Decision Support Systems 141
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Decision support systems (DSS) are a diverse group of interactive computer tools—primarily customizable software —designed to assist managerial decision making. They fall into a broader class known as management support systems (MSSs). The goal of a DSS is to make management more efficient and effective, particularly with ad hoc and discretionary decisions (versus routine or programmatic ones that require little judgment). Interactivity is key; unlike related expert systems and many artificial intelligence tools (see Figure 1), DSS generally do not attempt to make the decision themselves, but rather present information in a manner that is conducive to making an informed and efficient decision.


DSS were introduced in the 1970s and gained mainstream attention in the 1980s. Originally run largely on mainframes, they were seen as an evolutionary step from management information systems, which at the time were relatively inflexible storehouses of corporate data. In that environment, DSS were high-end applications reserved for occasional, non-recurring strategic decisions by senior management.

Since then, the rapid advances in personal computers ushered in a new breed of simple and widely used DSS. Indeed, some experts consider the built-in

Figure 1 Decision support Systems Versus Other Management Tools
Figure 1
Decision support Systems Versus Other Management Tools
analytic functions in popular spreadsheet programs, such as Microsoft Excel and Lotus 1-2-3, to be mini-DSS. As a result, many DSS today are simple, informal PC software tools that users create themselves with the help of templates, macros, user-programmed modules, and other customizable features.

While a simple DSS for an individual may cost a couple hundred dollars and some programming time, sophisticated ones continue to be significant business investments. At their inception they were exceptionally expensive to develop, and thus only large companies could afford them. Although relative prices have come down, they still tend to cost anywhere from $30,000 to $500,000 or more to implement company-wide. Premium systems are offered by such firms as IBM, SAS Institute, SPSS, and a host of more specialized vendors.


There are three basic components in a DSS:

Depending on the system, each of these components may be very simple or highly elaborate. The database, or in advanced systems, a database management system (DBMS) or a data warehouse, consists of structured, real-life information, such as customer account records, product sales history, employee schedules, or manufacturing process statistics. The model base, or model base management system (MBMS), contains one or more models for the kind of analysis the system will perform. For example, if the purpose of the system is to supply sales projections under different conditions, one model might be a linear regression formula derived from past sales and other factors. The user interface integrates the two into a coherent system and provides the decision maker with controls for—and possibly feedback about—managing the data and the models.


In order to discuss the support of decisions and what these tools can or should do, it is necessary to have a perspective on the nature of the decision process and thus what it means to support it. One way of looking at a decision is in terms of three areas or components. The first component is the data collected by a decision maker to be used in making the decision. The second component is the process selected by the decision maker to combine this data. Finally, there is an evaluation or learning component that compares decisions and examines them to see if there is a need to change either the data being used or the process that combines the data. These components of a decision interact with the characteristics of the decision being made. One approach to categorizing decisions is to consider the degree of structure in the decision-making activity.



A structured decision is one in which all three components can be fairly well specified, i.e., the data, process, and evaluation are determined. Usually structured decisions are made regularly and therefore it makes sense to place a comparatively rigid framework around the decision and the people making it. An example of this type of decision may be the routine credit-granting decision made by many businesses. It is probably the case that most firms collect rather similar sets of data for credit granting decision makers to use. In addition the way in which the data is combined is likely to be consistent (for instance, household debt must be less than 25 percent of gross income). Finally, this decision can also be evaluated in a very structured way (specifically when the marginal cost of relaxing credit requirements equals the marginal revenue obtained from additional sales). For structured decisions it is possible and desirable to develop computer programs that collect and combine the data, thus giving the process a high degree of consistency. However, because these tend to be routine and predictable choices, a DSS is typically not needed for highly structured decisions. Instead, there are any number of automated tools that can make the decision based on the predefined criteria.


At the other end of the continuum are unstructured decisions. These decisions have the same components as structured ones; however, there is little agreement on their nature. For instance, with these types of decisions, each decision maker may use different data and processes to reach a conclusion. In addition, because of the nature of the decision there may also be few people that are even qualified to evaluate the decision. These types of decisions are generally the domain of experts in a given field. This is why firms hire consulting engineers to assist their decision-making activities in these areas. To support unstructured decisions requires an appreciation of individual approaches, and it may not be terribly beneficial to expend a great deal of effort to support them.

Generally, unstructured decisions are not made regularly or are made in situations in which the environment is not well understood. New product decisions may fit into this category for either of these reasons. To support a decision like this requires a system that begins by focusing on the individual or team that will make the decision. These decision makers are usually entrusted with decisions that are unstructured because of their experience or expertise, and therefore it is their individual ability that is of value. One approach to support systems in this area is to construct a program that simulates the process used by a particular individual. These have been called "expert systems." It is probably not the case that an expert decision maker would be replaced by such a system, although it may offer support in terms of providing another perspective of the decision. Another approach is to monitor and document the process that was used so that the decision maker(s) can readily review what has already been examined and concluded. An even more novel approach used to support these decisions is to provide environments that are specially designed to give these decision makers an atmosphere that is conducive to their particular tastes, a task well suited for a DSS. The key to support of unstructured decisions is to understand the role that individual experience or expertise plays in the decision and to allow for individual approaches.


In the middle of the continuum are semi-structured decisions, and this is where most of what are considered to be true decision support systems are focused. Decisions of this type are characterized as having some agreement on the data, process, and/or evaluation to be used, but there is still a desire not to place too much structure on the decision and to let some human judgment be used. An initial step in analyzing which support system is required is to understand where the limitations of the decision maker may be manifested, i.e., will it be in the data acquisition portion, or in the process component, or possibly in the evaluation of outcomes. For instance, suppose an insurance executive is trying to decide whether to offer a new type of product to existing policyholders that will focus on families with two or more children that will be ready to attend college in six to nine years. The support required for this decision is essentially data oriented. The information required can be expressed in terms of the following query on the insurance company's database: "Give me a list of all of our policyholders that have a college education and have more than two children between ages 10 and 12."


A major role of DSS is simple information processing; the program makes a large array of facts and considerations more digestible. They also automate tasks at which humans tend to be slow and inaccurate, such as sorting and mathematical calculations.


For instance, the insurance executive who wanted to offer the new product now has to decide on a price for the product. In order to make this decision, the effect of different variables (including price) on demand for the product and the subsequent profit must be evaluated. The executive's perceptions of the demand for the product can be captured in a mathematical formula that portrays the relationship between profit, price, and other variables considered important. Once the relationships have been expressed, the decision maker may now want to change the values for different variables and see what the effect on profits would be. The ability to save mathematical relationships and then obtain results for different values is a feature of many decision support systems. This is called "what-if' analysis and is a common application for DSS to automate.


Of course, the output from such a system is only as good as the model or data being used; if the demand model is inaccurate or outdated or based on dissimilar products, the outcome projections may be worthless. Thus, decision makers must be aware of the risk of potential inaccuracies and understand the underlying logic behind a DSS's output, as opposed to accepting its output blindly, in order to make an informed decision. The object of a good DSS is to obtain useful information for human consideration rather than to let the computer make the decision itself. Advanced DSS may contain safeguards and pointers to help users avoid misinterpreting output or creating meaningless output.


Systems such as the Statistical Navigator go through a dialogue with the user to determine what the data's characteristics are and what questions are actually being asked of the data. Then the system suggests what techniques are most appropriate to use. This approach to supporting decision makers requires that the DSS possess a great deal more than database or processing capabilities—it should actually have an understanding of the domain in which the system is being used. The Statistical Navigator has knowledge of statistical methods and the benefits, assumptions, and problems associated with each method. A future step would be a system that has an understanding of more processing options than just statistical methods. This might include linear programming or present value analysis. As DSS start to include many different processing models in the library of choices, two possibilities exist.

One possibility is that the system will merely allow users to choose different methods within the same overall DSS. In this instance the user must still supply the knowledge of what is the most appropriate method and must be able to interpret the results. Another possibility is similar to the approach used in the Statistical Navigator, which would be to include a knowledge of the methods in the DSS and let it help the user select among many methods, not just statistical. Of course each approach does have its problems. There are software packages that allow users to select among different methods, but they do not offer a great deal of guidance on their use. Thus, as is the case with certain statistical analysis packages, the conclusions may not be correct because the method was applied incorrectly.

The second possibility presents a very different problem, or perhaps challenge; that is, how much knowledge to build into the DSS. A single system with general knowledge of most processing methods would be very popular with most users. However, designers would be confronted with the problem of what to include in a support system or what decision activities it should support. Should it have simple knowledge of the processing methods, such as linear programming, statistical regression, and present value? Or should it have knowledge about decision areas, such as cash budgeting, locating a new plant, or pricing policies?

This second approach may keep inappropriate data from being used, but then the questions about the role of the decision maker and how structured decisions may become must be addressed. Is the decision maker merely an information provider to a DSS that performs many functions, or should the role of the DSS be simply to make whatever analysis is desired as easy as possible. As software develops more and more capabilities, designers and users of decision support systems will have to answer the question of what it actually means to support a decision.


Although all DSS are designed to tackle fairly specific types of problems, there are a number of recognized subcategories of DSS. Among them group decision support systems (GDSS) and executive information systems (EIS). At times these can be hard to distinguish from a "conventional" DSS, but both continue to enjoy solid backing in corporations and the separate terminology persists.


As the name implies, GDSS are used to assist groups of decision makers who have common or overlapping responsibilities, such as executive committees, task forces, and work teams. Some of these tools are designed to be used directly when the group is convened. One example is tallying and processing group member preferences, and then presenting output for the participants to discuss. In other cases the group may never meet, but a centralized system is available to each member for common tasks they perform, such as financial monitoring and reporting.


EISs are suites of data analysis tools that are meant to be applied to a company's most critical financial and performance data. In large organizations, usually this means the EIS has the ability to pull and manipulate data—increasingly in real time instead of waiting days or weeks for the most recent data—on multiple corporate systems. EISs enjoyed a resurgence in the 1990s in part because of widespread management interest in activity-based costing, data warehousing, and enterprise resource planning systems. Software advances have also made EISs less costly and more powerful. Many of the latest systems are run on client/server technology using a Web browser.

SEE ALSO : Artificial Intelligence ; Decision Tree ; Expert Systems


Bidgoli, Hossein. Intelligent Management Support Systems. Westport, CT: Quorum Books, 1998.

Poe, Vidette, Patricia Klauer, and Stephen Brobst. Building a Data Warehouse for Decision Support. Upper Saddle River, NJ: Prentice Hall, 1998.

Sauter, Vicki L. Decision Support Systems: An Applied Managerial Approach. New York: John Wiley & Sons, 1997.

Turban, Efriam, and Jay E. Aronson. Decision Support Systems and Intelligent Systems. Upper Saddle River, NJ: Prentice Hall, 1998.

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Nov 19, 2008 @ 10:10 am
it is very useful for DSS readers,i need much more information about this artical.

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