Decision Support Systems Encyclopediacom


Decision support systems (DSS) are computer information systems that perform complex data analysis in order to help users make informed decisions. In general, a DSS retrieves information from a large data warehouse, analyzes it in accordance with user specifications, then publishes the results in a format that users can readily understand and use. DSS applications are interactive, and they are valuable in a wide range of business settings.

According to ComputerWorld Magazine, the rise of the decision support system can be traced to the late 1960s when businesses started to use computer mainframes. These mainframe computers first enabled businesses to interactively query data so they could enhance their previously-static reports. Other experts generally agree that computerized decision support systems became practical with the development of minicomputers, timeshare operating systems, and distributed computing.

In the 1970s, however, decision support systems experienced a huge boom. Query systems, what-if spreadsheets, and rules-based software were developed. The advent of packaged algorithms made it easier to get better, faster decisions. As technology evolved, new computerized decision support applications were developed and studied. Researchers used multiple frameworks to help build and understand these systems. Today, one can organize the history of DSS into the five broad DSS categories, including: communications-driven, data-driven, document driven, knowledge-driven, and model-driven decision support systems.

Trends in all these categories are emerging. Data-driven DSS continuously use faster, real-time access to larger, better integrated databases. Trends suggest that model-driven DSS will grow more complex. Systems built using simulations and accompanying visual displays are becoming increasingly realistic. Communications-driven DSS provide more real-time video communications support. Document-driven DSS access larger repositories of data; the systems present appropriate documents in more useable formats. Finally, knowledge-driven DSS are usually more sophisticated and comprehensive. The advice from knowledge-driven DSS is often considered better, and the applications cover broader domains.

Technology advances continue to make it easier and more efficient to collect relevant data. However, collecting, analyzing, correlating, and applying these massive amounts of data pose a challenge to businesses. Even so, companies are eager to respond in real-time to customer queries. They strive to anticipate customer needs, create opportunities, and avoid potential problems, for the end goal is to establish a predictive business.

The airline industry provides a good example of using data to instantaneously respond to customer queries. In the past, most customers called the airlines to purchase their airline tickets—a process that typically took about twenty minutes. That all changed with Web transactions, which can provide more information, more quickly. Ultimately, these types of DSS enable customers to book a ticket in just a few minutes.

With decision support systems, companies correlate information about their operations and performance with information about expected behavior and business rules. Decision makers anticipate and respond to threats and capitalize on opportunities before they occur. This ability makes predictive business, which is considered the next step in the evolution of a real-time enterprise, a reality.

Decision support systems were first tested in portfolio management, which poses one of the most essential problems in modern financial theory. It involves the construction of a portfolio of securities (stocks, bonds, treasury bills, etc.) that maximizes the investor’s utility. The process leading to the construction of such a portfolio consists of two major steps. In the first step, the decision-maker (investor, portfolio manager) has to evaluate the securities that are available as investment instruments. The vast number of available securities, especially in the case of stocks, makes this step necessary, in order to focus the analysis on a limited number of the best investment choices. Thus, on the basis of this evaluation stage, the decision-maker selects a small number of securities that constitute the best investment opportunities. In the second step of the process, the decision maker must decide on the amount of the available capital that should be invested in each security, thus constructing a portfolio of the selected securities. The portfolio should be constructed in accordance with the decision-maker’s investment policy and risk tolerance.

The portfolio theory assumes that the decision-maker’s judgment and investment policy can be represented by a utility function that is implicitly used by the decision-maker

in making his investment decisions. Thus, the maximization of this utility function will result in the construction of a portfolio that is as consistent as possible with the decision-maker’s expectations and investment policy. However, it is quite difficult to determine the specific form of this utility function.

The founder of portfolio theory, Harry Markowitz (recipient of the 1990 Nobel prize in economics), developed a framework according to which the decision-maker’s utility is a function of two variables: the expected return of the portfolio and its risk. Thus, he formulated the maximization of the decision-maker’s utility as a two-objective problem: maximizing the expected return of the portfolio and minimizing the corresponding risk. To consider the return and the risk, Markowitz used two well-known statistical measures, the mean of all possible returns to estimate the return of the portfolio, and the variance to measure its risk. On the basis of this mean-variance framework, Markowitz developed a mathematical framework to identify the efficient set of portfolios that maximizes returns at any given level of allowable risk. Given the risk aversion policy of the investor, it is possible to select the most appropriate portfolio from the efficient set.

This pioneering work of Markowitz motivated financial researchers to develop new portfolio management techniques, and significant contributions have been made over the last decades. The most significant of the approaches that have been proposed for portfolio management include the capital asset pricing model (CAPM), the arbitrage pricing theory (APT), single- and multi-index models, as well as several optimization techniques.

The concept of decision support systems (DSS) was introduced, from a theoretical point of view, in the late 1960s. DSS can be defined as computer information systems that provide information in a specific problem domain using analytical decision models and techniques, as well as access to databases, in order to support a decision maker in making decisions effectively in complex and ill-structured problems. Thus, the basic goal of DSS is to provide the necessary information to the decision-maker in order to help him or her get a better understanding of the decision environment and the alternatives available.

A typical structure of a DSS includes three main parts: the database, the model base, and the user interface. The database includes all the information and data that are necessary to perform the analysis on the decision problem at hand. Data entry, storage, and retrieval are performed through a database management system. The model base is an arsenal of methods, techniques, and models that can be used to perform the analysis and support the decision maker. These models or techniques are applied to the raw data in order to produce analysis or more meaningful output for the decision maker. A model base management system is responsible for performing all tasks that are related to model management, such as model development, updates, storage, and retrieval. Finally, the user interface is responsible for the communication between the user and the system, while it further serves as a link between the database and the model base. The appropriate design of the user interface is a key issue towards the successful implementation of the whole system, so as to ensure that the user can take full advantage of the analytical capabilities that the system provides. Advances in computer hardware and software have enabled user-friendly graphical user interfaces (GUIs) to serve this function.

During the last four decades, DSS have been developed and implemented to tackle a variety of real world decision-making problems, in addition to financial problems and portfolio management. The portfolio management process involves the analysis of a vast volume of information and data, including financial, stock market, and macroeconomic data. Analyzing a continuous flow of such a vast amount of information for every available security in order to make real time portfolio management decisions is clearly impossible without the support of a specifically designed computer system that will facilitate not only the data management process, but also the analysis.

Thus, the contribution of DSS to portfolio management becomes apparent. They provide an integrated tool to perform real-time analyses of portfolio-management-related data, and provide information according to the decision-maker’s preferences. Furthermore, they enable the decision maker to take full advantage of sophisticated analytic methods, including multivariate statistical and econometric techniques, powerful optimization methods, advanced preference modeling, and multiple-criteria decision-making techniques. DSS incorporating multiple-criteria decision-making methods in their structure are known as multicriteria DSS, and they have found several applications in the field of finance.

The Investor system is a DSS designed and developed to support the portfolio management process and to help construct portfolios of stocks. The system includes a combination of portfolio theory models, multivariate statistical methods, and multiple criteria decision-making techniques for stock evaluation and portfolio construction. The structure of the system is presented in Figure 1.

Financial Data. The database of the system includes four types of information and data. The first involves the financial statements of the firms whose stocks are considered in the portfolio management problem. The balance sheet and the income statement provide valuable information regarding the financial soundness of the firms (e.g., sales, net profit, net worth, liabilities, assets, etc.). The system contains such financial data spanning a five-year period, so that users can reach informed conclusions about the firms’ financial evolution.

Qualitative Information. In addition to these financial data, information on some qualitative factors is also inserted in the database. The management of the firms, their organization, their reputation in the market, their technical facilities, and their market position affect directly the operation and the performance of the firms; thus, they constitute fundamental factors in the analysis of the firms whose stocks are considered in the portfolio management problem.

Market Data. The third type of information included in the database involves the stocks’ market histories. This information involves the stock prices, the marketability of the stocks, their beta (b) coefficient (a measure of risk representing the relationship between the changes in the price of individual stocks with the changes in the market), the dividend yield, the price/earnings ratio, and so forth.

Macroeconomic Data. Finally, information regarding the macroeconomic environment is also included. Inflation, interest rates, exchange rates, and other macroeconomic variables have a direct impact on the performance of the stock market, thus potentially affecting any individual stock. The combination of this information with the financial and stock histories of individual firms enables portfolio managers to perform a global evaluation of the investment opportunities available, both in terms of their sensitivity and risk with respect to the economic environment, and to their individual characteristics.

Analysis Tools. The analysis of all this information is performed through the tools incorporated in the system’s model base. Two major components can be distinguished

in the model base. The first one consists of financial and stock market analysis tools. These can analyze the structure of the financial statements of the firms, calculate financial and stock market ratios, apply well-known portfolio theory models (e.g., the market model, the CAPM, the APT), and present several graphical summaries of the results obtained through these tools to facilitate drawing some initial conclusions about the stocks’ performance.

The second component of the model base involves more sophisticated analysis tools, including statistical and multiple-criteria decision-making techniques. More specifically, univariate statistical techniques are used to measure the stability of the beta coefficient of the stocks, while principal components analysis (a multivariate technique) is used to identify the most significant factors or criteria that describe the performance of the stocks, and to place the stocks into homogeneous groups according to their financial and stock market characteristics. The criteria identified as most crucial can be used to evaluate the stocks and thereby construct a portfolio that meets the investment policy of the investor/portfolio manager. Of course, the portfolio manager interacts with the system, and he or she can also introduce into the analysis the evaluation criteria that he or she considers important, even if these criteria are not found significant by principal components analysis.

The evaluation of the stocks’ performance is completed through multiple-criteria decision-making methods. Multiple-criteria decision-making is an advanced field of operations research that provides an arsenal of methodological tools and techniques to study real-world decision problems involving multiple criteria that often lead to conflicting results. The multiple-criteria decision-making methods that are incorporated in the model base of the investor system enable the investor to develop an additive utility function that is fairly consistent with his or her investment policy, preferences, and experience. On the basis of this additive utility function a score (global utility) is estimated for each stock that represents its overall performance with respect to the selected evaluation criteria. The scores of the stocks are used as an index so they may be placed into appropriate classes specified by the user. Thus, the portfolio manager can develop an evaluation model (additive utility function) to distinguish, for instance, among the stocks that constitute the best investment opportunities, the stocks that do not have a medium-long term prospect but they can be considered only for the short run, and the stocks that are too risky and should be avoided. Of course, any other classification can be determined according to the objectives and the policy of the portfolio manager.

On the basis of this classification, the investor/portfolio manager can select a limited number of stocks to include in the actual portfolio, which represent the best investment opportunities. Constructing the portfolio is accomplished through multiple-criteria decision-making

techniques that are appropriate for optimizing a set of objective functions subject to some constraints. The objective functions represent the investor/portfolio manager’s objectives on some evaluation criteria (return, beta, marketability, etc.), while constraints can be imposed to ensure that the constructed portfolio meets some basic aspects of the investment policy of the investor/portfolio manager.

For instance, the investor/portfolio manager can introduce constraints on the amount of capital invested in stocks of specific business sectors, the amount of capital invested in high-risk or low-risk stocks (high and low β coefficient, respectively), to determine a minimum level of return or a maximum level of risk, and so on. Once such details are determined, an interactive and iterative optimization procedure is performed that leads to the construction of a portfolio of stocks that meets the investor’s investment policy and preferences. The results presented through the screen of Figure 2 show the proportion of each stock in the constructed portfolio, the performance of the portfolio on the specified evaluation criteria (attained values), as well as the rate of closeness (achievement rate) of the performance of the portfolio as opposed to the optimal values on each evaluation criterion (the higher this rate is, the closer the performance of the portfolio to the optimal one for each criterion).

Since the development of the portfolio theory in the 1950s, portfolio management has gained increasing interest within the financial community. Periodic turmoil in stock markets worldwide demonstrates the necessity for developing risk management tools that can be used to analyze the vast volume of information that is available. The DSS framework provides such tools that enable investors and portfolio managers to employ sophisticated techniques from the fields of statistical analysis, econometric analysis, and operations research to make and implement real-time portfolio management decisions.

DSS research in the twenty-first century has been oriented toward combining the powerful analytical tools used in the DSS framework with the new modeling techniques provided by soft computing technology (neural networks, expert systems, fuzzy sets, etc.) to address the uncertainty, vagueness, and fuzziness that is often encountered in the financial and business environment.

Business intelligence (BI) practices are often cited as key to the evolution of decision support systems. BI refers to the technologies, applications, and practices used for collecting, integrating, analyzing, and presenting business information. It is the variety of software applications used to analyze an organization’s raw data and extract useful insights from it. Therefore, like DSS, business intelligence systems are data-driven. They use fact-based support systems to improve business decision-making, making BI a reporting and decision support tool.

Used at the operational level, BI projects have great potential to transform business processes. For example, well-known companies use BI technologies to improve corporate sales and customer service processes. Used correctly, BI systems can transform companies from regionally-operated businesses to unified global businesses.

Like many technological advances, there are obstacles. A key impediment to BI progress is lack of corporate understanding. Often, companies don’t know their own business processes well enough to determine how to improve them. Before commencing a BI project, companies must consider and understand all of the activities that make up a particular business process, how information and data flow across various processes, how data is passed between business users, and how people use it to execute their part of the process. In order to motivate upper management to standardize such processes company-wide, BI systems must have a direct impact on revenue.

Implementation of BI systems requires a change in thinking about the value of information inside organizations. Everyone involved in the BI process must have full access to information to be able to change the ways that they work. This necessitates a trusting working environment.

Well-known firm McKinsey Consulting noted that decision support systems were one of eight technology trends to watch in 2008. DSS technologies will advance as more innovative data collection and processing methods are introduced. This, according to McKinsey, will result in more granular segmentation and low-cost experimentation. The resulting information will help managers acquire more data, make smarter decisions, and develop competitive advantages and new business models.

SEE ALSO Competitive Intelligence; Computer-Aided Design and Manufacturing; Computer Networks; Management Information Systems; Strategic Planning Tools

Elton, E.J., and M.J. Gruber. Modern Portfolio Theory and Investment Analysis. 5th ed. New York: John Wiley and Sons, 1995.

Levinson, Meridith. “Business Intelligence: Not Just for Bosses Anymore.” CIO Magazine 15 Jan 2006. Available from: /article/16544/Business_Intelligence_Not_Just_for_Bosses_Anymore.

Lundberg, Alan. “Leverage Complex Event Processing to Improve Operational Performance.” Business Intelligence Journal 28 Mar 2006. Available from: /Publications/BIJournal/display.aspx?ID=7898.

Markowitz, H. Portfolio Selection: Efficient Diversification of Investments. New York: John Wiley and Sons, 1959.

Phillips-Wren, Gloria E., et al., “A Multiple-Criteria Framework for Evaluation of Decision Support Systems.” Omega August 2004.

Power, D.J. “A Brief History of Decision Support Systems.” 10 Mar 2007. Available from: /history/dsshistory.html,.

“Supply Chain News: McKinsey’s Eight Technology Trends to Watch for 2008” Supply Chain News 16 Jan 2008. Available from: /text_search.php?keyword=decision+support.

Turban, Efraim, et al. Decision Support Systems and Intelligent Systems. 7th ed. Englewood Cliffs, NJ: Prentice Hall, 2004.

Zopounidis, C., M. Godefroid, and C. Hurson. “Designing a Multicriteria DSS for Portfolio Selection and Management.” In Advances in Stochastic Modeling and Data Analysis. edited byJ. Janssen, C.H. Skiadas, and C. Zopounidis. Dordrecht, Netherlands: Kluwer Academic Publishers, 1995, 261–292.