Information for Decision Making - Assignment Example

Information for Decision Making Atta ur Rehman Academia Research 12 March 2008 Introduction For most businesses, there are a variety of requirements for information. Senior managers need information to help with their business planning. Middle management needs more detailed information to help them monitor and control business activities. Employees with operational roles need information to help them carry out their duties. As a result, businesses tend to have several information systems operating at the same time. Generally information systems are classified in the following classes. 1) Executive Support Systems 2) Management Information Systems 3) Decision-Support Systems 4) Knowledge Management Systems 5) Transaction Processing Systems 6) Office Automation Systems In the following paragraphs, Decision Support System is discussed. Decision Support System Decision-support systems (DSS) serve the management level of the organization. DSS help managers make decisions that are unique, rapidly changing, and not easily specified in advance. They address problems where the procedure for arriving at a solution may not be fully predefined in advance. Although DSS use internal information from TPS and MIS, they often bring in information from external sources, such as current stock prices or product prices of competitors. Clearly, by design, DSS have more analytical power than other systems. They are built explicitly with a variety of models to analyze data, or they condense large amounts of data into a form where they can be analyzed by decision makers. DSS are designed so that users can work with them directly; these systems explicitly include user-friendly software. DSS are interactive; the user can change assumptions, ask new questions, and include new data. Definition Decision Support Systems (DSS) are a specific class of computerized information system that supports business and organizational decision-making activities. A properly designed DSS is an interactive software-based system intended to help decision makers by compiling useful information from raw data, documents, personal knowledge, and business models to identify and solve problems and make decisions. A decision is a choice between alternatives based on estimates of the values of those alternatives. The concept of Decision Support Systems is very broad because there are many approaches of decision-making and a wide range of domains in which decisions are made. Thus, we can say that a Decision Support Systems is a computerized system for helping make decisions. Supporting a decision means helping people working alone or in a group gathers intelligence, generate alternatives and make choices. This process involves supporting the estimation, the evaluation and the comparison of alternatives (Alter, S. L. 1980). Components of a Decision Support System Building of Decision Support Systems has been conventionally argued by the practitioners and academics in terms of following four major components: 1) The user interface 2) The database 3) The models and analytical tools 4) The DSS architecture and network (Sprague and Carlson, 1982). It is very useful because it identifies similarities and differences between categories or types of DSS and it can help managers and analysts to build new DSS. According to Sprague (1980), Opening the large DSS box reveals a database, a model base, and a complex software system for linking the user to each of them (p. 14). He further argued: A decision support system is comprised of three sets of capabilities: database management software (DBMS), model base management software (MBMS), and the software for managing the interface between the user and the system, which might be called the dialogue generation and management software (DGMS). These three major subsystems provide a convenient scheme for identifying the technical capability which a DSS must have (p. 14). Characteristics of DSS Sprague and Carlson (1982), conclude that Decision Support Systems (DSS) are a specific class of computerized information system that supports decision-making activities. DSS are interactive computer-based systems and subsystems intended to help decision makers use communications technologies, data, documents, knowledge and/or models to identify and solve problems and make decisions. Five more specific DSS types include: Communications-driven DSS, Data-driven DSS, Document-driven DSS, Knowledge-driven DSS, and Model-driven DSS. Alter (1980) identified three major characteristics of DSS: 1) DSS are designed specifically to facilitate decision processes, 2) DSS should support rather than automate decision making, and 3) DSS should be able to respond quickly to the changing needs of decision makers. Users of Decision Support System In the framework provided by Sprague and Watson (1993), Sprague suggests that there are five roles involved in a typical Decision Support System development cycle. 1) The end user. 2) An intermediary. 3) DSS developer 4) Technical supporter 5) Systems Expert How Do Decision Support Systems Add Value? Decision Support Systems add the worth of a decision in the following ways. 1) It improves the personal efficiency. 2) It is less time consuming as it has the characteristics of expediting problem solving. 3) It adds the value by facilitating interpersonal communication 4) It emphasizes on promoting learning and training. 5) Decision Support Systems increasing the organizational control. Strategic Role of Decision Support Systems Decision Support Systems play a strategic role in the life of the firm because they can directly affect how managers decide? How senior managers plan, influence and control? What products and services are produced? Decision Support Systems change the goals, operations, products, services and environmental relationships of organizations to help them in gaining an edge over competitors. They can be used to support strategy at the business, firm, and industry level. At the business level of strategy, Decision Support Systems can be used to help firms become the low-cost producer, differentiate products, or serve new markets. The system can also be used to lock in customers and suppliers using efficient customer response and supply chain management applications. At organizational level, Decision Support Systems achieve new efficiencies or enhance the services by tying together the operations of disparate business units so that they can function as a whole or by promoting the sharing of knowledge across business units. Further, the systems can promote competitive advantage by facilitating cooperation with other firms in the industry, creating consortiums or communities for sharing information, exchanging transactions, or coordinating activities. For many reasons, managers need to understand the upside benefits and the downside risks of building specific DSS. Decision Support Systems can solve problems and create new problems. Managers need to know enough to make intelligent and informed DSS design, development and implementation choices to achieve the strategic objectives of the organization (Power, D., 2000). Business-Level Strategy At the business level of strategy, the key question is, “How can we compete effectively in this particular market?” The market might be light bulbs, utility vehicles, or cable television. The most common generic strategies at this level are: (1). to become the low-cost producer, (2). to differentiate your product or service, and (3). to change the scope of competition by either enlarging the market to include global markets or narrowing the market by focusing on small niches not well served by competitors. Digital firms provide new capabilities for supporting business-level strategy by managing the supply chain, building efficient customer “sense and respond” systems, and participating in “value webs” to deliver new products and services to market. Organizational Decision Support Systems An organizational Decision Support Systems defined as a DSS that is used by individuals or groups at several work stations in more than one organizational unit who make varied interrelated but autonomous decisions using a common set of tools (Carter et al. 1992: 19). According to the same source, an important goal of organizational Decision Support Systems is to provide the glue that holds a large organization together and keeps its parts marching to the beat of the same drummer toward common goals. The two key factors to achieving these outcomes are: 1. Transmittal of consistent, timely information up and down the organizational hierarchy in forms that is appropriate to each decision maker. 2. A set of decision-aiding models that use this information and that are appropriate for the decisions, being made by each decision maker. Decision Support System Trends: A Descriptive Approach At the turn of the year, it is common to think about trends and future developments related to many topics. The pace of technology change is speeding up and the expectations for DSS in companies continue to expand and grow. Chuck Russell posted a question on the DSSResources.COM Bulletin Board about trends that may be driving companies to do more modeling in support of decisions. Many of those trends deal with managing risk and improving quantitative analyses, but improved technologies should also drive companies to do more modeling. Recently, Alexandre Gachet updated me on his research on distributed cooperative DSS using Java based on the Jini technology. Hoda Hafez also wrote recently asking about knowledge management technologies. So, what seem to be the trends associated with information technology that may have a major impact on the design and development of DSS? What are the DSS technology trends? Portals, hand-helds, mobile computing, and visualization tools are driving Decision Support updates and innovations. Object-oriented development tools, especially Java, seem to hold much promise for building new model-driven DSS. Distributed and cooperative DSS can both be built using such tools. Network technologies are mission-critical in most companies. Computing and network technologies have become more integrated and more powerful. The speed and capacity of networks is increasing. Bandwidth expansion can support interactive video and real-time decision support anywhere in the world. Open source software may impact some DSS development tools. With access to source code programmers can read, redistribute, and modify the software; advocates of open source software feel the software will evolve faster. For example, Linux is becoming an important operating system for Web servers in corporations. Visualization technologies are more powerful than at any time in the past. New software helps users visualize almost anything they can imagine in a realistic, manipulatable format. Visualization tools can help pilots simulate flights and help managers "try" new products. The World Wide Web supports e-business including internal and external global communications, decision making, and collaboration for managers. Also, the Web can help managers gather, manage, share, and use information. The Web is the platform for managing documents and "knowledge". Handheld computing is gaining greater acceptance, and the use of pocket PCs integrated with cell phones by managers and other employees will increase. Wireless Web devices are expected to outnumber wired devices in the next few years. Pocket PCs support distributed access to data-driven DSS, expanded communications-driven DSS and distributed decision making. Mobile computing systems extend the reach of an enterprise-wide DSS. Large data storage systems and multiprocessing computers have removed the constraints on what can be stored and how much data can be stored. Data storage is faster and it is easier to organize and backup data of all types. Data can be kept forever in a form that can be sorted, analyzed and processed (Power, 2004). Main Trends and Challenges in the Development of DSS: A Practical Perspective The main trend seems to be vendor consolidation and the main challenge remains innovation. The older Business Intelligence and Modeling and Analysis vendors seem to be ready for a wave of mergers and other joint activities. Innovation is still possible in this product space and the example of Web Decision portals suggests that more innovation is both possible and likely. There is certainly a need for new products in decision support systems (Power, 2001). Descriptive Approach The above research has been done by descriptive approach. A descriptive research, also known as statistical research, describes data and characteristics about the population or phenomenon being studied. Descriptive research answers the questions who, what, where, when and how. Although the data description is factual, accurate and systematic, the research cannot describe what caused a situation. Thus, descriptive research cannot be used to create a causal relationship, where one variable affects another. In other words, descriptive research can be said to have a low requirement for internal validity. The description is used for frequencies, averages and other statistical calculations. Often the best approach, prior to writing descriptive research, is to conduct a survey investigation. Qualitative research often has the aim of description and researchers may follow-up with examinations of why the observations exist and what the implications of the findings are. References Alter, S. L. (1980). Decision Support Systems: current practice and continuing challenges. Reading, Mass., Addison-Wesley Pub. Carter, G.M., Murray, M.P., Walker, R.G. and Walker, W.E. (1992) Building Organizational Decision Support. Finlay, P. N. (1994). Introducing Decision Support Systems. Oxford, UK Cambridge, Mass., NCC Blackwell; Blackwell Publishers. Power, D. J. (2002). Decision Support Systems: Concepts and Resources for Managers, Westport, CT: Greenwood/Quorum Books. Power, D. (August 28, 2000). What Do Managers Need To Know About DSS? DSS News, Vol. 1, No. 8. Power, D. (November 18, 2001). What Are The Achievements, Trends And Challenges For Decision Support Systems (DSS)? DSS News, Vol. 2, No. 24. Power, D. (July 31, 2005). Can DSS provide firms with a sustainable competitive advantage? DSS News, Vol. 6, No. 17. Sharda, R., S. Barr, and J. McDonnell. (1988). Decision Support Systems Effectiveness: A Review and an Empirical Test, Management Science, vol. 34, no. 2, pp. 139-159. Sprague, R, H., Jr. (Dec. 1980). A Framework for the Development of Decision Support Systems. Management Information Systems Quarterly, vol. 4, no. 4, pp. 1-26. Sprague, R. H. and E. D. Carlson. (1982). Building Effective Decision Support Systems Englewood Cliffs, N.J.: Prentice-Hall, Inc. Turban, E. (1995). Decision Support and Expert Systems: Management Support Systems. Englewood Cliffs, N.J., Prentice Hall. Read More