focus, or
branding interests), please contact our corporate sales department at corpsales@pearsoned.com or (800) 382-3419.
For government sales inquiries, please contact governmentsales@pearsoned.com.
For questions about sales outside the U.S., please contact international@pearsoned.com.
Company and product names mentioned herein are the trademarks or registered trademarks of their respective owners.
All rights reserved. No part of this book may be reproduced, in any form or by any means, without permission in writing from the publisher.
Printed in the United States of America
First Printing November 2015
ISBN-10: 0-13-418994-9
ISBN-13: 978-0-13-418994-9
Pearson Education LTD.
Pearson Education Australia PTY, Limited
Pearson Education Singapore, Pte. Ltd.
Pearson Education Asia, Ltd.
Pearson Education Canada, Ltd.
Pearson Educación de Mexico, S.A. de C.V.
Pearson Education—Japan
Pearson Education Malaysia, Pte. Ltd.
Library of Congress Control Number: 2015949108
ii
Table of Contents
To my father, Sardar Puran Singh,
from whom I learned hard work, honesty, and work ethics,
and
to my wife Harjinder and daughters Kavleen and Amanroop
for their patience, unconditional love, and constant inspiration
throughout this project!
This page intentionally left blank
Table of Contents
Part 1 Approach
Chapter 1
Project Management Analytics . . . . . . . . . . . . . . . . 1
What Is Analytics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2
Why Is Analytics Important in Project Management?. . . . . . . . . .4
How Can Project Managers Use Analytics in Project
Management?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4
Project Management Analytics Approach . . . . . . . . . . . . . . . . . . . .8
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20
Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21
Case Study: City of Medville Uses Statistical Approach to
Estimate Costs for Its Pilot Project . . . . . . . . . . . . . . . . . . . . . .21
Case Study Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23
Chapter Review and Discussion Questions . . . . . . . . . . . . . . . . . .23
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24
Chapter 2
Data-Driven Decision-Making . . . . . . . . . . . . . . . . 25
Characteristics of a Good Decision . . . . . . . . . . . . . . . . . . . . . . . . .26
Decision-Making Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27
Importance of Decisive Project Managers . . . . . . . . . . . . . . . . . . .28
Automation and Management of the Decision-Making
Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30
Data-Driven Decision-Making . . . . . . . . . . . . . . . . . . . . . . . . . . . .31
Data-Driven Decision-Making Process Challenges . . . . . . . . . . .33
Garbage In, Garbage Out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34
Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35
Case Study: Kheri Construction, LLC. . . . . . . . . . . . . . . . . . . . . . .36
Case Study Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43
Chapter Review and Discussion Questions . . . . . . . . . . . . . . . . . .43
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44
Part 2 Project Management Fundamentals
Chapter 3
Project Management Framework . . . . . . . . . . . . . 45
What Is a Project? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .46
How Is a Project Different from Operations? . . . . . . . . . . . . . . . .52
Project versus Program versus Portfolio . . . . . . . . . . . . . . . . . . . .53
Project Management Office (PMO) . . . . . . . . . . . . . . . . . . . . . . . .55
Project Life Cycle (PLC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55
Project Management Life Cycle (PMLC) . . . . . . . . . . . . . . . . . . . .60
A Process within the PMLC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .65
Work Breakdown Structure (WBS) . . . . . . . . . . . . . . . . . . . . . . . .66
Systems Development Life Cycle (SDLC) . . . . . . . . . . . . . . . . . . .67
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70
Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .72
Case Study: Life Cycle of a Construction Project . . . . . . . . . . . . .72
Case Study Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .74
Chapter Review and Discussion Questions . . . . . . . . . . . . . . . . . .75
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75
Part 3 Introduction to Analytics Concepts, Tools,
and Techniques
Chapter 4
Chapter Statistical Fundamentals I:
Basics and Probability Distributions . . . . . . . . . . 77
Statistics Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .78
Probability Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .87
Mean, Variance, and Standard Deviation of a Binomial
Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .93
Poisson Distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .95
Normal Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .96
Confidence Intervals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .99
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Solutions to Example Problems . . . . . . . . . . . . . . . . . . . . . . . . . 103
Chapter Review and Discussion Questions . . . . . . . . . . . . . . . . 113
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
vi
Table of Contents
Chapter 5
Statistical Fundamentals II: Hypothesis,
Correlation, and Linear Regression. . . . . . . . . . . 117
What Is a Hypothesis? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
Statistical Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Rejection Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
The z-Test versus the t-Test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
Correlation in Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
Predicting y-Values Using the Multiple Regression
Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
Solutions to Example Problems . . . . . . . . . . . . . . . . . . . . . . . . . 143
Chapter Review and Discussion Questions . . . . . . . . . . . . . . . . 148
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Chapter 6
Analytic Hierarchy Process. . . . . . . . . . . . . . . . . . 151
Using the AHP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
AHP Pros and Cons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
Case Study: Topa Technologies Uses the AHP to Select
the Project Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
Case Questions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
Chapter Review and Discussion Questions . . . . . . . . . . . . . . . . 180
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
Chapter 7
Lean Six Sigma . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
What Is Lean Six Sigma? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
How LSS Can Improve the Status Quo. . . . . . . . . . . . . . . . . . . . 189
Lean Six Sigma Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
Case Study: Ropar Business Computers (RBC) Implements
a Lean Six Sigma Project to Improve Its Server
Test Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Select PDSA Cycles Explained . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
Table of Contents
vii
Case Questions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
Chapter Review and Discussion Questions . . . . . . . . . . . . . . . . 225
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
Part 4 Applications of Analytics Concepts, Tools, and
Techniques in Project Management
Decision-Making
Chapter 8
Statistical Applications in Project
Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
Statistical Tools and Techniques for Project Management . . . 230
Probability Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
Central Limit Theorem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
Critical Path Method (CPM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
Critical Chain Method (CCM) . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
Program Evaluation and Review Technique (PERT) . . . . . . . . 237
Graphical Evaluation and Review Technique (GERT). . . . . . . 239
Correlation and Covariance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Predictive Analysis: Linear Regression . . . . . . . . . . . . . . . . . . . 245
Confidence Intervals: Prediction Using Earned Value
Management (EVM) Coupled with Confidence Intervals . 251
Earned Value Management (EVM) . . . . . . . . . . . . . . . . . . . . . . 254
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258
Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260
Chapter Review and Discussion Questions . . . . . . . . . . . . . . . . 260
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262
Chapter 9
Project Decision-Making with the Analytic
Hierarchy Process (AHP) . . . . . . . . . . . . . . . . . . . 265
Project Evaluation and Selection. . . . . . . . . . . . . . . . . . . . . . . . . 267
More Applications of the AHP in Project Management . . . . . 283
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287
Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288
Chapter Review and Discussion Questions . . . . . . . . . . . . . . . . 288
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288
viii
Table of Contents
Chapter 10
Lean Six Sigma Applications
in Project Management . . . . . . . . . . . . . . . . . . . . . 291
Common Project Management Challenges and LSS
Remedies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292
Project Management with Lean Six Sigma (PMLSS)—
A Synergistic Blend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293
PMLC versus LSS DMAIC Stages . . . . . . . . . . . . . . . . . . . . . . . . 294
How LSS Tools and Techniques Can Help in the PMLC
or the PMBOK4 Process Framework. . . . . . . . . . . . . . . . . . . 298
The Power of LSS Control Charts . . . . . . . . . . . . . . . . . . . . . . . . 306
Agile Project Management and Lean Six Sigma . . . . . . . . . . . . 307
Role of Lean Techniques in Agile Project Management . . . . . 308
Role of Six Sigma Tools and Techniques in the Agile
Project Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
Lean PMO: Using LSS’s DMEDI Methodology to
Improve the PMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312
Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
Case Study: Implementing the Lean PMO. . . . . . . . . . . . . . . . . 313
Case Questions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318
Chapter Review and Discussion Questions . . . . . . . . . . . . . . . . 318
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319
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
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
Table of Contents
ix
Acknowledgments
I would like to acknowledge the contributions of my friends, past and present colleagues,
supervisors, and students who helped me bring this book to life with their valuable
feedback, inspiration, moral support, and encouragement. In particular, I extend my sincere thanks to Amy Cox-O’Farrell (Chief Information Officer, Department of Consumer
Affairs, State of California), Mary Cole (President, DeVry University, Folsom, California), Mark Stackpole, MA (Academic Affairs Specialist, DeVry University, Folsom, California), Staff (Sigma PM Consulting, Rocklin, California), Jaswant Saini (President, Saini
Immigration, Fresno, California and Chandigarh, India), Surinder Singh (President,
Singh Construction, Corona, California), JP Singh (President, Omega Machine & Tool,
W. Sacramento, California), Jaspreet Singh (President, Wesco Enterprises, Rancho Cordova, California), Ric Albani (President, RMA Consulting Group, Sacramento, California), and Laura Lorenzo (former President, Project Management Institute, Sacramento
Valley Chapter).
My special thanks go to Randal Wilson, MBA, PMP (author, Operations and Project
Manager at Parker Hose and Fittings, and Visiting Professor at Keller Graduate School
of Management, DeVry University, Folsom, California), Dr. Bob Biswas (author and
Associate Professor of Accounting at Keller Graduate School of Management, DeVry
University, Folsom, California), Gopal Kapur (founder of the Center for Project Management and Family Green Survival, Roseville, California), and Jorge Avila (Project Director,
Office of Technology, State of California) for their expert guidance and encouragement
to me throughout this project.
I sincerely appreciate Jeanne Glasser Levine (Project Executive Editor at Pearson), Elaine
Wiley (Project Editor at Pearson), Natasha Lee (Development Editor for Pearson) and
Paula Lowell (Copy Editor for Pearson) for their thorough reviews, critique of the manuscript, valuable suggestions, and support to keep me motivated. In addition, many thanks
to Paul Boger and his production crew at Pearson for their hard work in making this
project a reality.
Last but not least, I am indebted to my wife Harjinder and my daughters Kavleen and
Amanroop for their understanding, encouragement, and steadfast support during this
journey.
Harjit Singh
Rocklin, California
About the Author
Harjit Singh earned his MBA from University of Texas
and his master’s 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.
This page intentionally left blank
1
Project Management Analytics
Learning Objectives
After reading this chapter, you should be familiar with the
■
Definition of analytics
■
Difference between analytics and analysis
■
Purpose of using analytics in project management
■
Applications of analytics in project management
■
Statistical approach to project management analytics
■
Lean Six Sigma approach to project management analytics
■
Analytic Hierarchy Process approach to project management analytics
“Information is a source of learning. But unless it is organized, processed, and available
to the right people in a format for decision making, it is a burden, not a benefit.”
—William Pollard (1828–1893), English Clergyman
Effective project management entails operative management of uncertainty on the project. This requires the project managers today to use analytical techniques to monitor and
control the uncertainty as well as to estimate project schedule and cost more accurately
with analytics-driven prediction. Bharat Gera, Line Manager at IBM agrees, “Today,
project managers need to report the project metrics in terms of ‘analytical certainty.’”
Analytics-based project metrics can essentially enable the project managers to measure, observe, and analyze project performance objectively and make rational project
decisions with analytical certainty rather than making vague decisions with subjective
uncertainty. This chapter presents you an overview of the analytics-driven approach to
project management.
1
What Is Analytics?
Analytics (or data analytics) can be defined as the systematic quantitative analysis of data
or statistics to obtain meaningful information for better decision-making. It involves the
collective use of various analytical methodologies, including but not limited to statistical
and operational research methodologies, Lean Six Sigma, and software programming.
The computational complexity of analytics may vary from low to very high (for example,
big data). The highly complex applications usually utilize sophisticated algorithms based
on statistical, mathematical, and computer science knowledge.
Analytics versus Analysis
Analysis and analytics are similar-sounding terms, but they are not the same thing. They
do have some differences.
Both are important to project managers. They (project managers) can use analysis to
understand the status quo that may reflect the result of their efforts to achieve certain
objectives. They can use analytics to identify specific trends or patterns in the data under
analysis so that they can predict or forecast the future outcomes or behaviors based on
the past trends.
Table 1.1 outlines the key differences between analytics and analysis.
Table 1.1
Analytics vs. Analysis
Criterion
Analytics
Analysis
Working
Definition
Analytics can be defined as a
method to use the results of
analysis to better predict customer or stakeholder behaviors.
Analysis can be defined as the
process of dissecting past gathered
data into pieces so that the current (prevailing) situation can be
understood.
Dictionary
Definition
Per Merriam-Webster dictionary, analytics is the method of
logical analysis.
Per Merriam-Webster dictionary,
analysis is the separation of a whole
into its component parts to learn
about those parts.
Time Period
Analytics look forward to
project the future or predict
an outcome based on the past
performance as of the time of
analysis.
Analysis presents a historical view
of the project performance as of the
time of analysis.
2
Project Management Analytics
Criterion
Analytics
Analysis
Examples
Use analytics to predict which
functional areas are more
likely to show adequate participation in future surveys so
that a strategy can be developed to improve the future
participation.
Use analysis to determine how
many employees from each functional area of the organization participated in a voice of the workforce
survey.
Types of Analysis
Prediction of future audience
behaviors based on their past
behaviors
Target audience segmentation
Statistical, mathematical, computer science, and Lean Six
Sigma tools, and techniquesbased algorithms with advanced
logic
Business intelligence tools
Tools
Target audience grouping based on
multiple past behaviors
Structured query language (SQL)
Sophisticated predictive analytics software tools
Typical Activities
Identify specific data patterns
Develop a business case
Derive meaningful inferences
from data patterns
Elicit requirements
Document requirements
Use inferences to develop regres- Conduct risk assessment
sive/predictive models
Model business processes
Use predictive models
Develop business architecture
for rational and effective
decision-making
Develop a SharePoint list to track
key performance indicators
Run SQL queries on a data warehouse to extract relevant data for
reporting
Run simulations to investigate
different scenarios
Use statistical methods to predict
future sales based on past sales
data
Chapter 1 Project Management Analytics
3
Why Is Analytics Important in Project Management?
Although switching to the data-driven approach and utilizing the available analytical
tools makes perfect sense, most project managers either are not aware of the analytical
approach or they do not feel comfortable moving away from their largely subjective
legacy approach to project management decision-making. Their hesitation is related to
lack of training in the analytical tools, technologies, and processes. Most project management books only mention these tools, technologies, and processes in passing and do not
discuss them adequately and in an easily adaptable format. Even the Project Management
Body of Knowledge Guide (PMBOK), which is considered the global standard for project management processes, does not provide adequate details on an analytics-focused
approach.
The high availability of analytical technology today can enable project managers to use
the analytics paradigm to break down the processes and systems in complex projects to
predict their behavior and outcomes. Project managers can use this predictive information to make better decisions and keep projects on schedule and on budget. Analytics
does more than simply enable project managers to capture data and mark the tasks done
when completed. It enables them to analyze the captured data to understand certain
patterns or trends. They can then use that understanding to determine how projects
or project portfolios are performing, and what strategic decisions they need to make to
improve the success rate if the measured/observed project/portfolio performance is not
in line with the overall objectives.
How Can Project Managers Use Analytics in Project
Management?
Analytics finds its use in multiple areas throughout the project and project management
life cycles. The key applications of analytics in this context include, but are not limited
to, the following:
Assessing feasibility: Analytics can be used to assess the feasibility of various alternatives
so that a project manager can pick the best option.
Managing data overload: Due to the contemporary Internet age, data overload has
crippled project managers’ capability to capture meaningful information from mountains of data. Analytics can help project managers overcome this issue.
Enhancing data visibility and control via focused dashboards: An analytics dashboard
can provide a project manager a single view to look at the big picture and determine
both how each project and its project team members are doing. This information comes
4
Project Management Analytics
in handy for prioritizing project tasks and/or moving project team members around to
maximize productivity.
Analyzing project portfolios for project selection and prioritization: Project portfolio
analysis is a useful application of analytics. This involves evaluating a large number of
project proposals (or ideas) and selecting and prioritizing the most viable ones within
the constraints of organizational resources and other relevant factors.
Across all project organizations in general, but in a matrix organization in particular, multiple projects compete for finite resources. Organizations must select projects
carefully after complete assessment of each candidate project’s feasibility based on the
organization’s project selection criteria, which might include, but not be limited to, the
following factors:
■
Technical, economic, legal, political, capacity, and capability constraints
■
Cost-benefits analysis resulting in scoring based on various financial models
such as:
■
1
■
Net present value (NPV)1
■
Return on investment (ROI)2
■
Payback period3
■
Breakeven analysis4
Resource requirements
■
Internal resources (only functional department resources, cross-functional
resources, cross-organizational resources, or any combination of the preceding)
■
External resources
■
Both internal and external resources
■
Project complexity
■
Project risks
■
Training requirements
NPV is used to compare today’s investment with the present value of the future cash flows after those
cash flows are discounted by a certain rate of return.
2
ROI = Net Profit / Total Investment
Payback period is the time required to recoup the initial investment in terms of savings or profits.
4
Breakeven analysis determines the amount of revenue needed to offset the costs incurred to earn that
revenue.
3
Chapter 1 Project Management Analytics
5
Analytics can help organizations with selecting projects and prioritizing shortlisted projects for optimal allocation of any scarce and finite resources.
Improve project stakeholder management: Analytics can help improve project stakeholder management by enabling a project manager to predict stakeholder responses to
various project decisions. Project stakeholder management is both art and science—art
because it depends partly on the individual skillset, approach, and personality of the
individual project manager, and science because it is a highly data-driven process. Project managers can use analytics to predict the outcomes of the execution of their strategic
plans for stakeholder engagement management and to guide their decisions for appropriate corrective actions if they find any discrepancy (variance) between the planned and
the actual results of their efforts.
Project stakeholder management is much like customer relationship management
(CRM5) in marketing because customers are essentially among the top-level project
stakeholders and project success depends on their satisfaction and acceptance of the
project outcome (product or service). Demographic studies, customer segmentation,
conjoint analysis, and other techniques allow marketers to use large amounts of consumer purchase, survey, and panel data to understand and communicate marketing
strategy. In his paper “CRM and Stakeholder Management,” Dr. Ramakrishnan (2009)
discusses how CRM can help with effective stakeholder management. According to him,
there are seven Cs of stakeholder management:
1. Concern
2. Communicate
3. Contribute
4. Connect
5. Compound
6. Co-Create
7. Complete
Figure 1.1 illustrates the seven Cs of stakeholder management.
The seven Cs constitute seven elements of the project stakeholder management criteria,
which can be evaluated for their relative importance or strength with respect to the goal
5
6
CRM refers to a process or methodology used to understand the needs and behaviors of customers so
that relationships with them can be improved and strengthened.
Project Management Analytics
of achieving effective stakeholder management by utilizing the multi-criteria evaluation
capability of the Analytic Hierarchy Process (AHP).6
Understand and address
stakeholder concerns
Engage in communication with
stakeholders
Connect
Concern
Communicate
Create value for stakeholders to
meet their needs and
expectations
7 Cs of Project
Stakeholder
Management
Contribute
Interact with stakeholders
Compound
Use the blend of Concern,
Communicate, Contribute,
and Connect to create synergy
Co-Create
Engage stakeholders in
decision-making throughout
the project life cycle
Complete
Follow through with
stakeholders through the
complete project life cycle
Figure 1.1 Seven Cs of Project Stakeholder Management
Web analytics can also help managers analyze and interpret data related to the online
interactions with the project stakeholders. The source data for web analytics may include
personal identification information, search keywords, IP address, preferences, and various other stakeholder activities. The information from web analytics can help project
managers use the adaptive approach7 to understand the stakeholders better, which in
turn can further help them customize their communications according to the target
stakeholders.
Predict project schedule delays and cost overruns: Analytics can tell a project manager
whether the project is on schedule and whether it’s under or over budget. Also, analytics
can enable a project manager to predict the impact of various completion dates on the
bottom line (project cost). For example, Earned Value Analytics (covered in Chapter 8,
“Statistical Applications in Project Management”) helps project managers avoid surprises
by helping them proactively discover trends in project schedule and cost performance.
Manage project risks: Another area in a project’s life cycle where analytics can be
extremely helpful is the project risk management area. Project risk identification, ranking, and prioritization depend upon multiple factors, including at least the following:
■
Size and complexity of the project
■
Organization’s risk tolerance
■
Risk probability, impact, and horizon
■
Competency of the project or risk manager
6
Read Chapter 6, “Analytical Hierarchy Process,” to learn about AHP.
7
The process of gaining knowledge by adapting to the new learning for better decision-making.
Chapter 1 Project Management Analytics
7
Predictive analytics models can be used to analyze those multiple factors for making
rational decisions to manage the risks effectively.
Improve project processes: Project management involves the execution of a multitude
of project processes. Thus, continuous process improvement is essential for eliminating waste and improving the quality of the processes and the product of the project.
Improvement projects typically involve four steps:
1. Understand the current situation.
2. Determine the desired (target) future situation.
3. Perform gap analysis (find the delta between the target and the current situations).
4. Make improvement decisions to address the gap.
Analytics can help project managers through all four process improvement steps by
enabling the use of a “Project Management —Lean Six Sigma” blended or hybrid methodology for managing the projects with embedded continuous improvement.
Project Management Analytics Approach
The project management analytics approach can vary from organization to organization and even from project to project. It depends on multiple factors including, but not
limited to, organizational culture; policies and procedures; project environment; project
complexity; project size; available resources; available tools and technologies; and the
skills, knowledge, and experience of the project manager or project/business analysts.
This book covers the following approaches to project management analytics:
■
Statistical
■
Lean Six Sigma
■
Analytic Hierarchy Process
You will look at the application of each of these approaches and the possible combination
of two or more of these approaches, depending upon the project characteristics.
Statistical Approach
“Lies, damned lies, and statistics!
Nothing in progression can rest on its original plan.”
—Thomas S. Monson (American religious leader and author)
8
Project Management Analytics
Throughout the project life cycle, project managers must deal with a large number of
uncertainties. For instance, project risks are uncertainties that can derail the project
if they are not addressed in a timely and effective way. Similarly, all project baselines
(plans) are developed to deal with the uncertain future of the project. That’s why the
project plans are called living documents because they are subject to change based on
future changes. Because picturing the future precisely is hard, best estimates are used to
develop the project plans.
Statistical approach comes in handy when dealing with project uncertainties because it
includes tools and techniques that managers can deploy to interpret specific patterns
in the data pertaining to the project management processes to predict the future more
accurately.
Quantitative measure of a process, when that process is performed over and over, is
likely to follow a certain frequency pattern of occurrence. In other words, there is a
likelihood or probability of recurrence of the same quantitative measure in the long
run. This likelihood or probability represents the uncertainty of recurrence of a certain
quantitative value of the process. Statistical analysis can help predict certain behaviors
of the processes or systems in the environment of uncertainty, which is fundamental to
data-driven decision-making.
We use the following analytical probability distributions to illustrate how a statistical
approach can help in effective decision-making in project management:
■
Normal distribution
■
Poisson distribution
■
Uniform distribution
■
Triangular distribution
■
Beta distribution
Normal Distribution
Depicted in Figure 1.2, the normal distribution is the most common form of the probability density function. Due to its shape, it is also referred to as the bell curve. In this
distribution, all data values are symmetrically distributed around the mean of the probability. The normal distribution method constitutes a significant portion of the statistical content that this book covers because the project management processes involve a
number of normal events.8
8
For example, project selection criteria scores, stakeholders’ opinions, labor wages, project activity
duration, project risk probability, and so on.
Chapter 1 Project Management Analytics
9
 – 3
 – 2
 – 1

 + 1
 + 2
 + 3
Figure 1.2 Normal Distribution
Normal distribution is the result of the process of accumulation. Usually, the sum or
average of the outcomes of various uncertainties constitutes an outcome whose probability distribution is a normal distribution.
For data with a normal distribution, the standard deviation has the following
characteristics:9
■
68.27% of the data values lie within one standard deviation of the mean.
■
95.45% of the data values lie within two standard deviations of the mean.
■
99.73% of the data values lie within three standard deviations of the mean.
9
This is also known as the empirical rule.
10
Project Management Analytics
Poisson Distribution
Poisson distribution is the result of the process of counting. Figure 1.3 depicts the shape
of a typical Poisson distribution curve.
0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
0
5
10
15
20
25
30
Figure 1.3 Poisson Distribution
This distribution can be used to count the number of successes or opportunities as a
result of multiple tries within a certain time period. For example, it can be used to count
■
The number of projects human resources acquired in a period of two months
■
The number of project milestones completed in a month
■
The number of project tasks completed in a given week
■
The number of project change requests processed in a given month
Chapter 4, “Statistical Fundamentals I,” covers the Poisson distribution in more depth
and examines how this distribution can be used in project management to count discrete,10 countable, independent events.
10
Discrete random variables are small in number and can be counted easily. For example, if a random
variable represents the output of tossing a coin, then it is a discrete random variable because there are
just two possible outcomes—heads or tails.
Chapter 1 Project Management Analytics
11
Uniform Distribution
Illustrated in Figure 1.4, a uniform distribution is also referred to as a rectangular distribution with constant probability.
f(x)
1
b–a
a
b
x
Figure 1.4 Uniform Distribution
The area of the rectangle is equal to the product of its length and its width.
Thus, the area of the rectangle equals (b – a) * 1/ (b – a) = 1.
What does this mean? This means that for a continuous11 random variable, the area
under the curve is equal to 1. This is true in the case of a discrete random variable as well
provided the values of the discrete random variable are close enough to appear almost
continuous.
The unit area under the curve in Figure 1.4 illustrates that relative frequencies or probabilities of occurrence of all values of the random variable, when integrated, are equal
to 1. That is:
11
12
When there are too many possible values for a random variable to count, such a random variable is
called a continuous random variable. The spacing between the adjacent values of the random variable is so small that it is hard to distinguish one value from the other and the pattern of those values
appears to be continuous.
Project Management Analytics
∫
b− a
all f ( X ) dX = 1
In this equation, dX is an increment along the x-axis and f(X) is a value on the y-axis.
Uniform distribution arbitrarily determines a two-point estimate of the highest and lowest values (endpoints of a range) of a random variable. This simplest estimation method
allows project managers to transform subjective data into probability distributions for
better decision-making especially in risk management.
Triangular Distribution
Unlike uniform distribution, the triangular distribution illustrates that the probability
of all values of a random variable are not uniform. Figure 1.5 shows the shape of a triangular distribution.
f(x)
2
b–a
a
c
b
x
Figure 1.5 Triangular Distribution
A triangular distribution is called so because of its triangular shape. It is based on three
underlying values: a (minimum value), b (maximum value), and c (peak value) and can
be used estimate the minimum, maximum, and most likely values of the outcome. It is
also called three-point estimation, which is ideal to estimate the cost and duration associated with the project activities more accurately by considering the optimistic, pessimistic,
and realistic values of the random variable (cost or duration). The skewed nature of this
Chapter 1 Project Management Analytics
13
distribution represents the imbalance in the optimistic and pessimistic values in an event.
Like all probability density functions, triangular distribution also has the property that
the area under the curve is 1.
Beta Distribution
The beta distribution depends on two parameters—α and β where α determines the center or steepness of the hump of the curve and β determines the shape and fatness of the
tail of the curve. Figure 1.6 shows the shape of a beta distribution.
 determines center or steepness of the hump
 determines the shape and fatness of the tail
0
1
Time t
Figure 1.6 Beta Distribution
Like triangular distribution, beta distribution is also useful in project management to
model the events that occur within an interval bounded by maximum and minimum
end values. You will learn how to use this distribution in PERT (Program Evaluation
and Review Technique) and CPM (Critical Path Method) for three-point estimation in
Chapter 8.
14
Project Management Analytics
Lean Six Sigma Approach
The Lean12 Six Sigma13 approach encompasses reduction in waste and reduction in variation (inaccuracy). For decisions to be rational and effective, they should be based on an
approach that promotes these things. That is the rationale behind the use of the Lean Six
Sigma approach in project management analytics.
NOTE
“Lean-Six Sigma is a fact-based, data-driven philosophy of improvement that values
defect prevention over defect detection. It drives customer satisfaction and bottomline results by reducing variation, waste, and cycle time, while promoting the use of
work standardization and flow, thereby creating a competitive advantage. It applies
anywhere variation and waste exist, and every employee should be involved.”
Source: American Society of Quality (ASQ). http://asq.org/learn-about-quality/
six-sigma/lean.html
The goal of every project organization in terms of project outcome is SUCCESS, which
stands for
SMART14 Goals Established and Achieved
Under Budget Delivered Outcome
Communications Effectiveness Realized
Core Values Practiced
Excellence in Project Management Achieved
Schedule Optimized to Shorten Time to Delivery
Scope Delivered as Committed
The projects are typically undertaken to improve the status quo of a certain prevailing
condition, which might include an altogether missing functionality or broken functionality. This improvement effort involves defining the current (existing) and the target
conditions, performing gap analysis (delta between the target and the current condition),
12
The Lean concept, originated in Toyota Production System, Japan, focuses on reduction in waste.
13
The Six Sigma concept, originated in Motorola, USA, focuses on reduction in variation.
14
Specific, Measurable, Achievable, Realistic, and Timely
Chapter 1 Project Management Analytics
15
and understanding what needs to be done to improve the status quo. The change from the
current condition to the target condition needs to be managed through effective change
management. Change management is an integral part of project management and the
Lean Six Sigma approach is an excellent vehicle to implement changes successfully.
The DMAIC Cycle
Like the project management life cycle, Lean Six Sigma also has its own life cycle called
the DMAIC cycle. DMAIC stands for the following stages of the Lean Six Sigma life cycle:
Define
Measure
Analyze
Improve
Control
The DMAIC is a data-driven process improvement, optimization, and stabilization cycle.
All stages of the DMAIC cycle are mandatory and must be performed in the order from
“define” to “control.” Figure 1.7 depicts a typical DMAIC cycle.
Define
Measure
Measure Performance of
the Modified Process
Analyze
Modify
Process?
Improve
Control
No
N
Yes
Modify
Figure 1.7 DMAIC Cycle
The various stages of the DMAIC cycle are briefly described here (refer to Chapter 7,
“Lean Six Sigma,” for detailed discussion on the DMAIC cycle):
16
■
Define: Define the problem and customer requirements.
■
Measure: Measure the current performance of the process (establish baseline),
determine the future desired performance of the process (determine target), and
perform gap analysis (target minus baseline).
■
Analyze: Analyze observed and/or measured data and find root cause(s). Modify
the process if necessary but re-baseline the performance post-modification.
Project Management Analytics
■
Improve: Address the root cause(s) to improve the process.
■
Control: Control the future performance variations.
The PDSA Cycle
Project quality is an integral part of project management. The knowledge of Lean Six
Sigma tools and processes arms a project manager with the complementary and essential skills for effective project management. The core of Lean Six Sigma methodology is
the iterative PDSA (Plan, Do, Study, Act) cycle, which is a very structured approach to
eliminating or minimizing defects and waste from any process.
Figure 1.8 shows the PDSA cycle. We discuss this cycle as part of our discussion on the
applications of the Lean Six Sigma approach in project management.
PLAN
DO
ACT
STUDY
Figure 1.8 PDSA Cycle
Brief explanations of the building blocks of the PDSA cycle follow (refer to Chapter 7 for
detailed discussion on the PDSA cycle):
■
Plan: The development of the plan to carry out the cycle
■
Do: The execution of the plan and documentation of the observations
■
Study: The analysis of the observed and collected data during the execution of
the PDSA plan
■
Act: The next steps based on the analysis results obtained during study
Lean Six Sigma Tools
The Lean Six Sigma processes involve a lot of data collection and analysis. The various
tools used for this purpose include the following:
Chapter 1 Project Management Analytics
17
■
Brainstorming: To collect mass ideas on potential root causes
■
Surveys: To collect views of the individuals who are large in number and/or outside personal reach
■
Five whys: A method that asks five probing questions to identify the root cause
■
Value stream mapping: Process map analysis to identify wasteful process steps
■
Cause and effect or fishbone or Ishikawa diagram: A tool to help with brainstorming on the possible root causes
■
Control charts: To identify “common” and “special” causes in the stream of data
observed over a period of time
■
Correlation: To study the correlation between two variables
■
Cost-benefits analysis: To estimate the cost of implementing an improvement
plan and the benefits realized
■
Design of experiments: To identify the recipe for the best possible solution
■
Histograms: Unordered frequency (of defects) map
■
Pareto charts: Ordered (descending) frequency (of defects) map
■
Regression analysis: To study the effect of one variable with all other variables
held constant
■
Root cause analysis: Analysis to find the “cure” for a problem rather than just
“symptoms treatment”
■
Run charts: Observed data over a period of time
■
SIPOC15 chart: Process analysis to identify input and output interfaces to the
process
These tools are discussed in more detail in Chapter 7.
The Goal of Lean Six Sigma–Driven Project Management
Executing only those activities that are value adding, when they are needed, utilizing
minimum possible resources, without adversely impacting the quality, scope, cost,
and delivery time of the project.
15
18
SIPOC (Supplier, Input, Process, Output, Customer) is a process analysis tool.
Project Management Analytics
How Can You Use the Lean Six Sigma Approach in Project
Management?
We will examine a hybrid approach by blending the DMAIC cycle with the project management life cycle, which project managers can use to find the root cause(s) of the following project path holes and recommend the appropriate corrective actions to fix them.
■
Schedule delays
■
Project scope creep
■
Cost overruns
■
Poor quality deliverables
■
Process variation
■
Stakeholder dissatisfaction
Analytic Hierarchy Process (AHP) Approach
Proposed by Thomas L. Saaty in 1980, the AHP is a popular and effective approach to
multi-criteria-driven decision-making. According to Saaty, both tangible and intangible
factors should be considered while making decisions. “Decisions involve many intangibles that need to be traded off. To do that, they have to be measured alongside tangibles
whose measurements must also be evaluated as to how well they serve the objectives of
the decision maker,” says Saaty.
You can use the AHP approach in any scenario that includes multiple factors in decisionmaking. For example:
■
Deciding which major to select after high school
■
Deciding which university to select after high school
■
Deciding which car to select for buying
■
Deciding which projects to select for inclusion in the portfolio
Often in decision-making, the intangible factors are either overlooked or the decisions
are just made based on subjective or intuitional criteria alone. The AHP approach is a
360o approach, which includes both subjective and objective criteria in decision-making.
The key characteristic of this approach is that it uses pairwise comparisons16 of all the
possible factors of the complex problem at hand and evaluates their relative importance
to the decision-making process. For example, project management decision-making
16
Pairwise comparisons include comparison of each factor in the decision-making criteria against every
other factor in the criteria.
Chapter 1 Project Management Analytics
19
criteria may include three factors: schedule flexibility, budget flexibility, and scope flexibility. To make a decision, the project manager must consider the relative importance
of each of the three factors against every other factor in the criteria. Schedule, budget,
and scope are the triple constraints of project management and a tradeoff often has to
be made to find the right balance among them based on the business need and/or the
project environment. For instance, less flexibility in scope requires schedule, budget, or
both to be relatively more flexible.
Chapter 6 covers the AHP approach in more detail. This book makes extensive use of this
approach in recommending data-driven methodology for making the most effective and
rational project management decisions, including the following:
■
Project selection and prioritization
■
Project risk identification and assessment
■
Selection of project risk response strategy
■
Vendor selection
■
Project resource allocation optimization
■
Project procurement management
■
Project quality evaluation
Summary
The mind map in Figure 1.9 summarizes the project management analytics approach.
Why is Analytics
important in Project
Management?
What is
Analytics?
Analytics ( aka Data
Analytics) involves
the systematic
quantitative analysis
of data or statistics to
obtain meaningful
information for better
decision-making
Analytics can help project
managers use the predictive
information to make better
decisions to keep the projects
on-schedule and on-budget
Analytics can be used in Project
Management to
Project Management
Analytics Overview
Which Analytics Approaches
can be used?
How can Analytics
be used in Project
Management?
• Statistical Approach
• Lean Six Sigma Approach
• Analytical Hierarchy Process
Approach
Figure 1.9 Project Management Analytics Approach Summary
20
Project Management Analytics
• Assess Feasibility
• Manage Data Overload
• Enhance Data Visibility and Control
via Focused Dashboards
• Analyze Project Portfolio for Project
Selection and Prioritization
• Improve Project Stakeholder
Management
‡¬3redict Project Schedule Delays and
Cost Overruns
• Manage Project Risks
Improve Project Processes
Key Terms
Analytic Hierarchy Process (AHP)
Net Present Value (NPV)
Analytics
Normal Distribution
Beta Distribution
NORMDIST
Breakeven Analysis
Payback Period
Continuous Random Variable
PDSA Cycle
Cost-Benefit Analysis
Poisson Distribution
Critical Path Method (CPM)
Program Evaluation and Review
Technique (PERT)
Customer Relationship Management
(CRM)
Return on Investment (ROI)
Discrete Random Variable
SIPOC
DMAIC Cycle
Three-Point Estimating
Earned Value Analysis
Triangular Distribution
Empirical Rule
Uniform Distribution
Lean Six Sigma
Value Stream Mapping
Case Study: City of Medville Uses Statistical Approach to
Estimate Costs for Its Pilot Project
To encourage sports and fitness among students from kindergarten to 12th grade, the
education department of the city of Medville, Pennsylvania, conceived a 12-month pilot
project to provide special free training, nutrition, and sports gear to the students of a
select 10 schools. The goal of this project was to cover 70% of the student population
under the new program. The initial challenge was to figure out the funds required to run
this project and also the plan to carry out the project work.
For scope management, the project management committee divided the student population in different age groups and estimated the cost for students in each age group. Table
1.2 depicts the various student age groups and the cost estimates.
Chapter 1 Project Management Analytics
21
Table 1.2
Estimated Project Cost for Various Student Age Groups
Student Age Group
Estimated Cost Per Student
Less than 10 years old
$2,000
10 to 15 years old
$5,000
More than 15 years old
$3,000
The project assumed that the total population of students (2,000 students) was normally
distributed with a mean age of 12 and a standard deviation of 3. The following statistical
calculations for normal distribution were used to make decisions.
Determine Target Age Group for Initial Project Pilot
For normal distribution,
■
1 σ covers roughly 68% of the population, which implies 68% of the total 2,000
students fall in the age group 9 to 15 (12 +/– 3).
■
2 σ covers roughly 95% of the population, which implies 95% of the total 2,000
students fall in the age group 6 to 18 (12 +/– 6).
Because the goal of the pilot project was to cover 70% of the student population, students
in age group 6 to 18 were selected for the initial pilot.
Estimate Project Costs for the Target Age Group
The target age group contained student population from all three population bands listed
in Table 1.2. Thus, cost estimates pertaining to those population bands or age groups had
to be considered for calculating costs for the target age group (6 to 18 years old). The
project figured it out using the Excel NORMDIST17 function as follows:
Percentage of target students belonging to age group under 10 years (6 to 10 years
old) = NORMDIST (10, 12, 3, 1) – NORMDIST (6, 12, 3, 1) = 22.97%
Cost Allocation for 6- to 10-year old students = (2000 * 22.97% * 2000) = $918,970
Percentage of target students belonging to age group 10 to 15 years (10 to 15 years
old) = NORMDIST (15, 12, 3, 1) – NORMDIST (10, 12, 3, 1) = 58.89%
17
22
NORMDIST(x, μ, σ, 1), where x = random variable (upper or lower end of the age-group range),
μ = mean age in the age-group, σ = standard deviation, and 1 stands for cumulative.
Project Management Analytics
Cost Allocation for 10- to 15-years-old students = (5000 * 58.89% * 2000)
= $5,888,522
Percentage of target students belonging to age group over 15 years = 1 –
(22.97% + 58.89%) = 18.14%
Cost Allocation for over 15-year-old students = (3000 * 18.14% * 2000)
= $1,088,432
Total Estimated Cost for All Target Students for the Initial Pilot = $918,970 +
$5,888,522 + $1,088,432 = $7,895,924
Case Study Questions
1. What approach was used by the city of Medville to estimate the overall project
cost?
2. Define the scope of this project.
3. Do you think the city made a wise decision to use this approach for cost estimation? Why do you think so?
Chapter Review and Discussion Questions
1. Define analytics.
2. What is the difference between analytics and analysis?
3. What are advantages of using analytics in project management?
4. How can analytics be used in project selection and prioritization?
5. Describe briefly the 7 Cs of project stakeholder management.
6. What are the characteristics of normal distribution in terms of standard deviation?
7. When can Poisson distribution be used for project management? Provide some
examples.
8. Which statistical distribution is used for three-point estimation in project
management?
9. Describe briefly the various stages of the DMAIC cycle.
10. What does PDSA stand for?
11. What is the primary purpose of using the Lean Six Sigma approach in project
management?
12. List some of the applications of the AHP approach.
Chapter 1 Project Management Analytics
23
13. What is the empirical rule in normal distribution?
14. The mean duration of the activities of a project is 10 days with a standard deviation of 2 days. Using the empirical rule estimate the percentage of project activities with duration between 7 and 10 days.
15. Solve the preceding problem using Excel’s NORMDIST function.
Bibliography
Anbari, F.T. (1997). Quantitative Methods for Project Management. 59th Street, New York: International Institute for Learning, Inc.
Borror, C. (2009). “The Define Measure Analyze Improve Control (DMAIC) Process.” Retrieved
February 14, 2015, from http://asq.org/learn-about-quality/six-sigma/overview/dmaic.html
Deltek. (2013, September 11). “Deltek wInsight Analytics: Avoid Surprises and Quickly Discover
Trends and Issues in Your Earned Value Data.” Retrieved February 14, 2015, from http://www.
deltek.com/~/media/pdf/productsheets/govcon/winsight-ipm-ps.ashx
Ghera, B. (2011). “Project and Program Management Analytics.” Retrieved February 10, 2015,
from http://www.pmi.org/~/media/PDF/Knowledge-Shelf/Gera_2011(2).ashx
Goodpasture, John C. (2003). Quantitative Methods in Project Management. Boca Raton, Florida,
USA: J. Ross Publishing.
Larson, R. and Farber, E. (2011). Elementary Statistics: Picturing the World, 5th ed. Upper Saddle
River, New Jersey: Pearson.
Mavenlink. (2013). “Using Analytics for Project Management.” Retrieved February 11, 2015, from
http://blog.mavenlink.com/using-analytics-for-project-management
MDH QI Toolbox. (2014). “PDSA: Plan-Do-Study-Act.” Minnesota Department of Health.
Retrieved February 15, 2015, from http://www.health.state.mn.us/divs/opi/qi/toolbox/pdsa.html
Pollard, W. (n.d.). BrainyQuote.com. Retrieved October 5, 2015, from BrainyQuote.com Web site:
http://www.brainyquote.com/quotes/authors/w/william_pollard.html.
Project Management Institute (2014). A Guide to the Project Management Body of Knowledge
(PMBOK® Guide), 5th ed. Newton Square, Pennsylvania: Project Management Institute (PMI).
Quora. (2014). What is the difference between “Business Analytics” and “Business Analysis”?
Retrieved September 4, 2015, from http://www.quora.com/What-is-the-differencebetween-Business-Analytics-and-Business-Analysis
Ramakrishnan, Dr. (2009). “CRM and Stakeholder Management.” 20th SKOCH Summit, Hyatt
Regency, Mumbai, July 16-17 2009.
Saaty, T.L. (2008). “Decision Making with Analytic Hierarchy Process.” International Journal of
Services Sciences, 1 (1), pp. 83–98.
24
Project Management Analytics
2
Data-Driven Decision-Making
Learning Objectives
After reading this chapter, you should be familiar with
■
Common project management decisions
■
Characteristics of a good decision
■
Factors influencing decision-making
■
Analysis paralysis
■
Importance of a decisive project manager
■
Automation of decision-making process
■
Predictive versus prescriptive analytics
■
Data-driven decision-making process flow
■
Benefits of data-driven decision-making
■
Challenges associated with data-driven decision-making
“There is nothing like first-hand evidence.”
—Sherlock Holmes
Strong project management and leadership skills are not the only prerequisites for the
ability of a project manager to deliver a successful project. His or her ability to make
complex project decisions in a timely manner is also one of the “must have” skills because
there is a strong positive correlation between the quality of project decisions and the
project success. Being able to select the best course of action based on careful evaluation
of various alternatives by analyzing the underlying tangible and intangible criteria is the
only way a project manager can lead the project to achieve the stipulated objectives.
25
Decisions are ubiquitous throughout the project life cycle (PLC). For instance, decisions
must be made
■
To undertake the project
■
To move forward from one stage of the PLC to the next
■
To hire or not hire a project human resource
■
To buy or build
■
To select the best supplier from multiple alternatives
■
To approve or reject a project risk
■
To approve or reject a change request
■
To accept or reject a deliverable
Characteristics of a Good Decision
An action must follow a decision made. If the action is missing, the decision made is
useless and the effort leading to that decision is wasted.
The following are the characteristics of a good decision:
26
■
Considers all factors influencing the situation
■
Based on the “win-win” approach?
■
Incorporates appropriate tools and techniques
■
Involves the right participants from beginning to end
■
Considers viewpoints of all parties involved
■
Transparent to all parties involved; no hidden agenda exists
■
Utilizes a 360-degree analytical approach to include tangibles (measurable data)
as well as intangibles (such as intuition and subjectivity)
■
Based on high-quality predictive analysis (intelligent anticipation) because the
results of decisions made today will be noticeable in the future and the future
involves uncertainty. For example, Hewlett-Packard’s decision to undertake a
project to launch its tablet product “touchpad” resulted in a product that died
right after its birth because by the time the project was completed and the touchpad was launched, the marketplace was already flooded with lower cost and higher
quality tablets.
Project Management Analytics
Decision-Making Factors
Decision-making depends on multiple factors including knowledge, skills, tangibles,
intangibles, pragmatism, and decision-making methodology.
Knowledge: Knowledge pertains to the information needed to make a decision. For decisions to be feasible and effective, all parties involved in the decision-making process must
have knowledge of the information about the situation and the context of the situation.
Skills: Decision-makers must have skills to use their knowledge and experience to
acquire and intelligently analyze the information pertaining to the situation about which
the decision is being made.
Tangibles: Tangibles include directly measured or observed qualitative and quantitative
data such as hard facts or evidences pertaining to the situation.
Intangibles: Intangibles in decision-making refers to decision-makers’ intuitions and
subjective approach.
Measuring Project Manager Soft Competencies: Quantifying
the Subjective Information for Measurement
For fully informed decision-making both subjective and objective information should
be considered. The subjective information is often collected via surveys but until some
criteria are developed, many decision-makers do not know how to take the subjective
information into account.
Gregory J. Skulmoski et al. shared in their article published in the March 2010 issue of
Project Management General how the subjective answers to a survey questions about an
information systems project manager’s soft skills were quantified. They wrote, “During the pilot testing of the interview questions, the research participants had some
difficulty discussing competence broadly and deeply…the interviewees were provided
with a list of competencies by project phase (initiation, planning, implementation, and
closeout) to rank. They were given 25 points to use to rank and weight the competencies within the list. They could distribute their 25 points within each category in any
way they felt appropriate.”
Pragmatism: A pragmatic approach allows the decision-makers to accept less-thanperfect results. The quest to achieve a perfect outcome often paralyzes decisionmaking efficiency. Pragmatism is the factor in the decision-making process that takes
into account the practical realities (such as politics, regulations, financial constraints,
cost-benefit tradeoff, urgency, and so on) of the project environment and helps prevent
analysis paralysis.
Chapter 2 Data-Driven Decision-Making
27
Analysis Paralysis (Over-Analysis of the Information)
Data analysis for project decision-making is important but it should not become “analysis paralysis” where project managers just keep spinning the wheels in analysis and
can’t make a clear decision. When they do finally make a decision due to being forced
up against the wall by certain critical project deadlines, they end up making poor
decisions.
The level of analysis should match the complexity of the situation. For example, a
project manager does not need to collect and analyze a massive amount of data just to
make decision whether to buy a projector for presentations or not; however, he or she
must perform a thorough analysis before deciding which vendor to award the project
solution integration contract to.
Decision-Making Methodology: Effective decision-making involves the use of proper
tools, technologies, and methodologies, which include brainstorming, facilitation, meetings, negotiation, research, cost-benefit analysis, alternative analysis, communication
techniques, and so on.
Brainstorming allows for a fear-free environment for free flow of ideas from all participants. Tight facilitation keeps the meeting discussions focused on the subject matter of
interest for quicker and effective decision-making. Well-researched alternatives processed through cost-benefits–based alternative analysis enable the decision-maker to
select the best possible alternative. Effective communication techniques help with stakeholder engagement and exchange of information among the participants in the decisionmaking process.
Importance of Decisive Project Managers
An integral part of a project manager’s day-to-day project management job is to make
a variety of often time-sensitive important project decisions. Thus, a project manager’s
decisiveness attribute alone has the potential to steer the project ship toward the destination or toward destruction.
The mind map in Figure 2.1 captures the key reasons why project managers’ decisiveness
is important in project management.
28
Project Management Analytics
Figure 2.1 Importance of Decisive Project Managers
Time Is of the Essence
Right and rational decisions made by a project manager in a timely1 manner are critical
for the progress of a project. “Rational” means the decisions being made are logical and
properly thought through. A project manager’s strong project management knowledge
and soft skills enable him or her to make rational decisions in a timely manner. Also, it
is important that the key stakeholders are involved in the decision-making process and
that their consent is given due consideration. However, when the team takes a significant amount of time to arrive at a consensus, the project manager should take control in
making a decision so that the project can move forward because time is of the essence.
Lead by Example
Leading by example is an important and effective skill of a project manager to motivate
the project team. By being able to make effective project decisions in a timely manner,
the project manager sets an example for the rest of the team that right decisions need to
be made in right time frame by involving the right people.
Establish Credibility
The ability of a project manager to be decisive and make things happen on the project
helps her establish her credibility among the project team members as a strong leader.
Indecisive project managers can lose credibility as strong project leaders and the project
team members may soon lose confidence in their ability to steer the project ship in the
right direction.
1
“Timely” does not mean that the decisions are made in haste with their quality compromised.
Chapter 2 Data-Driven Decision-Making
29
Resolve Conflicts and Other Project Problems
Projects typically have to deal with lots of uncertainty and involve multiple diverse stakeholders. They involve a variety of conflicts and project problems. The project manager’s
responsibility ultimately is to handle and resolve those conflicts and problems effectively
to keep the project on track to success. This often requires a project manager to be able
to make quick and rational decisions to find a win-win resolution. Unexpected situations cannot be proactively planned for. Therefore, resolving conflicts and problems
pertaining to these unexpected situations requires that a project manager be able to think
quickly and clearly under pressure to make the best possible decisions after weighing the
pros and cons of various alternatives.
Avoid Analysis Paralysis
We discussed the concept of analysis paralysis earlier in this chapter under “Pragmatism.” Many project managers often hesitate to make decisions and they fall into this
trap. They keep over-analyzing the same set of information without arriving at a decisive
conclusion. An alternative-analysis-based decision-making approach can enable the
project manager to make quicker and correct decisions and avoid analysis paralysis by
evaluating:
■
The pros and cons of pursuing each alternative
■
The opportunity cost of not pursuing an alternative
■
SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of each
alternative
Automation and Management of the Decision-Making
Process
The project decision-making process, when automated and effectively managed, can
produce effective and efficient decisions that are critical to the success of a project.
Project decision-making is an ongoing process. Decisions made throughout the stages of
the PLC not only impact the domain within which the decisions are made but they also
impact other decisions in various other domains of the project. This complexity of the
wide array of project decisions requires some level of automation of the project decisionmaking process. Some methodologies or approaches that can be used to automate and
manage the decision-making process, include the following:
30
Project Management Analytics
■
Predictive analytics: This involves data mining to analyze historical data to identify certain patterns or trends in data that can help make data-driven predictive
decisions to mitigate the risks due to uncertainty of the future.
■
Optimization techniques (prescriptive analytics): These help with optimizing
the allocation and use of scarce project resources within project constraints.
■
Statistical analytics: This presents statistical techniques to analyze probability for
decision-making.
■
Big data: Big data refers to data-sets that are too large and/or complex to use
traditional methods for searching, capturing, analyzing, archiving, securing,
and distributing data. Big data makes use of various advanced computational
techniques such as predictive analytics to assist in automating the data-driven
decision-making process.
■
Analytic Hierarchy Process: This is an effective approach to multi-criteria-driven
decision-making.
The automation of the decision-making process is hard to achieve if project managers
keep delaying decision-making in continued quest for perfection. Experts recommend
the application of the 80–20 rule in decision-making. According to Butler Analytics,
“Eighty percent of the benefit will come from twenty percent of the rules.”
An efficient, reliable, consistent, and fact-based decision-making process is very important in any organization. It is specifically more critical in environments such as banking,
insurance, and other financial services where the volume of decisions to be made is very
high and/or the decision-making process is repetitive.
Data-Driven Decision-Making
Data-driven decision-making is defined as the process of making decisions not just on
the basis of gut feeling or intuition but also by taking the actual facts or data into consideration. The mind map in Figure 2.2 outlines seven steps to data-driven decision-making.
Data-Driven Decision-Making—Pathway to Gaining the
Competitive Advantage
In his article in Harvard Business Review (HBR), Walter Frick (2014) refers to the 2012
report by Andrew McAfee and Erik Brynjolfsson in HBR that highlights the benefits
of data-driven decision-making, “Companies in the top third of their industry in the
Chapter 2 Data-Driven Decision-Making
31
use of data-driven decision making were, on average, 5% more productive and 6%
more profitable than their competitors.” To reinforce his stance, Frick further quotes
comments from McAfee’s other post on HBR, “Data and algorithms have a tendency
to outperform human intuition in a wide variety of circumstances.” Also, the datadriven approach minimizes the risks generally associated with the process of making
decisions.
Step 1: Define/Identify
the Problem or
Situation
Step 5: Propose
Potential Possible
Solutions
(Alternatives)
Clearly understand the problem Wrong diagnosis means wrong
treatment.
Interpret data analysis
results from Step 4 and
propose potential possible
solutions (alternatives);
Define proper metrics.
Step 2: Review
Historical Records /
Lessons Learned
Check if the current problem
occurred in the past. If yes, how was
it resolved? If solution is already
available, there is no need to
re-invent the wheel. Even if the
problem at hand did not occur in
the past, find if any similar
problems occurred in the past and
how they were solved if they did
occur. Leverage as much as
possible to speed up the decision
making process.
7 Steps to
Data-Driven
Decision Making
Step 6: Model
analytics approach
and analyze the
proposed alternatives
Use appropriate analytics
such as predictive analytics,
prescriptive analytics, or
analytic hierarchy process to
analyze proposed
alternatives and to select the
best alternative.
Step 3: Collect Data
Step 7: Make Decision
Collect data pertaining to the
defined problem or situation.
Step 4: Analyze Data
Analyze the collected data to find
specific patterns, trends, outliers,
and special causes.
Figure 2.2 Seven Steps to Data-Driven Decision-Making
32
Project Management Analytics
Make smart informed
decision by selecting the
best alternative from all
potential solution
candidates (alternatives)
based on the alternative
analysis results from Step 6.
Data-Driven Decision-Making Process Challenges
Although data-driven decision-making provides numerous benefits, it is not without
challenges. Project managers must consider these challenges while planning for the datadriven decision-making processes to achieve the desired results. The following are common challenges associated with this process:
■
Magnitude and complexity of the data: The higher the magnitude and complexity of the data, the more difficult and time consuming is the security, storage, and
processing of the data.
■
Sources of the data: Sources of data determine the type of data collected. Incorrect sources means incorrect data and hence incorrect decisions, as discussed in
the next section, “Garbage In, Garbage Out.”
■
Quality of the data: Uniform data (attributes) pertaining to various alternatives
must be compared to mimic apple-to-apple comparison for fair alternative analysis. Also, the quality of the data collected must be adequate to bring forth the true
value of a given alternative. The collected data is not always all good and sorting
out good data from the bad is a must but is not an easy task, as says Alexey Shelushkov in one of the 2014 blog posts on itransition.com, “Not all data that glitters
is gold. Data has to be exact, correct and uniform in order to be the yardstick to
measure the business potency of this or that decision.”
■
Personnel analytical skills: Inadequate analytical skills of data analyst personnel
will certainly pose a challenge in ensuring the accuracy, quality, and efficiency of
the analysis.
■
Tools and technologies: The speed, accuracy, and quality of data collection, storage, analysis, and interpretation processes depend on the available tools and technologies, particularly when the data in question is large and complex. Inadequacy
or lack of appropriate tools and technology certainly pose a challenge to datadriven decision-making.
■
Shelf-life of the data: Data collected that is not processed in a timely manner may
become stale and no longer useful. For example, data pertaining to the technology
in use today may not be worth analyzing two years from now when this technology becomes obsolete and is replaced by another technology.
Chapter 2 Data-Driven Decision-Making
33
Garbage In, Garbage Out
The quality of data-driven decisions is determined by the type2 and quality of the data
collected and by the manner in which the collected data is analyzed, interpreted, and
used for decision-making.
Data for decision-making is collected through various means such as measurements,
observations, conversations, and surveys. The quality of the data collected through conversations and surveys depends on the types of data-related questions asked from the
responders. In his article “Keep Up with Your Quants,” published in the July 2013 issue
of HBR, Thomas H. Davenport identifies the following six questions that should be asked
to collect the good type and quality of data:
■
What was the source of your data?
■
How well do the sample data represent the population?
■
Does your data distribution include outliers? How did they affect the results?
■
What assumptions are behind your analysis? Might certain conditions render
your assumptions and your model invalid?
■
Why did you decide on that particular analytical approach? What alternatives did
you consider?
■
How likely is it that the independent variables are actually causing the changes in
the dependent variable? Might other analyses establish causality more clearly?
Summary
The mind map in Figure 2.3 summarizes tthe data-driven decision-making process.
2
34
The type of data collected refers to the metrics used to collect data. Due diligence must be used to
select the good (right) metrics. According to Frick (2014), “Good metrics are consistent, cheap, and
quick to collect. But most importantly, they must capture something your business cares about.”
Project Management Analytics
What is
Data-Driven
Decision-Making?
Why is it important?
Data-driven decision-making is
defined as the process of making
decisions not just on the basis of
gut feeling or intuition but also by
taking the actual facts or data into
consideration.
Data-Driven
Decision-Making
Process Overview
How can the accuracy,
effectiveness, and efficiency of
data-driven decisions be
enhanced?
Data-driven decision-making helps
improve productivity and
profitability and minimize risks by
improving the accuracy and
efficiency of project management
decisions.
What factors
influence Data-Driven
Decision-Making?
Factors including knowledge,
skills, tangibles, intangibles,
pragmatism, and decision-making
methodology influence decisionmaking.
Project decision-making process
should be automated and
effectively managed for effective
and efficient decision-making
utilizing predictive analytics,
optimization techniques
(prescriptive analytics),
statistical analytics,
big data, and
Analytic Hierarchy Process.
Figure 2.3 Data-Driven Decision-Making Process Summary
Key Terms
Analysis Paralysis
Pragmatism
Analytic Hierarchy Process (AHP)
Predictive Analytics
Big Data
Prescriptive Analytics
Cost-Benefits Tradeoff
Project Life Cycle (PLC)
Earned Value Management (EMV)
SWOT Analysis
Intangibles
Tangibles
Chapter 2 Data-Driven Decision-Making
35
Case Study: Kheri Construction, LLC
In this case study, Kheri Construction, LLC uses the data-driven decision-making process to resolve the issue of high staff turnover.
Background
Kheri Construction (KC), LLC is a Dallas, Texas–based premier commercial construction company. The company has a reputation for successfully completing on-time and
under-budget mega-million dollar projects in the state of Texas. The large portfolio of
the projects completed by the company includes multi-story skyscrapers, multi-lane
highways, railroad tracks, and shopping malls.
In the spring of 2011, KC was awarded a contract by the Texas state government to
implement a large and complex highway reconstruction project in Houston. The company hired a limited-term (LT) project manager, Emma Veronica, and the project was
initiated.
Problem
The project performance was measured primarily via the popular Earned Value Management (EVM). One year into the project, the periodic EVM analysis results over the
year revealed that the project’s schedule and budget have not been on track. The main
reason, according to Emma, was the high turnover of the project staff. High turnover
of the project staff (average 52.7% annual) had become a big issue on the project. The
project would invest huge resources in training the new employees to bring then onboard
quickly, many of whom would leave the project pre-maturely. The project would hire
more temporary people to fill the vacancies but they had to be trained from scratch and
there was a lengthy lead time before the new hires were able to contribute any significant
value to the project. This staff turnover cycle had become a norm and it was hurting the
project and KC in turn badly.
Eventually, KC Project Director James Rodriguez realized that the water was over the
company’s head and something needed to be done. He decided to engage an outside
consultant, Rick Albany, to investigate the situation and suggest the best possible remedial solution.
36
Project Management Analytics
Initial Investigation
The first logical step Rick took toward investigation was to review KC’s historical organizational project artifacts3 to understand whether the company had encountered a similar
situation before. After reviewing archived artifacts including lessons learned, issue logs,
risk databases, and decision logs for three weeks, Rick found that the staff turnover
rate started ramping up exponentially since 2008 and it became worst while the project
was being investigated. He noticed that nothing was done to address the situation all
along. He also found that KC used to have mostly permanent staff prior to the economic
downturn impact it faced in 2008. That was a bad year for KC that pushed the company
very close to filing bankruptcy. That led the company to lay off most of its permanent
staff. Thereafter, the company changed its hiring strategy to hire all new personnel on a
LT basis (depending upon the length of the project the personnel were being hired for).
During the planning stage of the project, Emma, the project manager suggested to KC
management that the company should consider hiring at least some key positions on
a permanent basis to maintain business continuity due to the long-term nature of the
project. Emma’s suggestion, however, was overruled by the KC management. Therefore,
the project was staffed with mostly LT positions.
Further Root Cause Analysis (RCA)
Rick invited key project stakeholders4 for a brainstorming session to find the root cause(s)
and potential remedies for the issue of turnover. With Rick facilitating, the brainstorming session was conducted. Rick decided to use a fishbone diagram, affinity diagram, and
Pareto chart to capture and analyze the data. First he captured the raw inputs from the
brainstorming session participants, as shown in Figure 2.4.
3
“The historical organizational project artifacts refer to an organization’s historical artifacts archived
from other similar projects completed previously. Leveraging lessons learned, historical information,
tools, and other artifacts from previously done similar projects can save the project at hand a lot of
time and money.” Source: Singh, H. (2014). Mastering Project Human Resource Management, 1st ed.
Upper Saddle River, New Jersey: Pearson FT Press.
4
“Key stakeholders are stakeholders with high power, influence on the project, and interest in the success
or failure of the project.” Source: Singh, H. (2014). Mastering Project Human Resource Management,
1st ed. Upper Saddle River, New Jersey: Pearson FT Press.
Chapter 2 Data-Driven Decision-Making
37
Vision not communicated
Micro-management
Short-term vision
Autocratic managers
Expensive living
Work-salary imbalance (less
salary, more work)
Expensive housing
Irregular salary disbursement
Fear of natural calamities
Unsupported/obsolete
technologies
Houston climate
Inadequate/lacking technologies
Limited chances of gaining
permanent status
Lack of/inadequate training
No chances of gaining
permanent status
Permanent but moveable
(re-locatable) position
Project Staff Turnover
Possible Causes
(Brainstorming Raw
Inputs)
No bonus, free parking,
overtime, and health benefits
Inadequate bonus and other perks
Limited-Term and/or part-time position
Lack of tools required to do the job
Permanent but part-time position
Inadequate/outdated tools for the job
Limited chances of growth of job
status and salary
No chances of growth of job
status and salary
Highly complex job
Long hours
Job involves inherent danger
Competition offers better jobs
Job involves dirty work
Competition offers more jobs
Competition offers benefits
Competition offers more benefits
Figure 2.4 Brainstorming Raw Inputs
After capturing the raw inputs from all brainstorming participants, Rick used an affinity
diagram,5 shown in Figure 2.5, to categorize them. He identified the following categories:
5
38
The affinity diagram is typically used after a brainstorming session to organize a large number of
ideas into relevant categories for ease of analysis.
Project Management Analytics
■
Tools and technologies
■
Management
■
Compensation
■
Working/living conditions
■
Competition
■
Tenure
■
Nature of job
■
Future prospects
Working/Living Conditions
Calamities
Weather
Tenure
Inflation
Overall Term
Hot
Hurricanes
Expensive housing
Humid
Floods
Expensive living
Location-based Term
Permanent but part-time
Permanent but Moveable
Limited-Term and/or
part-time
Temporary
Competition
Future Prospects
Benefits
Jobs
Career Growth
Permanent Status
Competition offers benefits
Competition offers better jobs
Competition offers more benefits
$
Competition offers more jobs
Project Staff Turnover
(Affinity Diagram)
Compensation
No chances of gaining
permanent status
No chances of growth of job
status and salary
Limited chances of gaining
permanent status
Limited chances of growth of
job status and salary
Management
Salary
Benefits
Work-salary imbalance (less
salary, more work)
No bonus, free parking,
overtime, and health benefits
Irregular salary disbursement
Inadequate bonus and other perks
Management Style
Vision
Micro-management
Short-term vision
Autocratic
Vision not communicated
Tools & Technologies
i
Tools
Technologies
Difficulty
Lack of tools required to do the job
Nature of job
Danger/Cleanliness
Unsupported/obsolete
technologies
Inadequate/outdated tools for the job
Inadequate/lacking
technologies
Lack of/inadequate training
Highly complex job
Job involves inherent danger
Long hours
Job involves dirty work
Figure 2.5 Affinity Diagram Displaying Categories of Various Causes for Staff Turnover
Chapter 2 Data-Driven Decision-Making
39
In the next step, Rick transferred the categorized information from the affinity diagram
to a fishbone6 or cause-and-effect diagram, shown in Figure 2.6, and discussed it with
the key stakeholders participating in the brainstorming session.
All participants anonymously approved the possible causes identified in the fishbone
analysis. Rick suggested that the KC human resources department frame exit interview
questions based on the “identified possible causes” and ask them from all the personnel
leaving the project over the next three months. He also suggested asking similar questions to the existing staff as well to understand what would motivate them to stay.
After three months, the collected data was analyzed. Table 2.1 captures the percentage of
votes for the criticality of each type (category) of possible cause.
Table 2.1
Percentage of Votes for Each Area of Criticality
Category
% Votes
Tools and technologies
11.7
Compensation
15.0
Competition
1.2
Nature of job
2.6
Management
5.2
Working and living conditions
3.3
Tenure
46.4
Future Prospects
14.6
Rick used Microsoft Excel to develop a Pareto chart, shown in Figure 2.7, to focus KC
management on the areas that needed the most attention.
6
40
The fishbone diagram (also known as a cause-and-effect diagram or Ishikawa diagram) is used to
help identify various causes that lead to certain effects.
Project Management Analytics
Lack of tools required to do the job
Tools
Inadequate/outdated tools for the job
Unsupported/obsolete technologies
Tools & Technologies
Inadequate/lacking technologies
Technologies
Lack of/inadequate training
Work-salary imbalance (less salary, more work)
$
Salary
Irregular salary disbursement
Compensation
No bonus, free parking, overtime, and health benefits
Benefits
Inadequate bonus and other perks
Competition offers more jobs
Jobs
Competition offers better jobs
Competition
Competition offers benefits
Benefits
Competition offers more benefits
Highly complex job
Difficulty
i
Long hours
Nature of job
Job involves inherent danger
Danger/Cleanliness
Job involves dirty work
Project Staff Turnover
Micro-management
Management Style
Management
Autocratic
Short-term vision
Vision
Vision not communicated
Hot
Weather
Humid
Hurricanes
Calamities
Working/Living Conditions
Floods
Expensive housing
Inflation
Expensive living
Permanent but part-time
Overall Term
Limited-Term and/or part-time
Tenure
Permanent but Moveable
Location-based Term
Temporary
No chances of gaining permanent status
Permanent Status
Future Prospects
Limited chances of gaining permanent status
No chances of growth of job status and salary
Career Growth
Limited chances of growth of job status and salary
Figure 2.6 Fishbone Analysis for Possible Causes for Staff Turnover
Chapter 2 Data-Driven Decision-Making
41
% Votes
tit
i
on
Jo
b
e
C
om
pe
an
g
W
or
ki
n
N
at
ur
liv
in
d
ag
of
g…
t
em
en
ie
d
To
o
ls
an
Fu
M
an
Te
ch
n
ol
og
os
pe
Pr
e
tu
r
s
ct
s
n
ns
at
io
C
om
pe
Te
n
ur
e
50
45
40
35
30
25
20
15
10
50
0
% Votes
Figure 2.7 Pareto Chart Highlighting Most Critical Areas Needing Improvement
Based on the analysis, the top four areas that demanded immediate attention included
tenure, compensation, future prospects, and tools and technologies. Rick outlined the
following alternatives going forward:
Alternative 1: Do nothing and live with the status quo.
Alternative 2: Convert the key project positions to permanent full-time.
Alternative 3: Convert the key project positions to permanent full-time, offer
competitive compensation, improve job tools and technologies, ensure appropriate training, and improve opportunities for growth.
Decision-Making
Rick performed a comprehensive alternative analysis and discussed cost versus benefits
for each alternative with KC management, which then decided to pursue alternative 3.
Action Plan
KC management drafted the following action plan to implement alternative 3:
■
42
Make all key project positions (such as project director, project manager, project
scheduler, business analysts, project cost analysts, and project quality analysts)
permanent full-time
Project Management Analytics
■
Adjust paygrades to competitive levels
■
Improve benefits (for example, match 401k contributions up to 3%, resume
sabbatical leaves, fund Christmas breakfast and company picnics, and initiate a
rewards and recognition program)
■
Upgrade staff laptops to better models
■
Implement SharePoint and Project Server for improvement in collaboration, productivity, and project management
■
Ensure appropriate training for the project staff to learn new tools and technologies, to improve productivity in the current job, or to prepare for promotional
opportunities
■
Enhance opportunities for career growth within the organization (for example,
start a Leadership Academy program to provide special leadership training to the
employees who have desire and aptitude for the leadership positions)
Results
KC started observing the positive results within a month after the action plan was
implemented. After one year of the plan implementation, the annual staff turnover rate
dropped from average 52.7% to merely 8.6%, an 83.68% improvement.
Case Study Questions
1. What data analytics tools did Rick Albany use to capture and analyze the data in
this case?
2. What is fishbone analysis? How does it help in decision-making?
3. How effective was data-driven decision-making in this case?
Chapter Review and Discussion Questions
1. Define data-driven decision-making.
2. List some of the key decisions made during the project life cycle.
3. What is meant by the term analysis paralysis?
4. What are the advantages of using data-driven decision-making in project
management?
5. What methodologies or approaches can be used to automate and manage the
process of decision-making?
6. What is the difference between predictive and prescriptive analytics?
Chapter 2 Data-Driven Decision-Making
43
7. What is meant by garbage in, garbage out?
8. Define pragmatism.
9. What are typical steps in a data-driven decision-making process?
10. Discuss some challenges associated with the data-driven decision-making process.
Bibliography
BI Insights. (2013). “6 Steps to Becoming a Data-Driven Decision Maker.” Retrieved March 7,
2015, from http://businessintelligence.com/bi-insights/6-steps-to-becoming-a-data-drivendecision-maker/
Butler Analytics. (2015). “Decision Oriented Business Process Management.” Retrieved March 8,
2015, from http://butleranalytics.com/wp-content/uploads/Decision-Oriented-Business-ProcessManagement.pdf
Davenport, T. H. (2013). “Keep Up with Your Quants,” Harvard Business Review. Retrieved
March 10, 2015, from https://hbr.org/2013/07/keep-up-with-your-quants
Ferris, B. (2012). “Why You Need to Be a Decisive Project Manager.” Retrieved March 9, 2015,
from http://cobaltpm.com/why-you-need-to-be-a-decisive-project-manager/
Frick, W. (2014). “An Introduction to Data-Driven Decisions for Managers Who Don’t Like
Math,” Harvard Business Review. Retrieved March 6, 2015, from https://hbr.org/2014/05/
an-introduction-to-data-driven-decisions-for-managers-who-dont-like-math
Pitagorsky, G. (2013). “Decision Making – A Critical Success Factor.” Retrieved March 8, 2015,
from http://www.projecttimes.com/george-pitagorsky/decision-making-a-critical-success-factor.
html
Rouse, M. (2012). “What Is Decision Management?” Definition from WhatIs.com. Retrieved
March 9, 2015, from http://whatis.techtarget.com/definition/decision-management
Shelushkov, A. (2014). “Gaining Competitive Advantage with Data-Driven Decision Making.”
Retrieved March 8, 2015, from http://www.itransition.com/blog/gaining-competitiveadvantage-with-data-driven-decision-making/
Singh, H. (2014). Mastering Project Human Resource Management, 1st ed. Upper Saddle River,
New Jersey: Pearson FT Press.
Skulmoski, G.J. et al. (2010). “Information Systems Project Manager Soft Competencies: A
Project-Phase Investigation.” Project Management Journal, 41(1): p. 63.
Villanova University. (2015). “Importance of a Decisive Project Manager.” Retrieved March 6,
2015, from http://www.villanovau.com/resources/project-management/importance-of-projectmanager-decisiveness/#.VRTnafnF-7w
44
Project Management Analytics
3
Project Management Framework
Learning Objectives
After reading this chapter, you should be familiar with
■
Project definition and characteristics
■
Project constraints
■
Project success criteria
■
Why projects fail
■
Project versus operations
■
Project, program, and portfolio management
■
Project Management Office (PMO)
■
Project life cycle
■
Project management life cycle
■
Systems (software) development life cycle
■
Project processes
■
Work Breakdown Structure (WBS)
“All things are created twice; first mentally, then physically. The key to creativity is to
begin with the end in mind, with a vision and a blue print of the desired result.”
—Stephen Covey, Author of The Seven Habits of Highly Effective People
45
Because the discussion in this book focuses on project management analytics, you must
clearly understand the context or environment (project management framework1)
within which the project management analytics knowledge is targeted. This chapter
defines some key project management terms in addition to providing you an overview
of the project management framework, including the Project Life Cycle (PLC), Project
Management Life Cycle (PMLC), Systems (Software) Development Life Cycle (SDLC),
and the project management processes.
What Is a Project?
A project is a temporary2 endeavor taken on to create a unique product, service, process,
or outcome. It is a temporary endeavor because it has a definite start and a definite end.
It also uses a specific scope and budget, and it involves a particular set of operations
targeted to achieve an unusual goal.
A project is initiated when a unique business need has to be fulfilled and a project manager is authorized (via the approval of a project charter3) to undertake the efforts to fulfill
that business need. A project ends for various reasons, such as
1
■
The project objectives have been met.
■
The project is terminated (prematurely) due to lack of confidence that the project
objectives can be met.
“Project management framework (PM framework) is a subset of tasks, processes, tools, and templates
used in combination b…