Fuzzy Inference Systems for IT Project Portfolio Management

Overview of IT Project Portfolio Management

Despite the recognized need, the majority of organizations in the United States do not possess the information required to effectively manage their strategic initiatives because the requisite data is stored away in organizational silos (Project and portfolio management, 2014). The need for effective project portfolio management has become even more pronounced as the number and complexity of information technology projects has increased in recent years (Drake & Byrd, 2006). The corresponding growth in the range and scope of IT projects has made the effective management of project portfolios even more complex (Drake & Byrd, 2006). According to Drake and Byrd, "Translating strategic goals into successful projects would help ensure that IT investments resulted in increased business performance. Research into business-IT alignment answered some of the questions about how to translate IT investments in business to business performance" (2006, p. 1).

Today, practitioners are increasingly implementing organizational structures that are specifically designed to support the strategic alignment of IT projects with organizational goals, prioritizing them as the competitive environment changes (Drake & Byrd, 2006). In this regard, Drake and Byrd advise that, "This structure, IT project portfolio management, bridges the gap between project management and strategic management" (2006, p. 2). The function of IT project portfolio management is to evaluate strategic goals and organizational core competencies in order to formulate appropriate information systems for the enterprise — systems capable of communicating and storing information efficiently and effectively (Drake & Byrd, 2006). In the past, strategic information system planning was used for this purpose, but this approach failed to provide the robust attributes that are possible with efficient and effective project portfolio management techniques (Drake & Byrd, 2006).

There are two functions that comprise IT project portfolio management: (a) the planning of new projects and migration to new systems, and (b) the reassessment of ongoing projects (Drake & Byrd, 2006). The first function — the planning phase — can still be initiated using strategic information system planning, which is "the process of identifying which computer-based applications will assist an organization in executing its business plans and realizing its business goals" (Lederer & Sethi, 1988, p. 36). Following the planning phase, a portfolio of projects should be developed that aligns organizational information requirements with strategic objectives (Drake & Byrd, 2006).

The second function of IT portfolio management — the reassessment of ongoing projects and systems to determine whether they are achieving their objectives within established time or budgetary constraints — requires comprehensive examination at the portfolio level (Drake & Byrd, 2006). In this regard, Drake and Byrd emphasize that, "As the size and complexity of IT departments increase, so does the size and complexity of the projects they undertake. It takes a portfolio-level analysis to determine the progress and relevance of these projects" (2006, p. 2).

In addition, a risk assessment is required in order to better understand how project portfolio management should proceed (Drake & Byrd, 2006). According to Drake and Byrd, "Risk is the measure of probability and magnitude of an unwanted event happening. In risk management, identification of risks helps managers prevent and/or mitigate the effects of those risks" (2006, p. 2). Practitioners must identify which unwanted events can adversely affect the outcome of portfolio projects. This step is essential in order to prevent or mitigate the adverse impact of risks and to increase portfolio health, which is defined as the extent to which the projects in the portfolio are satisfying business requirements (Drake & Byrd, 2006).

Over the years, researchers have conducted a wide range of studies to identify and quantify project risk factors and portfolio risk (Drake & Byrd, 2006). For instance, McFarlan (1981) examined various risk factors associated with identifying a risk profile of corporations. Likewise, Shoval and Giladi (1996) identified a number of portfolio-level risks concerning the order of implementation for information service projects. Similarly, Jiang and Klein (1999) examined the selection criteria used for various information service projects that were deemed important to the executive leadership team when confronted with a new project portfolio.

The purpose of a study by Drake and Byrd (2006) was to explore the relevant literature, develop a list of important risk factors faced by IT project portfolios, and prioritize them along an emergent continuum. Based on this list, Drake and Byrd developed a framework for identifying, measuring, and mitigating risks at the portfolio level. This approach is consistent with previous research concerning financial portfolio management for a number of reasons. For instance, a number of researchers have determined that projects are investments organizations make for their future in the same way that stocks are an investment in the future (Benko & McFarlan, 2003). This financial element of portfolio management is based in part on the Modern Portfolio Theory first propounded by Markowitz (1959), which includes among its key principles the following:

An optimal portfolio generates the highest possible return for a given level of risk. Expected risk has two sources: (1) investment risk — the risk of the stock itself (unsystematic), and (2) relationship risk — the risk derived from how a stock relates to the other stocks in a portfolio (systematic).

Generally speaking, the risk of an IT portfolio can be expected to resemble that of a financial portfolio because there is risk involved in individual projects and risk in how projects interrelate to each other; the term "relationship risk" is used to refer to risk that can impact the entire portfolio (Drake & Byrd, 2006, p. 2). According to Drake and Byrd, "These risks cannot be diversified away because the entire portfolio is affected by outside influences" (2006, p. 2). The analysis of relationship risk in project portfolios is typically more complicated than in financial portfolios, however, because projects can directly affect the outcomes of other projects in the portfolio (Drake & Byrd, 2006).

The difference in complexity between financial and project portfolios is especially salient in cases where projects rely on the completion of other projects before they can be implemented — for example, upgrading operating systems in order to support a new application (Drake & Byrd, 2006). In these cases, relationship risk operates in a highly unsystematic fashion that complicates ongoing evaluation because, unlike financial portfolios, each project may affect all of the others in a project portfolio (Drake & Byrd, 2006). Taken together, it is reasonable to suggest that there are three general areas of risk to consider when formulating the optimal project portfolio:

1. The risk of the projects themselves; 2. Risk from the relationships between projects; 3. Risk to the portfolio as a whole (Drake & Byrd, 2006, p. 2).

Beyond the foregoing, it is also imperative that all project objectives in the portfolio reflect an organization's high-level strategies (Project portfolio management, 2009). According to the editors of Research-Technology Management (2009), "Otherwise, the portfolio as a whole will suffer misalignment, thereby wasting time, energy, and resources" (Project portfolio management, p. 64). The next step in project portfolio management is implementation within the project portfolio, aligning R&D strategies with organizational strategies (Project portfolio management, 2009).

There are a number of tools and techniques available to facilitate this process. For instance, "Roadmapping is considered a powerful way to develop common language between different functions and for exploring possible future scenarios. Transferring the portfolio projects into the production and marketing environment is a further challenge" (Project portfolio management, 2009, p. 64). This is an important step because, as noted above, unlike financial portfolios, all projects in a portfolio stand to affect all other projects. In this regard, the editors of Research-Technology Management (2009) conclude that, "Roadmapping and knowledge management are considered to be effective tools in support of portfolio management, fostering organizational learning and the exploration and understanding of future scenarios. Roadmaps embed R&D projects in business projects instead of looking at 'independent' R&D projects" (Project portfolio management, 2009, p. 64).

The need for effective and efficient project portfolio management is also cited by Dinsmore and Cabanis-Brewin (2011), who emphasize that, "Executives without effective project portfolio management suffer from cross-functional resource conflicts with continual top management refereeing, poor or anemic organizational performance, and projects where the norm is to deliver late, over budget, or not within scope" (p. 315). Even when executives make the effort to plan their projects carefully, they may overlook the most important ones in their attempt to organize all of the enterprise's projects in a cohesive fashion. In this regard, Dinsmore and Cabanis-Brewin point out that, "Most executives are aware of the need for drastic change in multiproject management practices, but many place the emphasis in the wrong place. Unfortunately, a great deal of such investment is misdirected into multiyear efforts to implement software tools and time sheets before dealing with the highest leverage points" (2011, p. 315).

There are three discrete roles that organizations must formally define in order to develop an effective project portfolio management system:

1. Governance: This executive role involves decision making, usually conducted by top management teams. In the most effective implementations, this role includes "C"-level executives (CFO, CEO, COO, CIO) who meet monthly to make decisions about which projects to approve or reject; when to activate projects; how many projects to activate and which to deactivate; due dates for projects; criteria for project proposals; priorities; resource allocation, including capital expenditure, personnel, and operating expense budgets; project reviews with approval to proceed to the next stage, to terminate a project, or to approve or reject project improvement plans; and investment in project management methodology and tools.

2. Management: Relative to project portfolio management, management's role is to ensure that the project management system is "in control." According to the late quality expert W. Edwards Deming, a system is in control when the goals of the system can be predictably met more than 95 percent of the time. Every project has three distinct goals — to be delivered on time, on budget, and within scope, according to original commitments. This role includes providing the project management processes for planning and execution to deliver projects according to their goals, usually through a Project Management Office or similar organization.

3. Project Portfolio Management: The person or persons undertaking this role provide information and recommendations to the governance group for improved return on investment and monitor the execution of projects. There is usually a close relationship between the individual responsible for strategic planning and the portfolio manager. While strategic planners identify the ideas necessary to meet organizational goals, the portfolio manager ensures that corresponding programs and projects are sufficient to accomplish those ideas, maps and tracks project execution against strategies, raises a flag when there is danger of missing a goal, and informs strategic planning when a strategy is not practical relative to available project resources (Dinsmore & Cabanis-Brewin, 2011, p. 315).

References

Benko, C. & McFarlan, F. W. (2003). Connecting the dots: Aligning projects with objectives in unpredictable times. Boston: Harvard Business School Press.

Butler, R. (2005, Fall). Ageism: Looking back over my shoulder. Generations, 29(3), 84–89.

Crunk, J. & North, M. M. (2007, January 1). Decision support systems and artificial intelligence technologies in aid of information systems based marketing. International Management Review, 3(2), 61–69.

Dewitte, J. & Willey, J. (1995). WCNN '95, Washington, D.C.: World Congress on Neural Networks: 1995 International Neural Network Society Annual Meeting. Mahwah, NJ: Lawrence Erlbaum Associates.

Dinsmore, P. C. & Cabanis-Brewin, J. (2011). The AMA handbook of project management. New York: American Management Association.

Drake, J. R. & Byrd, T. A. (2006, January 1). Risk in information technology project portfolio management. Journal of Information Technology Theory and Application, 8(3), 1–5.

Foundations of fuzzy logic. (2014). MathWorks. Retrieved from

Fuzzy inference process. (2014). MathWorks. Retrieved from

Guan, J. & Zurada, J. (2008, December). An adaptive neuro-fuzzy inference system based approach to real estate property assessment. The Journal of Real Estate Research, 30(4), 395–403.

Guillaume, S. (2001, July). Designing fuzzy inference systems from data: An interpretability-oriented review. IEEE Transactions on Fuzzy Systems, 9(3), 426–443.

Hahn, U. & Ramscar, M. (2001). Similarity and categorization. New York: Oxford University Press.

Hamidzadeh, B. & Shekhar, S. (1999). Specification and analysis of real-time problem solvers. IEEE Transactions on Software Engineering.

Hamzeh, M., Fakhaire, S. M. & Lucas, C. (2007). Soft real-time fuzzy task scheduling for multiprocessor systems. International Journal of Intelligent Technology, 2(4), 211–216.

Hippolyte, J-L. (2006). Fuzzy fiction. Lincoln, NE: University of Nebraska Press.

Jiang, J. J. & Klein, G. (1999). Project selection criteria by strategic orientation. Information & Management, 36(2), 63–75.

Kyburg, H. E., Jr. & Choh, M. T. (2001). Uncertain inference. Cambridge, England: Cambridge University Press.

Lederer, A. & Sethi, V. (1996). Key prescriptions for strategic information systems planning. Journal of Management Information Systems, 13(1), 35–62.

Lee, J., Tiao, A. & Yen, J. (1994). A fuzzy rule-based approach to real-time scheduling. Fuzzy Systems: IEEE World Congress on Computational.

McFarlan, F. W. (1981). Portfolio approach to information systems. Harvard Business Review, 59(5), 142–150.

Malhotra, R. & Malhotra, D. K. (1999, July–August). Fuzzy systems and neuro-computing in credit approval. The Journal of Lending & Credit Risk Management, 81(11), 24–29.

Markowitz, H. M. (1959). Portfolio selection: Efficient diversification of investments. Oxford: Basil Blackwell Ltd.

Muller, M. J., Farrell, R., Cebulka, K. D. & Smith, J. G. (1992). Issues in the usability of time-varying multimedia. In M. Blattner & R. Danenberg (Eds.), Interactive multimedia computing. Hillsdale, NJ: Lawrence Erlbaum Associates.

Nahavandi, S., Solomon, P., North, P. & Zealand, N. (1995). Application of fuzzy logic to shop floor scheduling. Proceedings of the Second New Zealand International Two.

Peterson, I. (2009, July 24). Examining fuzzy logic's widespread success. Science News, 144(4), 55.

Pitts, G. N. (2011). A fuzzy logic inference engine for simulation-based ITS. San Antonio: Trinity University Department of Computer Science.

Project and portfolio management. (2014). Hewlett-Packard. Retrieved from

Project portfolio management. (2009, March–April). Research-Technology Management, 46(2), 63–65.

Pryor, R. & Bright, J. (2007, Summer). The chaos theory of careers: Development, application, and possibilities. Career Planning and Adult Development Journal, 23(2), 49–55.

Qi, F. & Zhu, A-X. (2008, October). Knowledge discovery from area-class resource maps: Capturing prototype effects. Cartography and Geographic Information Science, 35(4), 223–230.

Sabeghi, M., Naghibzadeh, M. & Taghavi, T. (2006). Scheduling non-preemptive periodic tasks in soft real-time systems using fuzzy inference. Proceedings of the Ninth IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing.

Sharma, D. K. & Peer, S. K. (2012, January 1). Multi-factor user interface components layout problem with genetic algorithm. Academy of Information and Management Sciences Journal, 15(1), 73–79.

Shoval, P. & Giladi, R. (1996). Determination of an implementation order for IS projects. Information & Management, 31(2), 67–74.

Sun, R. & Alexandre, F. (1997). Connectionist-symbolic integration: From unified to hybrid approaches. Mahwah, NJ: Lawrence Erlbaum Associates.

Talaq, J. & Al-Basri, F. (1999, February). Adaptive fuzzy gain scheduling for load frequency control. IEEE Transactions on Power Systems, 14(1), 145–150.

Wang, L-X. (1996). A course in fuzzy systems and control. New York: Prentice Hall.

Yazdani-Chamzini, A. & Yakhchali, S. (2012, November). Forecasting gold price changes by using adaptive network fuzzy inference system. Journal of Business Economics and Management, 13(5), 994.