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Enterprise Project Portfolio Management: Building Competencies for R&D and IT Investment Success by Richard M. Bayney Ram Chakravarti J. Ross Publishing, 2012, ISBN: 9781604270600, hardcover, 384 pp., $56.95 Member, $69.95 Nonmember.

“Rank and yank” is a common technique used to select projects for the portfolio. First you prioritize them based on predefined criteria. Then you select those that rank high on the list based on your resource constraints and rid the others. Richard M. Bayney and Ram Chakravarti in Enterprise Project Portfolio Management: Building Competencies for R&D and IT Investment Success present a narrative, tools, and case studies to demonstrate that the ranking technique creates a sub-optimal portfolio.

The authors argue that you should first optimize the portfolio to select the right combination of projects before prioritization. An optimized portfolio most likely will not include the same projects you would select by simple ranking. But it will produce greater value. You may be “leaving value on the table” by not optimizing the portfolio. In addition to optimization and prioritization, effective portfolio management involves other key processes, which are discussed in the book in the form of the CREOPM™ framework. The foundation of the book, CREOPM™ stands for Project Categorization, Risk Analysis, Integrated Evaluation, Portfolio Optimization, Project Prioritization, and Portfolio Management.

Under categorization, projects are divided into three groups in the book: Must Do, Won't Do, and May Do. “Must Do” projects are compliance driven, mandates, or critical to the strategic and financial success of an organization. “Won't Dos” are technically and commercially unattractive and are sometimes alive because of sunk cost bias. “May Dos” are considered the true discretionary projects with technical and commercial merit.

Project risk is differentiated from portfolio risk as the likelihood of project completion meeting scope, time, and cost targets versus the likelihood of meeting organizational goals. Portfolio balance, value of information analysis, and options analysis are presented as key factors in risk analysis. Project evaluation, particularly related to project financials, is discussed with special attention to stochastic methods. Several IT investment valuation techniques (total cost of ownership, rapid economic justification, IT scorecard, business value index, etc.) are reviewed, underscoring the absence of one unified technique.

Several portfolio optimization techniques that involve binary integer linear programming, goal programming, multiple objective linear programming, and stochastic optimization are described. The concept and application of “efficient frontier” in portfolio optimization are briefly discussed. Examples are given to show how a portfolio can be optimized to yield the highest value (e.g., NPV) while accounting for budgetary and human resource constraints and any required dependencies between projects in the portfolio.

Prioritization models that utilize multiple objective decision analysis (MODA) and analytic hierarchy process (AHP) are illustrated with examples. Several portfolio management best practices related to capability maturity model (CMM), portfolio analysis, risk management, resource management, and stakeholder management are discussed.

The book has 12 chapters, grouped under four sections. The first section consists of one chapter that describes typical pharmaceutical R&D and IT portfolios and introduces the CREOPM™ framework. Section 2 provides an orientation to scenario analysis and strategic planning, followed by an overview of decision making. Section 3 has six chapters, each focusing on CREOPM™’s individual components. Section four is dedicated to two portfolio management case studies, one in IT and one in R&D. The appendices include primers on decision analysis and mathematical programming and a scoring model for prioritization of IT investments.

While the book presents a practical framework for portfolio management professionals, many chapters are academically oriented. Familiarity with decision trees, simulations, stochastic optimization, linear programming, and decision analysis will help the reader better understand and appreciate the technical details behind portfolio optimization.

The techniques presented in the book offer great value for managing not only IT and R&D but also other types of portfolios involving large investments. The general CREOPM™ framework can easily be applied to any portfolio, irrespective of its type and size.

Biography

  1. Top of page
  2. Biography
  • Reviewed by Prasad S. Kodukula, PhD, PMP, PgMP, President of Kodukula & Associates, Chicago, IL, USA, and recipient of the 2010 PMI Distinguished Contribution Award.