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IT Portfolio management step by step - Maizlish B

Maizlish B, Handler R. IT Portfolio management step by step - John Wiley & Sons, 2005. - 401 p.
ISBN.: 978-0-471-64984-8
Download (direct link): itportfoliomanagement2005.pdf
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The subsections that follow show the methods, models, and approaches used to assess investments:
• Scoring methods
• Standard financial models
• Advanced modeling and simulation
• Nonnumeric models
The maturity of companies in using IT portfolio management will dictate whether they use two or more models. Companies at the highest maturity level may use a combination of most of the models, while companies that are just beginning tend to use the financial and the nonnumeric models. The key is not to get bogged down in analysis paralysis and to make decisions based on the best information available.
Scoring Methods
Scoring methods provide a structured framework that allows comparison of qualitative and quantitative criteria to derive weights and establish priorities of alternatives used within the decision-making process.10 Multiattribute value tree (MAVT) analysis is a scoring methodology that gives meaning to multiple tangible and intangible objectives that may have conflicting goals and priorities in support of the decision-making process.11 The analytical hierarchy process is a commonly used scoring model for value tree analysis. The advantages and disadvantages of using scoring models in assessing investments and the IT portfolio are shown in Exhibit 5.31.
Standard Financial Models
Many financial models address the time value of money, translating costs and benefits into offsetting streams of discounted cash flows. The most commonly applied financial models are shown in Exhibit 5.32.
Return on investment (ROI), productivity index, profitability index, and payback periods are examples of additional financial models to assess the IT portfolio. Sensitivity analysis is an effective adjunct to financial models, offering a range of perspectives based on various scenarios. The advantages and disadvantages of using financial models in assessing investments and the IT portfolio are shown in Exhibit 5.33.
Advanced Modeling and Simulation
Advanced modeling and simulation approaches are explained in Exhibit 5.34. Unlike the scoring method or the financial models, the modeling and simulation approaches examine more than one single score or value. These models require
Scoring Methods Used to Assess IT Investments
Advantages Disadvantages
• Accepted practices, standard definitions • Result is a relative measure that has no real value or
• Easy to use • Weights may be based on subjective criteria
• Based on standardized weighting of company • There is independence between factors
priorities and objectives
• Considers both quantitative and qualitative inputs • It does not result in multiple decisions or possible
timing of options
• Factors risks and uncertainties through incorporating • Assumes that the highest scored investments should
the probability of success be given higher priority of consideration than lower-
• Produces scores for individual investments scoring investments --- it does not answer the
fundamental question: Is this a good investment?
• Allows users to adjust weights and parameters to
enable what-if scenarios and analysis
Sources: Adapted from:
Archer, N. P., Ghasemzadeh, F. (1996), “Project Portfolio Selection Techniques:A Review and a Suggested Integration Approach,” Innovation Research Working Group Working Paper No. 46, McMaster University.
Henriksen, A. D., Traynor, A. J. (1999). “A Practical R&D Project-Selection Scoring Tool,” IEEE Transactions on Engineering Management, 46/2, pp. 158—170.
Martikainen, Juha (2002). “Portfolio Management of Strategic Investment in the Metal Industry,” master’s thesis, Helsinki University of Technology, January.
Matejcik, F J. “TM 665 Project Planning & Control,” South Dakota School of Mines and Technology.
Meredith, J. R., Mantel, S. R., Jr. (2000). Project Management—A Managerial Approach, John Wiley & Sons.
Steele, L. W. (1988). “What We’ve Learned—Selecting R&D Programs and Objectives,” Research Technology Management, Mar.—Apr., pp. 17—36.
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