Running the Successful Hi-Tech Project Office (Artech House Technology Management Library) - page 78


Index

V

Validation, 68

Verbal scale, 160

Visual mappings, 156-57

bubble diagram, 156

resource-requirements map, 157

tools, 156-57



Index

W

Weinberg-Schulman experiment, 26

Work breakdown structures (WBS), 84

in Performance Analyzer, 124

standardized, 87

time reporting and, 123

Work/life conflict, 13

Work product description metadata, 108-9

Work stress dynamics, 12



List of Figures

Chapter 1: Introduction

Figure 1.1: Project simulation results: x-axis shows project duration; y-axis shows number of occurrences for a given duration. (After: [2].)
Figure 1.2: Expected results versus what really happened and what was possible.
Figure 1.3: Actual progress report showing typical coordination problems in multiproject environment (employee and product names have been changed).
Figure 1.4: Relationships among project management, project portfolio management, and line management. (After: [4].)
Figure 1.5: Effects of project management on development costs. (a) Management influence on cost: The y-axis shows the difference between the actual and the expected hours spent in the development of new pharmaceutical products after discounting project complexity, production scale, and therapeutic class. (b) Waste on IT projects: Almost one-third of the total spending, billions, never comes to fruition. (c) Organizations where projects are executed in functional silos waste, on average, 42% more effort than organizations that use the portfolios approach owing to inconsistent decision making concerning the termination of failed projects. (After: [7–9])
Figure 1.6: Quality-of-life indicators, 1991 versus 2001. (After: [10].)
Figure 1.7: Dynamics of work stress. (After: [11].)
Figure 1.8: Survey results showing positive perception of PO concept. (Source: [12].)
Figure 1.9: Perceived obstacles to PO deployment. (Source: [12].)

Chapter 2: The multiproject challenge

Figure 2.1: Relationships among principal management variables in a multiproject environment.
Figure 2.2: Sources of scheduling uncertainty. (After: [4].)
Figure 2.3: Interactions in the multiproject environment.
Figure 2.4: Feedback structure of multiproject product development. (After: [6].)
Figure 2.5: Execution modes in a multiproject environment. (After: [6].)
Figure 2.6: Quantity over quality. (After: [7].)
Figure 2.7: Software quality assurance and peer reviews are among the least satisfied key process areas of the Software Engineering Institute's Capability Maturity Model. (After: [9].)
Figure 2.8: Strategies for dealing with time pressure. (After: [14].)
Figure 2.9: Relative costs of fixing problems when originated during formal testing and in the field.
Figure 2.10: Relationship between timing of changes and rework costs in a mechanical engineering project for three different components. Numbers in circles correspond to order of changes. (After: [15].)
Figure 2.11: Hours at the office versus productive working hours. (After: [16].)
Figure 2.12: Analysis of aviation accidents due to human factors. (After: [17].)
Figure 2.13: Spending, commitment, and management intervention patterns.
Figure 2.14: Projects canceled after detailed design work in best-in-class firms as compared with other corporations. (After: [21].)
Figure 2.15: Loss of productivity due to task switching. (After: [20].)
Figure 2.16: Spitzer experiments showing student retention over time. (After: [23].)
Figure 2.17: Time allocation in project work. (After: [26].)
Figure 2.18: Work trajectory for an engineer assigned to a new-product development project. (Source: [27].)
Figure 2.19: According to a majority of Project Managers it takes at least four weeks to get a new team member up to speed. (After: [29].)
Figure 2.20: Communications path in an organization. Although the original study refers to a functional organization, the same pattern is likely to be found in a project whose sections correspond to subsystems and section heads to team leaders or subproject managers. (Source: [30].)
Figure 2.21: Effect of team size in communications. (After: [31].)
Figure 2.22: Prevalence of scope reductions to recover from a late project. (After: [32].)

Chapter 3: The project office

Figure 3.1: PO reporting relationships.
Figure 3.2: PO interfaces.
Figure 3.3: Master plan, resource plan, and financial forecast.
Figure 3.4: Requirements dependency matrix.
Figure 3.5: PO main processes.
Figure 3.6: Project life cycel processes.
Figure 3.7: Project formulation process.
Figure 3.8: Project startup process.
Figure 3.9: Project execution process.
Figure 3.10: Project closure process.
Figure 3.11: Project audit process.
Figure 3.12: Tollgate process.
Figure 3.13: Portfolio-management process.
Figure 3.14: Project portfolio-planning process.
Figure 3.15: Project oversight process.
Figure 3.16: Portfolio control process.
Figure 3.17: Support processes.
Figure 3.18: Process and information systems management.
Figure 3.19: Measurement process.
Figure 3.20: Change management process.
Figure 3.21: Procurement management process.
Figure 3.22: Quality assurance process.
Figure 3.23: Project accounting process.
Figure 3.24: Human-resources management process.

Chapter 4: Processes

Figure 4.1: Roles in project life-cycle management.
Figure 4.2: Standardized WBS. (After: [1].)
Figure 4.3: Project review lines of inquiry concerning requirements, management, and work climate. (After: [3].)
Figure 4.4: Two projects with identical best-case (10 months) and most likely (15 months) duration scenarios, but different worst-case duration scenarios (25 versus 40 months), have very different on-time probabilities (in the first project, the probability of finishing within or before 15 months is 34%, and in the second, it is 18%).
Figure 4.5: Budget structure.
Figure 4.6: Requirements management schema.
Figure 4.7: Risk-management process.
Figure 4.8: Risk-assessment method. (After: Risk Management Guide for DoD Acquisitions, 4th Edition, Defense Acquisition University Press, Feb. 2001)
Figure 4.9: CM levels.
Figure 4.10: Ericsson competence model. (Source:[13].)

Chapter 5: Tools

Figure 5.1: Different stakeholders have different objectives but all can be reduced to four basic information needs: What? When? Who? Where are we?. (After: [2].)
Figure 5.2: Master and resource plans: The workload curve changes in response to the movement of the projects on top.
Figure 5.3: Capacity-versus-demand charts.
Figure 5.4: Project positioning chart (benefits versus cost).
Figure 5.5: Project risk exposure.
Figure 5.6: Portfolio tracking.
Figure 5.7: Project planning, Gantt view.
Figure 5.8: Resource allocation display.
Figure 5.9: Project tracking tool.
Figure 5.10: Sample document management tool: Ericsson's Virtual Project Room (VPR).
Figure 5.11: Sample risk-management tool: Technical Risk Identification and Mitigation System (TRIMS) risk panel developed by the U.S. Navy for its Best Manufacturing Practices program. TRIMS is based on proven risk models (such as those from the Software Engineering Institute), published practices, and the Navy's best-practices templates and can be applied to all phases of both military and commercial programs.
Figure 5.12: Organizational and work-breakdown structures in Performance Analyzer, a tool for the analysis of cost performance reports, cost/schedule status reports, and contract funds status.
Figure 5.13: Performance Analyzer composite display showing financial performance.
Figure 5.14: Gartner Group's Magic Quadrants. Tools named in the gray ovals were added by the author to reflect product evolution; they do not appear in the original study. (After: [7].)

Chapter 6: Balancing the project portfolio

Figure 6.1: Portfolio-balancing process.
Figure 6.2: Project formulation process.
Figure 6.3: Three-point estimate.
Figure 6.4: Cumulative probability distribution.
Figure 6.5: Project schedule risk.
Figure 6.6: Schedule-risk calculations.
Figure 6.7: Cost of recovery. (Source: [3].)
Figure 6.8: Economics of project insurance—acceptable risk level set at the expected project completion date.
Figure 6.9: Economics of project insurance—acceptable risk level set at the project completion date with a 75% probability of being met.
Figure 6.10: Project cancellation and the propagation of consequences.
Figure 6.11: Bubble diagram.
Figure 6.12: Resource-requirements map.
Figure 6.13: Paired comparisons process.
Figure 6.14: NPV project valuation.
Figure 6.15: Decision-tree project valuation.
Figure 6.16: Mapping between real and financial options parameters. (After: [16].)

Chapter 7: Quantitative management

Figure 7.1: Measurements and the multiple purposes they serve.
Figure 7.2: Variability present in measurements due to uncontrollable factors.
Figure 7.3: Scatter plot showing the relationship between two measurement values.
Figure 7.4: Relationships between measurement variables: typical patterns.
Figure 7.5: Common problems in the analysis of relationships.
Figure 7.6: Aggregation patterns: Measurements including time report must be available at different levels of aggregation and along the time dimension.
Figure 7.7: Relationship between the number of elements summed, the coefficient of correlation, and the standard deviation of the sum.
Figure 7.8: Timing of the measurement contains as much information as its magnitude.
Figure 7.9: Progress, measured in terms of its visible output, is not constant through the duration of a task or project.
Figure 7.10: The S curve.
Figure 7.11: Error incurred by using linear forecast instead of S curve.
Figure 7.12: Using historical data to build an estimation model.
Figure 7.13: Danger of using historical data for estimation purposes. Normal productivity deteriorates over time.
Figure 7.14: Influence diagram showing effect of decisions.
Figure 7.15: Positive and negative feedback loops.
Figure 7.16: Model of software project dynamics showing relationship between different management variables.
Figure 7.17: Interpreting cost and schedule performance indexes.
Figure 7.18: Staffing profiles: (a) aggregated across all competence areas; (b–e) for each competence area. This level of detail is necessary because the aggregated curve could mask surplus in one area with shortfalls in another, which are not interchangeable.
Figure 7.19: Control chart assessing project morale. Project 1 seems to be experiencing a larger-than-expected turnover. This might be indicative of morale problems within the project.

Chapter 8: Deploying the project office

Figure 8.1: Business-process-improvement layers of change. (After: [2].)
Figure 8.2: Incongruent messages and their negative effect on overall performance. (After: [3].)
Figure 8.3: How the mental model conditions the active process of perception. (After: [8].)
Figure 8.4: Different perceptions of realities at ABB, Ericsson, and Saab. (Source: [10].)
Figure 8.5: Risk behavior. (Source: [16].)

Appendix A: IDEF0 notation

Figure A.1: IDEF0 syntax.
Figure A.2: IDEF0 hierarchical structure.