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For some companies, the traditional four focused quadrants of the balanced scorecard (finance, customer, internal process, and learning/innovation) provide reasonable visibility to make strategic decisions. For others, the focus might shift more to specific or narrow targets involving close links with operational measures. For example, simple financial measures are not sufficient to manage the outcomes of many aggressive e-business initiatives. The lessons learned from world-class firms often include:
• The scorecard is not as important as the performance planning and management activity itself
STAGE 4: ASSESSING 243
EXHIBIT 5.34 ADVANCED MODELING AND SIMULATION APPROACHES
Monte Carlo simulation: emerged from the sciences and engineering fields, having been deployed in domains for which the algebraic complexities are effectively unsolvable. In IT portfolio management, Monte Carlo simulation is not used in algebraic problems but rather in an incomprehensible spectrum of possible future outcomes as they affect a present decision. Unlike the scoring or many of the financial models, Monte Carlo does not produce a single value. Monte Carlo analysis results in a distribution of the present value of many possible future outcomes. Monte Carlo simulation is emerging as a modeling tool in IT portfolio management.
Real options: provide the right but not the future obligation to acquire an asset with its associated physical and intellectual capital assets. Real options are the present value of the future right to make a choice involving the full investment in a project. Most financial options have an established extant market, allowing buyers and sellers to readily determine value and volatility. For IT investments, value and volatility are not always as obvious, with little history on which to perform traditional valuation assessments. The key for using real options in valuing IT investments is knowing that at a future point additional information will be available to make a more economically intelligent decision.
Scenario planning: more expansive and more comprehensive than performing what-if analysis. Scenario planning is developing the ability to view and assess what happens if certain worlds are realized—the discrete classifications of the wide range of future possible business outcomes. Using Monte Carlo, there are elegant ways of incorporating scenario planning and modeling.
Decision (probability) trees: show the sequence of possible outcomes of an investment. Cash flows and net present value of a project under different circumstances can be highlighted in a decision tree. The advantage of this approach is the visibility into possible outcomes of the investment, which makes decision makers more cognizant of adverse possibilities and depicts the nature of short- and long-term cash flows. Decision tree analysis is complicated and requires reasonable knowledge of the complexities associated with an investment over the period of time it is evaluated.
Efficient frontier curve: economic concept developed by Dr. Harry Markowitz. The efficient frontier curve displays all possible combinations of optimal values at minimal risks (the impact of a dollar investment juxtaposed to the value received) that can be generated with resources in an unconstrained mode.
Sources: Adapted from:
Richard Razgaitis, Dealmaking Using Real Options and Monte Carlo Analysis, John Wiley & Sons, 2003.
Jae K. Shim and Joel G. Siegel, Handbook of Financial Analysis, Forecasting, & Modeling, Prentice-Hall, 1988.
United Management Technologies, www.umt.com.
244 CHAPTER 5 BUILDING THE IT PORTFOLIO
EXHIBIT 5.35 ADVANTAGES AND DISADVANTAGES OF MODELING AND SIMULATION APPROACHES
Modeling and Simulation Approaches Used to Assess IT Investments
• Evaluation of multiple stages and decisions; limited • High level of expertise to input, operate, and assess is
downside risk needed
• Identifies good investments • Detailed and accurate information and data are
• Focuses management on considering multiple options required
and scenarios • Determining probabilities can be highly subjective
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.