DSS help executives make better decisions by using historical and current data from internal Information Systems and external sources. By combining massive amounts of data with sophisticated analytical models and tools, and by making the system easy to use, they provide a much better source of information to use in the decision-making process.
Decision Support Systems (DSS) are a class of computerized information systems that support 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 successfully complete decision process tasks.
DSS and MIS
In order to better understand a decision support system, let's compare the characteristics of an MIS system with those of a DSS system:
MIS: Structured decisions
DSS: Semistructured, unstuctured decisions
MIS: Reports based on routine flows of data
DSS: Focused on specific decisions / classes of decisions
MIS: General control of organization
DSS: End-user control of data, tools, and sessions
MIS: Structured information flows
DSS: Emphasizes change, flexibility, quick responses
MIS: Presentation in form of reports
DSS: Presentation in form of graphics
DSS: Greater emphasis on models, assumptions, ad hoc queries
MIS: Traditional systems development
DSS: Develop through prototyping; iterative process
Framework of Decisions Support Systems
A conceptual framework for Decision Support Systems (DSS) is developed based on the dominant technology component or driver of decision support, the targeted users, the specific purpose of the system and the primary deployment technology. Five generic categories based on the dominant technology component are proposed, including Communications-Driven, Data-Driven, Document-Driven, Knowledge-Driven, and Model-Driven Decision Support Systems. Each generic DSS can be targeted to internal or external stakeholders. DSS can have specific or very general purposes. Finally, the DSS deployment technology may be a mainframe computer, a client/server LAN, or a Web-Based architecture. The goal in proposing this expanded DSS framework is to help people understand how to integrate, evaluate and select appropriate means for supporting and informing decision-makers.
Because of the limitations of hardware and software, early DSS systems provided executives only limited help. With the increased power of computer hardware, and the sophisticated software available today, DSS can crunch lots more data, in less time, in greater detail, with easy to use interfaces. The more detailed data and information executives have to work with, the better their decisions can be.
Types of DSS
Data-Driven DSS
Data-Driven DSS take the massive amounts of data available through the company's TPS and MIS systems and cull from it useful information which executives can use to make more informed decisions. They don't have to have a theory or model but can "free-flow" the data.
The first generic type of Decision Support System is a Data-Driven DSS. These systems include file drawer and management reporting systems, data warehousing and analysis systems, Executive Information Systems (EIS) and Spatial Decision Support Systems. Business Intelligence Systems are also examples of Data-Driven DSS. Data- Driven DSS emphasize access to and manipulation of large databases of structured data and especially a time-series of internal company data and sometimes external data. Simple file systems accessed by query and retrieval tools provide the most elementary level of functionality. Data warehouse systems that allow the manipulation of data by computerized tools tailored to a specific task and setting or by more general tools and operators provide additional functionality. Data-Driven DSS with Online Analytical Processing (OLAP) provide the highest level of functionality and decision support that is linked to analysis of large collections of historical data.
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 access to and manipulation of a model. Simple statistical and analytical 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 databases are usually not needed for Model-Driven DSS.
Model-Driven DSS were isolated from the main Information Systems of the organization and were primarily used for the typical "what-if" analysis. That is, "What if we increase production of our products and decrease the shipment time?" These systems rely heavily on models to help executives understand the impact of their decisions on the organization, its suppliers, and its customers.
Knowledge-Driven DSS
The terminology for this third generic type of DSS is still evolving. Currently, the best term seems to be Knowledge- Driven DSS. Adding the modifier "driven" to the word knowledge maintains a parallelism in the framework and focuses on the dominant knowledge base component. Knowledge-Driven DSS can suggest or recommend actions to managers. These DSS are personcomputer systems with specialized problem-solving expertise. The "expertise" consists of knowledge about a particular domain, understanding of problems within that domain, and "skill" at solving some of these problems. A related concept is Data Mining. It 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.
Document-Driven DSS
A new type of DSS, a Document-Driven DSS or Knowledge Management System, is evolving to help managers retrieve and manage unstructured documents and 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 databases including databases of hypertext documents, images, sounds and video. Examples of documents that would be accessed by a Document-Based DSS are policies and procedures, product specifications, catalogs, and corporate historical documents, including minutes of meetings, corporate records, and important correspondence. A search engine is a powerful decisionaiding tool associated with a Document-Driven DSS.
Communications-Driven and Group DSS
Group Decision Support Systems (GDSS) came first, but now a broader category of Communications-Driven DSS or groupware can be identified. This fifth generic type of Decision Support System includes communication, collaboration and decision support technologies that do not fit within those DSS types identified. Therefore, we need to identify these systems as a specific category of DSS. A Group DSS is a hybrid Decision Support System that emphasizes both the use of communications and decision models. A Group Decision Support System is an interactive computer-based system intended to facilitate the solution of problems by decision-makers working together as a group. Groupware supports electronic communication, scheduling, document sharing, and other group productivity and decision support enhancing activities We have a number of technologies and capabilities in this category in the framework – Group DSS, two-way interactive video, White Boards, Bulletin Boards, and Email.
Inter-Organizational or Intra-Organizational DSS
A relatively new targeted user group for DSS made possible by new technologies and the rapid growth of the Internet is customers and suppliers. We can call DSS targeted for external users an Inter-organizational DSS. 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 a Data-Driven DSS available to suppliers or a Model-Driven DSS available to customers to design a product or choose a product. Most DSS are Intra-Organizational DSS that are designed for use by individuals in a company as "standalone DSS" or for use by a group of managers in a company as a Group or Enterprise-Wide DSS.
Function-Specific or General Purpose DSS
Many DSS are designed to support specific business functions or types of businesses and industries. We can call such a Decision Support System a function-specific or industry- specific DSS. A Function-Specific DSS like a budgeting system may be purchased from a vendor or customized in-house using a more general-purpose development package. Vendor developed or "off-the-shelf" DSS support functional areas of a business like marketing or finance; some DSS products are designed to support decision tasks in a specific industry like a crew scheduling DSS for an airline. A task-specific DSS has an important purpose in solving a routine or recurring decision task. Function or task-specific DSS can be further classified and understood in terms of the dominant DSS component, that is as a Model-Driven, Data-Driven or Suggestion DSS. A function or task-specific DSS holds and derives knowledge relevant for a decision about some function that an organization performs (e.g., a marketing function or a production function). This type of DSS is categorized by purpose; function-specific DSS help a person or group accomplish a specific decision task. General-purpose DSS software helps support broad tasks like project management, decision analysis, or business planning.
Components of DSS
Traditionally, academics and MIS staffs have discussed building Decision Support Systems in terms of four major components:
• The user interface
• The database
• The models and analytical tools and
• The DSS architecture and network
This traditional list of components remains useful because it identifies similarities and differences between categories or types of DSS. The DSS framework is primarily based on the different emphases placed on DSS components when systems are actually constructed.
Data-Driven, Document-Driven and Knowledge-Driven DSS need specialized database components.
A Model- Driven DSS may use a simple flat-file database with fewer than 1,000 records, but the model component is very important. Experience and some empirical evidence indicate that design and implementation issues vary for Data-Driven, Document-Driven, Model-Driven and Knowledge-Driven DSS.
Multi-participant systems like Group and Inter- Organizational DSS also create complex implementation issues. For instance, when implementing a Data-Driven DSS a designer should be especially concerned about the user's interest in applying the DSS in unanticipated or novel situations. Despite the significant differences created by the specific task and scope of a DSS, all Decision Support Systems have similar technical components and share a common purpose, supporting decision- making.
A Data-Driven DSS database is a collection of current and historical structured data from a number of sources that have been organized for easy access and analysis.
We are expanding the data component to include unstructured documents in Document-Driven DSS and "knowledge" in the form of rules or frames in Knowledge-Driven DSS. Supporting management decision-making means that computerized tools are used to make sense of the structured data or documents in a database.
Mathematical and analytical models are the major component of a Model-Driven DSS. Each Model-Driven DSS has a specific set of purposes and hence different models are needed and used. Choosing appropriate models is a key design issue. Also, the software used for creating specific models needs to manage needed data and the user interface. In Model-Driven DSS the values of key variables or parameters are changed, often repeatedly, to reflect potential changes in supply, production, the economy, sales, the marketplace, costs, and/or other environmental and internal factors. Information from the models is then analyzed and evaluated by the decision-maker.
Knowledge-Driven DSS use special models for processing rules or identifying relationships in data. The DSS architecture and networking design component refers to how hardware is organized, how software and data are distributed in the system, and how components of the system are integrated and connected. A major issue today is whether DSS should be available using a Web browser on a company intranet and also available on the Global Internet. Networking is the key driver of Communications- Driven DSS.
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The DSS software system must be easy to use and adaptable to the needs of each executive. A well-built DSS uses the models that the text describes. You've probably used statistical models in other classes to determine the mean, median, or deviations of data. These statistical models are the basis of datamining.
The What-If decisions most commonly made by executives use sensitivity analysis to help them predict what effect their decisions will have on the organization. Executives don't make decisions based solely on intuition. The more information they have, the more they experiment with different outcomes in a safe mode, the better their decisions. That's the benefit of the models used in the software tools.
Examples of DSS Applications
Organization - DSS Application
1. American Airlines - Price and route selection
2. Equico Capital Corporation - Investment evaluation
3. General Accident Insurance - Customer buying patterns and fraud detection
4. Bank of America - Customer profiles
5. Frito-Lay, Inc. - Price, advertising, and promotion selection
6. Burlington Coat Factory - Store location and inventory mix
7. National Gypsum - Corporate planning and forecasting
8. Southern Railway - Train dispatching and routing
9. Texas Oil and Gas Corporation- Evaluation of potential drilling sites
10. United Airlines - Flight scheduling
11. U.S. Department of Defense - Defense contract analysis
Web-Based DSS
Of course, no discussion would be complete without information about how companies are using the Internet and the Web in the customer DSS decision-making process. The following figure shows an Internet CDSS (Customer Decision-Support System).
Here's an example: You decide to purchase a new home and use the Web to search real estate sites. You find the perfect house in a good neighborhood but it seems a little pricey. You don't know the down payment you'll need. You also need to find out how much your monthly payments will be based on the interest rate you can get. Luckily the real estate Web site has several helpful calculators (customer decision support systems) you can use to determine the down payment, current interest rates available, and the monthly payment. Some customer decision support systems will even provide an amortization schedule. You can make your decision about the purchase of the home or know instantly that you need to find another house.
POINTS TO PONDER:
Decision Support Systems
•an information system
•purpose to provide information for making informed decisions
•interactive (needed for experimenting and prospecting)
Definitions of DSS
•Management Decision Systems --Interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems.
•Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semi-structured problems.
Basic Themes of DSS
•Information systems.
•Used by managers.
•Used in making decisions.
•Used to support, not to replace people.
•Used when the decision is "semistructured" or "unstructured."
•Incorporate a database of some sort.
•Incorporate models.
DSS Benefits
•Improving Personal Efficiency
•Expediting Problem Solving
•Facilitating Interpersonal Communications
•Promoting Learning or Training
•Increasing Organizational Control
DSS SystemDSS
- Man-Machine System: DSS is man-machine system for decision making purposes. Man part is more open and probabilistic while the machine part is more closed and deterministic. E.g. DSS for deciding PRICE and ADVERTISING levels
- Closed-loop system with feedback external to system: DSS uses feedback to adjust output. Feedback is not internal like anelevator. The user provides judgmental inputs to DSS.
- DSS components: Database, model base, knowledge base, interface which interact with each other and the user.
The DSS Hierarchy
•Suggestion systems
•Optimization systems
•Representational models
•Accounting models
•Analysis information systems
•Data analysis systems
•File drawer systems
