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• In investment categories (run, grow, transform the business)
• By business unit
• By product line
• By geography
For smaller, singularly focused companies, a single portfolio showing all of these investments may be appropriate. In addition, each of these allocations can be further broken down according to risk versus return, portfolio types, and so on.
Typically, there are more investments than a company can afford. The highest-scoring to the lowest-scoring investments are usually shown in order. A cutoff point is reached when cumulative investment costs equal the budgeted amount. Investments that fall above this line have a high likelihood ofbeing accepted;those falling below the line will probably be canceled or put on hold.
And now the fun begins. Tools alone do not make decisions; people do! So on first impression, you may think that the perfectly balanced, aligned, achievable, and
STAGE 5: BALANCING 25 1
valuable portfolio has been built. The prioritized ranking was built on the foundation of decision maker input on the development, definition, and weighting of the criteria. But variables such as uncertainty and subjectivity in some of the data and information, interdependencies, constraints (costs, resources, capacity, timelines of value creation, acceptable risk and return levels), changing conditions and priorities, and other factors can result in multiple iterations and adjustments by the decision makers to optimize the portfolio. Alternatives range from adjusting a few variables through what-if analysis (e.g., testing the sensitivity of the portfolio to adjustments made to risk threshold levels, budget plus-ups or cutbacks, or alterations to strategic scenarios) to scenario planning, where multiple-value dials are adjusted. In addition to the bucket method, there are other approaches, examples shown in Exhibit 5.38, that companies can use to assess portfolio balance.
EXHIBIT 5.38 ADDITIONAL MODELS USED TO ACCESS THE BALANCE OF THE IT PORTFOLIO
• Mathematical programming: produces an optimal set of investments based on objectives and constraints. Mathematical models use detailed sensitivity analysis through many models and methods such as integer, linear, nonlinear, dynamic, and goal programming.
• Contingent portfolio programming (CPP): recent approach to portfolio balancing, combining decision trees with mathematical programming. Utilizes uncertainties with certainty equivalents.
• Pictorial diagrams: two-dimensional bubble charts, histograms, line and pie charts graphically showing investments under consideration.
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.
Gustafsson, J., Salo, A. (2001). Managing Risky Projects with Contingent Portfolio Programming, unpublished manuscript.
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.
Lockett, A. G., Gear, A. E. (1973). “Representation and Analysis of Multi-Stage Problems in R&D,” Management Science, 19/8, pp. 947-960.
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.
Taha, H. A. (1997). Operations Research—An Introduction, 6th edition, Prentice-Hall.
252 CHAPTER 5 BUILDING THE IT PORTFOLIO
Trade-offs in the Application Portfolio
The application portfolio is one of the more active portfolios that is frequently balanced. Many applications paid for the infrastructure needed to run the application, leading to ad hoc infrastructural growth and maintenance of unique components in support of aging applications. Adding to this chaos is the fact that applications may be added and not retired, so the actual portfolio grows and becomes a burden that inhibits change.
Determining how to balance the application portfolio should begin with a firm understanding of the objectives and key performance indicators a company is trying to deliver over the short term, medium term, and long term. These are mapped against the as-is and to-be business processes, and a gap analysis is developed. Existing applications are analyzed to determine their (also see Chapter 4):
• Functional quality
• Data completeness
• Data accuracy
• Data consistency
• Data currency
• System quality
• Technical quality
• Architectural (development, environment, middleware, database, server, storage, network protocol, client code, etc.)
• Operational (job scheduling, program management, change management, system monitoring, vendor/contract management)
• Costs (application maintenance, operations, software, hardware, depreciation)