The Dynamic Progress Method Using Advanced Simulation to Improve Project Planning and Management

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.

IntroductionBackground of ResearchGetting Good Projects for the ResearchResults of the DARPA SBIR EffortExample: Large Defense ContractBasic Issues with Microsoft Project Algorithms (and the CPM)Direct Comparisons between Microsoft Project and pmBLOXWhat Does This Mean for Project Managers?ReferencesWhy "Dynamic Progress Method?"IntroductionUnderstanding Systems and System ComplexityClasses of Business ModelsSystem Complexity and Project ComplexityIntroducing the DPMReferencesThe Current Status of Project ManagementIntroductionProject Failure Rates Are Greater Than ZeroLarger Projects Suffer More Than Smaller ProjectsSome Project Failures Are PreventableThe Need for a Revolutionary Project Planning and Management ToolReferencesCritical Path Method and Earned Value ManagementIntroductionCPM RefresherSome Comments on Program Evaluation and Review TechniqueBenefits and Disadvantages of PERT/CPMSome Comments on Critical ChainUncertainty and Monte Carlo AnalysisAdditional Issues with PERT/CPMEV RefresherReferencesThe New Approach of Dynamic Progress MethodIntroductionA Simple Project Framework for ConsiderationDPM and PERT/CPM: Different Sides of the Same CoinCPM and System DynamicsReferencesOverview of the Dynamic Progress Method Simulation ModelIntroductionBasic Task StructureResource ProductivityManagement Corrective ActionsConsequences of Corrective ActionsA Final Note on the DPM ModelOverview of pmBLOXIntroductionInstalling and Running pmBLOXExample 1—Creating Your First Project PlanExample 2—Defining a Task ResourceExample 3—Productivity ImpactsExample 4—Varying Productivity ImpactsExample 5—Responding to Reduced ProductivityExample 6—Multiple Task ResourcesExample 7—The Role of Scope ModeExample 8—Task DependenciesExample 9—Working with MaterialsFinal CommentsAdvanced Capabilities of Dynamic Progress MethodIntroductionExample Microsoft Project FileImporting a Microsoft Project XML File into pmBLOXAccelerating the ProjectSchedule 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.