Recent computer-based tools for project planning and management focus on user-friendliness and interconnectivity. However, these programs function on the Critical Path Method, or CPM, which was created in the 1950s. These programs, which involve simplistic models and methods, ignore the fact that the underlying computations on which they function have become woefully inadequate for the complex projects of today.
The product of nearly a decade of work, The Dynamic Progress Method: Using Advanced Simulation to Improve Project Planning and Management provides an overview of the research conducted while illustrating some of the issues with current approaches. It presents the Dynamic Progress Method (DPM), an innovative simulation-based approach to project management. It also includes instructions on how to use the accompanying DPM-based simulation tool pmBLOX to plan, manage, and analyze projects.
This groundbreaking book is a must-have resource for project planning and management. It introduces a new and better way of planning, estimating, and managing projects that corrects some of the fundamental flaws of the CPM. It brings the computational integrity of planning simulations up to speed with modern needs, making it useful not only to current project managers but also to students who will become project managers.
Table of Contents
Background of Research
Getting Good Projects for the Research
Results of the DARPA SBIR Effort
Example: Large Defense Contract
Basic Issues with Microsoft Project Algorithms (and the CPM)
Direct Comparisons between Microsoft Project and pmBLOX
What Does This Mean for Project Managers?
Why "Dynamic Progress Method?"
Understanding Systems and System Complexity
Classes of Business Models
System Complexity and Project Complexity
Introducing the DPM
The Current Status of Project Management
Project Failure Rates Are Greater Than Zero
Larger Projects Suffer More Than Smaller Projects
Some Project Failures Are Preventable
The Need for a Revolutionary Project Planning and Management Tool
Critical Path Method and Earned Value Management
Some Comments on Program Evaluation and Review Technique
Benefits and Disadvantages of PERT/CPM
Some Comments on Critical Chain
Uncertainty and Monte Carlo Analysis
Additional Issues with PERT/CPM
The New Approach of Dynamic Progress Method
A Simple Project Framework for Consideration
DPM and PERT/CPM: Different Sides of the Same Coin
CPM and System Dynamics
Overview of the Dynamic Progress Method Simulation Model
Basic Task Structure
Management Corrective Actions
Consequences of Corrective Actions
A Final Note on the DPM Model
Overview of pmBLOX
Installing and Running pmBLOX
Example 1—Creating Your First Project Plan
Example 2—Defining a Task Resource
Example 3—Productivity Impacts
Example 4—Varying Productivity Impacts
Example 5—Responding to Reduced Productivity
Example 6—Multiple Task Resources
Example 7—The Role of Scope Mode
Example 8—Task Dependencies
Example 9—Working with Materials
Advanced Capabilities of Dynamic Progress Method
Example Microsoft Project File
Importing a Microsoft Project XML File into pmBLOX
Accelerating the Project
Schedule and Cost Trade-Offs
J. Chris White earned his BS in aerospace engineering from the Massachusetts Institute of Technology and his MS in industrial engineering from the University of Michigan. He is President of ViaSim Solutions and is also an adjunct instructor at the University of Texas at Dallas and Texas A&M University–Commerce. He is a Project Management Professional (PMP) and Certified Scrum Master (CSM) as well as a Lean Sensei and a Six Sigma Master Black Belt. He has published numerous articles in the fields of leadership, total quality management, Six Sigma, project management, strategic management, and simulation.
Robert M. Sholtes earned his BS in aeronautical and astronautical engineering from the University of Illinois at Urbana–Champaign and his MS in engineering and public policy from Washington University in St. Louis. He has been a special instructor at the George Washington University and an assistant instructor and research assistant at Washington University. He has published several articles and presented several conference papers in the fields of software development, simulation, and genetic algorithm optimization techniques.