Research Paper, 8 pages (2000 words)

Mis: decision support systems

Decision Support Systems


Beginning in the late 1970s, many vendors, practitioners, and academics promoted the development of computer based Decision Support Systems (DSS). Their actions created high expectations for DSS and generated much optimism about the prospects for improving decision making. Despite the building and excitement, the success rate of decision support applications has been unsatisfactory. Although the computing industry has transformed how business transactions and data are processed, managers have frequently been disappointed by attempts to use computer and informationtechnologyto support decision making (cf, Drucker, 1998). Recently, because of technological developments, managers have become more enthusiastic about implementing innovative decision support projects. This attitude change is a positive development, but both managers and Management Information System (MIS) practitioners need to discuss and review their expectations about Decision Support Systems before beginning new projects.

According to Sprague and Carlson (1982, pg. 9), “DSS comprise a class of information system that draws on transaction processing systems and interacts with the other parts of the overall information system to support the decision-making activities of managers and other knowledge workers in organizations”. Decision Support Systems are defined broadly as interactive computer based systems that help people use computer communications, data, documents, knowledge and models to solve problems and make decisions. DSS are ancillary or auxiliary systems; they are not intended to replace skilled decision makers.

Decision Support Systems should be considered when two assumptions seem reasonable: first, good information is likely to improve decision making; and second managers need and want computerized decision support. Anecdotes and research show that some computer-based DSS can provide managers with analytical capabilities and information that improve decision making.

In pursuing the goal of improving decision making, many different types of computerized DSS have been built to help decision teams and individual decision makers. Some systems provide structured information directly to managers. Other systems can help managers and staff specialist analyze situations using carious types of models. Some DSS store knowledge and make it available to managers. Some systems support decision making by small and large groups. Companies even develop DSS to support the decision making of their customer and suppliers.

Today, e-business technologies are transforming business transactions, and similar technologies can transform and improve decision activities. This report will also discuss how computing, the World Wide Web and information technologies can support and improve business and managerial decision making.

A brief history of Decision Support Systems

Prior to 1965, it was very expensive to build large scale information systems. At about this time, the development of the IBM system 360 and other more powerful mainframe systems made it more practical and cost effective to develop Management Information Systems (MIS) in large companies. MIS focused on providing managers with structured, periodic reports. Much of the information was from accounting and transaction systems.

In the late, 1960s, a new type of information system became practical model-oriented DSS or management decision systems. Two DSS pioneers, Peter Keen and Carles Stabell, claim the concept of decision support evolved from “the theoretical studies of organizational decision support making done at the Carnegie Institute of Technology during the late 1950s and early 60s and the technical work on interactive computer systems, mainly carried out at the Massachusetts Institute of Technology in the 1960s” (Keen and Scott Morton, 1978, preface).

Management Information Needs

Managers and their support staff need to consider what information and analyses are actually needed to support their management and business activities. Some managers need both detailed transaction data and summarized transaction data. Most managers only want summaries of transactions. Managers usually want lots of charts and graphs; a few only want tables of numbers. Many managers want information provided routinely or periodically and some want information available on-line and on demand. Managers usually want financial analyses and some managers want primarily “Soft” non-financial or qualitative information.

In general, an information system can provide business transaction information, and it can help managers understand many business operations and performance issues. For example, a computerized system can help managers understand the status of operations, monitor business results, review customer preference data, and investigate competitor actions. In all of these situations, management information and analyses should have a number of characteristics. Information must be both timely and current. These characteristics mean the information is up-to-date and available when manages want it. Also, management information must be accurate, relevant and complete. Finally, managers want information presented in a format that assists them in making decisions. In general, management information should be summarized and concise, and any support system should have an option for managers to obtain more detailed information.

In summary, DSS must provide current, timely information and analyses that are accurate, relevant and complete. A specific DSS must present information in an appropriate format that is easy to understand and manipulate. The information presented by a DSS may result from analysis of transaction data, it may be the result of a decision model, or it may have been gathered from external sources. DSS can present internal and external facts, informed opinions and forecasts to managers.

DSS versus MIS

Is a DSS an MIS? How does a Decision Support System differ from a Management Information System? One can begin drawing distinctions between these two terms by first examining the concepts management information system (MIS) and information system (IS). Many authors have used the term “MIS” to describe a broad, general category of information systems. Also, MIS and IS are used interchangeably to describe a functional department in companies and organizations responsible for managing information systems and technology.  In 1970s, and MIS generated periodic management reports. Today, managers use data-driven DSS to meet their management reporting needs. When the term “Management Information System” is defined narrowly, it refers to a management report system that provides periodic structured, paper based reports. In contrast data-driven DSS are intended to be interactive, real time systems that are responsible to unplanned, as well as planned, information requests and reporting needs. Model-driven DSS are usually focused on modeling a specific decision or a set of related decision (cf., Power, 1997).

DSS include a wide variety of analytical information systems. DSS provide managers more control of their data, access to analytical tools, and capabilities for consulting and interacting with a distributed group of staff. An enterprise-wide DSS is linked to a large data warehouse and serves many managers within one company. Also, a DSS is defined as an interactive system in a networkedenvironmentthat helps a targeted group of managers make decisions. The primary focus in the following discussion is on various types of DSS. The term MIS will be used sparingly and will usually refer broadly to an information system that provides managers with on-line access to information.

Types of DSS

Data-Driven DSS

The first category of DSS, data-driven DSS, emphasizes analysis of large amounts of structured data. These systems include file drawer and management reporting systems, data warehousing and analytical systems, Executive Information Systems, and Spatial DSS (SDSS). EIS are targeted to senior managers, and SDSS display spatial data for decision support. Business Intelligence (BI) systems are also examples of data-driven DSS.

Model-Driven DSS

A second category, model driven DSS, includes systems that use accounting and financial models, representational models, and optimization models. Model driven DSS emphasize tools provide the most elementary level of functionality. Some OLAP systems that allow complex analysis of data may be classified as hybrid DSS systems providing modeling, data retrieval, and data summarization functionality. Model-driven DSS use data and parameters provided by decision makers to aid them in analyzing a situation, but they are not usually data intensive. Very large database are usually not needed for model-driven DSS, but data for a specific analysis may need to be extracted from a large database.

Knowledge-Driven DSS

The terminology for this category of DSS is still evolving. Currently, the best term seems to be “Knowledge-Driven” DSS. Sometimes it seems equally appropriate to use Alters’ term “Suggestion DSS” or the narrower term “Management Expert System.” knowledge-driven DSS suggest or recommend actions to managers. They use business rules and knowledge bases. These DSS are person-computer systems with specialized problem solving expertise. The “expertise” consists of knowledge about a particular domain understanding of problems within that domain and “skills” at solving some of these problems. A related concept is “Data mining.” This term refers to a class of analytical applications that search for hidden patterns in a database. Data mining is the process of sifting through large amounts of data to produce data content relationships. Tools used for building these systems are also called Intelligent Decision Support methods (cf., Dhar and Stein, 1997). Data mining tools can be used to create hybrid data-driven and knowledge driven DSS.

Document-Driven DSS

A new type of DSS, a document-driven DSS is evolving to help managers gather, retrieve, classify and manager unstructured documents, including Web pages. A document-driven DSS integrates a variety of storage and processing technologies to provide complete document retrieval and analysis. The web provides access to large document database including databases of hypertext documents, images, sounds, and video. Examples of documents that would be accessed by a document driven DSS are policies and procedures, product specification, catalogs and corporate historical documents. Including minutes of meetings, corporate records and important correspondence. A search engine is a powerful decision-aiding tool associated with a document driven DSS (cf., Fedorowicz, 1993). Some authors call this type of system a Knowledge Management System.

Communications Driven and Group DSS

Group Decision Support Systems (GDSS) and groupware came first, but now a broader category of communications-driven DSS can be identified. This type of DSS includescommunication, collaboration, and decision support technologies that do not fit within those DSS types identified by Alter. Therefore, communications-driven DSS need to be identified as a specific category of DSS. It seems appropriate to call these systems communications driven DSS even through many people are more familiar with the term GDSS. A GDSS is best viewed as a hybrid DSS that emphasizes both the use of communications technologies and decision process models.

Inter-organizational or Intra-organizational DSS

A relatively new category of DSS made possible by new technologies and the rapid growth of the public internet is inter-organizational DSS. These DSS serve a company’s customers or suppliers. The public Internet is creating communication links for many types of inter-organizational systems, including DSS. An inter-organizational DSS provides stakeholders with access to a company’s intranet and authority or privileges to use specific DSS capabilities. Companies can make data-driven DSS available to suppliers or a model driven DSS available to customers to design a product or choose a product.


Some examples show the wide variety of DSS applications. Major airlines use DSS for many tasks including pricing and route selection. Many companies have DSS that sir in corporate planning and forecasting. Specialists often use these DSS that focus on financial and simulation models. DSS can help monitor costs and revenue and track department budgets. Also, investment evaluation and support systems are increasingly common. Frito-Lay has a DSS that aids in pricing, advertising, and promotion. Route salesmen use handheld computers to support decision-making activities. Many manufacturing companies use Manufacturing Resources Planning (MRP) software. This specific, operational-level DSS supports master production scheduling, purchasing, and material requirements planning. More recent MRP systems support “WHAT-IF” analysis and simulation capabilities. DSS support quality improvement and control decisions. Monsanto, FedEx, and most transportation companies use DSS for scheduling trucks, airplanes and ships. The Coast Guard uses a DSS for procurement decisions. Companies like Wal-Mart have large data warehouses and use data mining software. Business Intelligence and Knowledge Management Systems are increasingly common.


Michel Klein, Leif B. Methlie, Wiley, 2007, Knowledge-based Decision Support Systems: With Applications in Business

Peter G. W. Keen, Michael S. Scott Morton, Addison-Wesley, 2007, Decision Support Systems: An Organizational Perspective

Ralph H. Sprague, Eric D. Carlson, Prentice-Hall, 2006, Building Effective Decision Support Systems

Steven Alter, Addison-Wesley, 2004, Decision Support Systems: Current Practice and Continuing Challenges

Daniel J. Power, Greenwood, 2002, Decision Support Systems: Concepts and Resources for Managers

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