DECISION SUPPORT SYSTEMS



Decision Support Systems 401
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Broadly speaking, decision support systems are a set of manual or computer-based tools that assist in some decision-making activity. In today's business environment, however, decision support systems (DSS) are commonly understood to be computerized management information systems designed to help business owners, executives, and managers resolve complicated business problems and/or questions. Good decision support systems can help business people perform a wide variety of functions, including cash flow analysis, concept ranking, multistage fore-casting, product performance improvement, and resource allocation analysis. Previously regarded as primarily a tool for big companies, DSS has in recent years come to be recognized as a potentially valuable tool for small business enterprises as well.

THE STRUCTURE OF DECISIONS

In order to discuss the support of decisions and what DSS tools can or should do, it is necessary to have a perspective on the nature of the decision process and the various requirements of supporting it. One way of looking at a decision is in terms of its key 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 that is being made.

STRUCTURED DECISIONS Many analysts categorize decisions according to the degree of structure involved in the decision-making activity. Business analysts describe a structured decision as one in which all three components of a decision—the data, process, and evaluation—are determined. Since structured decisions are made on a regular basis in business environments, it makes sense to place a comparatively rigid framework around the decision and the people making it.

Structured decision support systems may simply use a checklist or form to ensure that all necessary data is collected and that the decision making process is not skewed by the absence of necessary data. If the choice is also to support the procedural or process component of the decision, then it is quite possible to develop a program either as part of the checklist or form. In fact, it is also possible and desirable to develop computer programs that collect and combine the data, thus giving the process a high degree of consistency or structure. When there is a desire to make a decision more structured, the support system for that decision is designed to ensure consistency. Many firms that hire individuals without a great deal of experience provide them with detailed guidelines on their decision making activities and support them by giving them little flexibility. One interesting consequence of making a decision more structured is that the liability for inappropriate decisions is shifted from individual decision makers to the larger company or organization.

UNSTRUCTURED DECISIONS At the other end of the continuum are unstructured decisions. While these decisions have the same components as structured ones—data, process, and evaluation—there is little agreement on their nature. With unstructured decisions, for example, each decision maker may use different data and processes to reach a conclusion. In addition, because of the nature of the decision there may only a limited number of people within the organization that are even qualified to evaluate the decision.

Generally, unstructured decisions are made in instances in which all elements of the business environment—customer expectations, competitor response, cost of securing raw materials, etc.—are not completely understood (new product and marketing strategy decisions commonly fit into this category). Unstructured decision systems typically focus 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. In essence, these systems—commonly referred to as "expert systems"—prompt the user with a series of questions regarding a decision situation. "Once the expert system has sufficient information about the decision scenario, it uses an inference engine which draws upon a data base of expertise in this decision area to provide the manager with the best possible alternative for the problem," explained Jatinder N.D. Gupta and Thomas M. Harris in the Journal of Systems Management. " The purported advantage of this decision aid is that it allows the manager the use of the collective knowledge of experts in this decision realm. Some of the current DSS applications have included long-range and strategic planning policy setting, new product planning, market planning, cash flow management, operational planning and budgeting, and portfolio management."

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. The key to support of unstructured decisions is to understand the role that individuals experience or expertise plays in the decision and to allow for individual approaches.

SEMI-STRUCTURED DECISIONS 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 are also typified by efforts to retain some level of human judgement in the decision making process. 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., the data acquisition portion, the process component, or the evaluation of outcomes).

Grappling with the latter two types of decisions—unstructured and semi-structured—can be particularly problematic for small businesses, which often have limited technological or work force resources. As Gupta and Harris indicated, "many decision situations faced by executives in small business are one-of-a-kind, one-shot occurrences requiring specifically tailored solution approaches without the benefit of any previously available rules or procedures. This unstructured or semi-structured nature of these decisions situations aggravates the problem of limited resources and staff expertise available to a small business executive to analyze important decisions appropriately. Faced with this difficulty, an executive in a small business must seek tools and techniques that do not demand too much of his time and resources and are useful to make his life easier." Subsequently, small businesses have increasingly turned to DSS to provide them with assistance in business guidance and management.

KEY DSS FUNCTIONS

Gupta and Harris observed that DSS is predicated on the effective performance of three functions: information management, data quantification, and model manipulation: "Information management refers to the storage, retrieval, and reporting of information in a structured format convenient to the user. Data quantification is the process by which large amounts of information are condensed and analytically manipulated into a few core indicators that extract the essence of data. Model manipulation refers to the construction and resolution of various scenarios to answer 'what if' questions. It includes the processes of model formulation, alternatives generation and solution of the proposed models, often through the use of several operations research/management science approaches."

Entrepreneurs and owners of established enterprises are urged to make certain that their business needs a DSS before buying the various computer systems and software necessary to create one. Some small businesses, of course, have no need of a DSS. The owner of a car washing establishment, for instance, would be highly unlikely to make such an investment. But for those business owners who are guiding a complex operation, a decision support system can be a valuable tool. Another key consideration is whether the business's key personnel will ensure that the necessary time and effort is spent to incorporate DSS into the establishment's operations. After all, even the best decision support system is of little use if the business does not possess the training and knowledge necessary to use it effectively. If, after careful study of questions of DSS utility, the small business owner decides that DSS can help his or her company, the necessary investment can be made, and the key managers of the business can begin the process of developing their own DSS applications using available spreadsheet software.

DSS UNCERTAINTIES AND LIMITATIONS

While decision support systems have been embraced by small business operators in a wide range of industries in recent years, entrepreneurs, programmers, and business consultants all agree that such systems are not perfect.

LEVEL OF "USER-FRIENDLINESS" Some observers contend that although decision support systems have become much more user-friendly in recent years, it remains an issue, especially for small business operations that do not have significant resources in terms of technological knowledge.

HARD-TO-QUANTIFY FACTORS Another limitation that decision makers confront has to do with combining or processing the information that they obtain. In many cases these limitations are due to the number of mathematical calculations required. For instance, a manufacturer pondering the introduction of a new product can not do so without first deciding 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 manufacturer'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 today's spreadsheet software packages are fully equipped to support this decision-making activity. Of course, additional factors must be taken into consideration as well when making business decisions. Hard-to-quantify factors such as future interest rates, new legislation, and hunches about product shelf life may all be considered. So even though the calculations may indicate that a certain demand for the product will be achieved at a certain price, the decision maker must use his or her judgment in making the final decision.

If the decision maker simply follows the output of a process model, then the decision is being moved toward the structured end of the continuum. In certain corporate environments, it may be easier for the decision maker to follow the prescriptions of the DSS; users of support systems are usually aware of the risks associated with certain choices. If decision makers feel that there is more risk associated with exercising judgment and opposing the suggestion of the DSS than there is in simply supporting the process, the DSS is moving the decision more toward the structured end of the spectrum. Therefore, the way in which a DSS will be used must be considered within the decision-making environment.

PROCESSING MODEL LIMITATIONS Another problem with the use of support systems that perform calculations is that the user/decision maker may not be fully aware of the limitations or assumptions of the particular processing model. There may be instances in which the decision maker has an idea of the knowledge that is desired, but not necessarily the best way to get that knowledge. This problem may be seen in the use of statistical analysis to support a decision. Most statistical packages provide a variety of tests and will perform them on whatever data is presented, regardless of whether or not it is appropriate. This problem has been recognized by designers of support systems and has resulted in the development of DSS that support the choice of the type of analysis.

FURTHER READING:

Carlson, John R., Dawn S. Carlson, and Lori L. Wadsworth. "On the Relationship Between DSS Design Characteristics and Ethical Decision Making." Journal of Managerial Issues. Summer 1999.

Chaudhry, Sohail S., Linda Salchenberger, and Mahdi Beheshtian. "A Small Business Inventory DSS: Design, Development, and Implementation Issues." Computers & Operations Research. January 1996.

Gupta, Jatinder N.D., and Thomas M. Harris. "Decision Support Systems for Small Business." Journal of Systems Management. February 1989.

Kimball, Ralph, and Kevin Strahlo. "Why Decision Support Fails and How to Fix It." Datamation. June 1, 1994.

Kumar, Ram L. "Understanding DSS Value." Omega. June 1999.

Laudon, Kenneth C., and Jane Price Laudon. Management Information Systems: A Contemporary Perspective. New York: Macmillan, 1991.

Muller-Boling, Detlef, and Susanne Kirchhoff. "Expert Systems for Decision Support in Business Start-Ups." Journal of Small Business Management. April 1991.

Parkinson, Chris. "What If? Decision Shaping Systems." CMA—The Management Accounting Magazine. March 1995.

Raggad, Bel G. "Decision Support System: Use It or Skip It." Industrial Management and Data Systems. January 1997.

Raymond, Louis, and Francois Bergeron. "Personal DSS Success in Small Enterprises." Information and Management. May 1992.



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