Guideline for Creating Successful Decision Support Systems
Well successful DSS happens when its use actually makes a positive difference in decision making. These are most important factors to bring success to DSS using.
1. Execution time
Faster execution is not always better. Only a fraction of task time is determined by actual DSS execution time. Developers should select appropriate software and hardware that are fast enough to provide the result within control execution time. Well-designed user interface helps to reduce wasting time on the user’s part.
2. Versatility
DSS is used to perform tasks. Versatility is essential and must cover the full range of tasks for what a decision maker will want to use. Flexibility in adding new functions to DSS tools when scope of works extend is also important.
3. Quality of Help
Help functions or any online manual for decision maker when having problems are essential. Many development tools make it easy to incorporate online help into a system. However, help should be context sensitive. Developer must provide help support tailor as possible to any situation needs or expect the possible confusions or problems of user.
4. Adaptability
Personalizing of DSS system style of each user is another important thing. These features make easier, smarter, friendly and more tailor to each users used. Sometimes developers may create DSS in various modes. It may be “Standard mode” for starter users or “Advanced mode” for experienced users.
5. Uniformity of commands and interface
Same DSS commands compare to other systems or standard well-known commands require to be used in DSS is important thing. It’s easy to learn and remember when users switch frequently among them. A DSS developer should produce of what DSS users are familiar with and develop it like standard commands uniform.
6. Learning time
User-friendly and Easy-to-learn should be seem intuitive to targeted users. Saving learning time means to reduce costs of training and maintenance. Users are able to earn benefit from using DSS tools.
7. Ease of recall
Most DSS programs are not usually used every day. Manager often returns to a DSS after a long interval of nonuse. Consequently, recall ability is a more important factor. A user interface that facilitates recall will reduce the time it takes to "get back up to speed" with the DSS.
8. Errors
Sometimes, DSS users make some serious errors that lead to wrong decisions as a result of system misuse. Following after are database corrupted and computer crashed. It may waste the user’s time but no more bad effects to the system. Understanding the users' usual decision making process can help minimize errors.
9. Concentration required
Most people have difficulty keeping more than six or seven active facts in mind at one time. Label screens and output to reduce memory load is useful and make the program easier and smarter.
10. Fatigue
Usage frequency of DSS is usually low, therefore user physical fatigue is seldom a factor. However, mental fatigue can still occur. It can be minimized by asking for information once in systematic manner and by reducing the concentration required.
11. Fun
Frustrations can be minimized from using well-designed DSS system. More Fun, interacting, means "not aversive" and easy to use.
Design guidelines should be considered with these factors. Building an effective DSS is challenging for developers. Improving awareness of impact factors leads to better, and more effective DSS system.
Guideline for Creating Successful Decision Support Systems
Failures, Uncertainties and Limitations in Decision Support Systems
Failures, Uncertainties and Limitations in Decision Support Systems
Decision support system has been integrated into business for many years. All involving include entrepreneurs, programmers, and business consultants agree that such systems are not perfect. Decision support system has limitations as follow:
1. Technological knowledge of users is required
Although decision support systems have more user-friendly in recent years, it remains an issue, especially for small business firms that lack of technological knowledge of users. Most decision support systems still need technical term knowledge for the analysis.
2. Hard to Quantify Factors
In actual world, some values cannot be specific and some are hard to quantify factors such as future interest rates, new legislation or product shelf life that may all be considered while analyzing. Even though the decision support system may provide the certainly result, the decision maker must use their own judgment in making the final decision.
3. Hard to collect all of related data
At times, data are not recorded correctly or data without beware of errors or some data cannot be recorded. Some data must be evaluated in Analysis. Thus, the certain value from decision support tools may be different from what it should be.
4. Processing Model Limitations and Assumptions
As same as the processing analysis data in economics model, Decision makers may not be fully aware of the limitations or assumptions of the particular processing model. The assumptions and limitations are about “The situation MUST be under condition like this, the result should be…” but the situations cannot be controlled like assumptions and limitations of the decision support model.
Realize that we are under uncontrollable environment such as interest rates, political, disaster and more in actual world. Bringing the result from analysis to use in another condition without considering about uncontrollable factors or beware may cause incorrect decision making.
We must be aware of using result from decision support system as helper tools in making decisions. Realize of doing analysis under limitations and assumptions are important.
5. System design failures
Because of problems of each individual users are different, it’s a challenge of Decision support system developers to design program to support each person. Some decision makers don’t exactly know what they want and what they can obtain from decision support system or requirement may not well obtain.
Decision support system may be designed and not match to exactly what decision makers want. When it’s being used, the result from system may not be what decision maker want and information getting may not be sufficient to make any decision for decision maker.
6. Organization Resistance
Any new technology change will cause resistance from some users or stakeholders. Some people may fear of learning how to use new system or lost of status or influences in organization. Sometimes developer may have adequately received corporation by users in organization or no intention in using DSS system of users.
Outcome system may not be what users want. Benefit from using DSS at any issue may not be as much as expected.
Disadvantages of Decision Support System
Decision Support System can create advantages for organizations and can have positive benefits, however building and using Decision Support System can create negative outcomes in some situations.
(1) Monetary cost. The decision support system requires investing in information system to collect data from many sources and analyze them to support the decision making. Some analysis for Decision Support System needs the advance of data analysis, statistics, econometrics and information system, so it is the high cost to hire the specialists to set up the system.
(2) Overemphasize decision making. Clearly the focus of those of us interested in computerized decision support is on decisions and decision making. Implementing Decision Support System may reinforce the rational perspective and overemphasize decision processes and decision making. It is important to educate managers about the broader context of decision making and the social, political and emotional factors that impact organizational success. It is especially important to continue examining when and under what circumstances Decision Support System should be built and used. We must continue asking if the decision situation is appropriate for using any type of Decision Support System and if a specific Decision Support System is or remains appropriate to use for making or informing a specific decision.
(3) Assumption of relevance. According to Wino grad and Flores (1986), "Once a computer system has been installed it is difficult to avoid the assumption that the things it can deal with are the most relevant things for the manager's concern." The danger is that once Decision Support System become common in organizations, that managers will use them inappropriately. There is limited evidence that this occurs. Again training is the only way to avoid this potential problem.
(4) Transfer of power. Building Decision Support System, especially knowledge-driven Decision Support System, may be perceived as transferring decision authority to a software program. This is more a concern with decision automation systems than with Decision Support System. We advocate building computerized decision support systems because we want to improve decision making while keeping a human decision maker in the "decision loop". In general, we value the "need for human discretion and innovation" in the decision making process.
(5) Unanticipated effects. Implementing decision support technologies may have unanticipated consequences. It is conceivable and it has been demonstrated that some Decision Support System reduce the skill needed to perform a decision task. Some Decision Support System overload decision makers with information and actually reduce decision making effectiveness. We are sure that other such unintended consequences have been documented. Nevertheless, most of the examples seem correctable, avoidable or subject to remedy if and when they occur.
(6) Obscuring responsibility. The computer does not make a "bad" decision, people do. Unfortunately some people may deflect personal responsibility to a Decision Support System. Managers need to be continually reminded that the computerized decision support system is an intermediary between the people who built the system and the people who use the system. The entire responsibility associated with making a decision using a Decision Support System resides with people who built and use the system.
(7) False belief in objectivity. Managers who use Decision Support System may or may not be more objective in their decision making. Computer software can encourage more rational action, but managers can also use decision support technologies to rationalize their actions. It is an overstatement to suggest that people using a Decision Support System are more objective and rational than managers who are not using computerized decision support.
(8) Status reduction. Some managers argue using a Decision Support System will diminish their status and force them to do clerical work. This perceptual problem can be a disadvantage of implementing a Decision Support System. Managers and IS staff who advocate building and using computerized decision support need to deal with any status issues that may arise. This perception may or should be less common now that computer usage is common and accepted in organizations.
(9) Information overload. Too much information is a major problem for people and many Decision Support System increase the information load. Although this can be a problem, Decision Support System can help managers organize and use information. Decision Support System can actually reduce and manage the information load of a user. Decision Support System developers need to try to measure the information load created by the system and Decision Support System users need to monitor their perceptions of how much information they are receiving. The increasing ubiquity of handheld, wireless computing devices may exacerbate this problem and disadvantage.
In conclusion, before firms will invest in the Decision Support System, they must compare the advantages and disadvantages of the decision support system to get valuable investment.
Advantages of Decision Support System
(1) Time savings. For all categories of decision support systems, research has demonstrated and substantiated reduced decision cycle time, increased employee productivity and more timely information for decision making. The time savings that have been documented from using computerized decision support are often substantial. Researchers, however, have not always demonstrated that decision quality remained the same or actually improved.
(2) Enhance effectiveness. A second category of advantage that has been widely discussed and examined is improved decision making effectiveness and better decisions. Decision quality and decision making effectiveness are however hard to document and measure. Most researches have examined soft measures like perceived decision quality rather than objective measures. Advocates of building data warehouses identify the possibility of more and better analysis that can improve decision making.
(3) Improve interpersonal communication. DSS can improve communication and collaboration among decision makers. In appropriate circumstances, communications- driven and group DSS have had this impact. Model-driven DSS provides a means for sharing facts and assumptions. Data-driven DSS make "one version of the truth" about company operations available to managers and hence can encourage fact-based decision making. Improved data accessibility is often a major motivation for building a data-driven DSS. This advantage has not been adequately demonstrated for most types of DSS.
(4) Competitive advantage. Vendors frequently cite this advantage for business intelligence systems, performance management systems, and web-based DSS. Although it is possible to gain a competitive advantage from computerized decision support, this is not a likely outcome. Vendors routinely sell the same product to competitors and even help with the installation. Organizations are most likely to gain this advantage from novel, high risk, enterprise-wide, inward facing decision support systems. Measuring this is and will continue to be difficult.
(5) Cost reduction. Some researches and especially case studies have documented DSS cost saving from labor savings in making decisions and from lower infrastructure or technology costs. This is not always a goal of building DSS.
(6) Increase decision maker satisfaction. The novelty of using computers has and may continue to confound analysis of this outcome. DSS may reduce frustrations of decision makers, create perceptions that better information is being used and/or creates perceptions that the individual is a "better" decision maker. Satisfaction is a complex measure and researchers often measure satisfaction with the DSS rather than satisfaction with using a DSS in decision making. Some studies have compared satisfaction with and without computerized decision aids. Those studies suggest the complexity and "love/hate" tension of using computers for decision support.
(7) Promote learning. Learning can occur as a by-product of initial and ongoing use of a DSS. Two types of learning seem to occur: learning of new concepts and the development of a better factual understanding of the business and decision making environment. Some DSS serve as "de facto" training tools for new employees. This potential advantage has not been adequately examined.
(8) Increase organizational control. Data-driven DSS often make business transaction data available for performance monitoring and ad hoc querying. Such systems can enhance management understanding of business operations and managers perceive that this is useful. What is not always evident is the financial benefit from increasingly detailed data.
Regulations like Sarbanes-Oxley often dictate reporting requirements and hence heavily influence the control information that is made available to managers. On a more ominous note, some DSS provide summary data about decisions made, usage of the systems, and recommendations of the system. Managers need to be very careful about how decision-related information is collected and then used for organizational control purposes. If employees feel threatened or spied upon when using a DSS, the benefits of the DSS can be reduced. More research is needed on these questions.
Components of Decision Support Systems
Decision support systems vary greatly in application and complexity, but they all share specific features. A typical Decision support systems has four components: data management, model management, knowledge management and user interface management.
4.1 Data Management Component
The data management component performs the function of storing and maintaining the information that you want your Decision Support System to use. The data management component, therefore, consists of both the Decision Support System information and the Decision Support System database management system. The information you use in your Decision Support System comes from one or more of three sources:
-Organizational information; you may want to use virtually any information available in the organization for your Decision Support System. What you use, of course, depends on what you need and whether it is available. You can design your Decision Support System to access this information directly from your company’s database and data warehouse. However, specific information is often copied to the Decision Support System database to save time in searching through the organization’s database and data warehouses.
-External information: some decisions require input from external sources of information. Various branches of federal government, Dow Jones, Compustat data, and the internet, to mention just a few, can provide additional information for the use with a Decision Support System.
-Personal information: you can incorporate your own insights and experience your personal information into your Decision Support System. You can design your Decision Support System so that you enter this personal information only as needed, or you can keep the information in a personal database that is accessible by the Decision Support System.
4.2 Model Management Component
The model management component consists of both the Decision Support System models and the Decision Support System model management system. A model is a representation of some event, fact, or situation. As it is not always practical, or wise, to experiment with reality, people build models and use them for experimentation. Models can take various forms.
Businesses use models to represent variables and their relationships. For example, you would use a statistical model called analysis of variance to determine whether newspaper, TV, and billboard advertizing are equally effective in increasing sales.
Decision Support Systems help in various decision-making situations by utilizing models that allow you to analyze information in many different ways. The models you use in a Decision Support System depend on the decision you are making and, consequently, the kind of analysis you require. For example, you would use what-if analysis to see what effect the change of one or more variables will have on other variables, or optimization to find the most profitable solution given operating restrictions and limited resources. Spreadsheet software such as excel can be used as a Decision Support System for what-if analysis.
The model management system stores and maintains the Decision Support System’s models. Its function of managing models is similar to that of a database management system. The model management component can not select the best model for you to use for a particular problem that requires your expertise but it can help you create and manipulate models quickly and easily.
4.3 User Interface Management Component
The user interface management component allows you to communicate with the Decision Support System. It consists of the user interface management system. This is the component that allows you to combine your know-how with the storage and processing capabilities of the computer.
The user interface is the part of the system you see through it when enter information, commands, and models. This is the only component of the system with which you have direct contract. If you have a Decision Support System with a poorly designed user interface, if it is too rigid or too cumbersome to use, you simply won’t use it no matter what its capabilities. The best user interface uses your terminology and methods and is flexible, consistent, simple, and adaptable.
For an example of the components of a Decision Support System, let’s consider the Decision Support System that Land’s End has tens of millions of names in its customer database. It sells a wide range of women’s, men’s, and children’s clothing, as well various household wares. To match the right customer with the catalog, land’s end has identified 20 different specialty target markets. Customers in these target markets receive catalogs of merchandise that they are likely to buy, saving Lands’ End the expense of sending catalogs of all products to all 20 million customers. To predict customer demand, lands’ end needs to continuously monitor buying trends. And to meet that demand, lands’ end must accurately forecast sales levels. To accomplish theses goals, it uses a Decision Support System which performs three tasks:
-Data management: The Decision Support System stores customer and product information. In addition to this organizational information, Lands’ End also needs external information, such as demographic information and industry and style trend information.
-Model management: The Decision Support System has to have models to analyze the information. The models create new information that decision makers need to plan product lines and inventory levels. For example, Lands’ End uses a statistical model called regression analysis to determine trends in customer buying patterns and forecasting models to predict sales levels.
-User interface management: A user interface enables Lands’ End decision makers to access information and to specify the models they want to use to create the information they need.
4.4 Knowledge Management Component
The knowledge management component, like that in an expert system, provides information about the relationship among data that is too complex for a database to represent. It consists of rules that can constrain possible solution as well as alternative solutions and methods for evaluating them.
For example, when analyzing the impact of a price reduction, a Decision Support System should signal if the forecasted volume of activity exceeds the volume that the projected staff can service. Such signaling requires the Decision Support System to incorporate some rules-of-thumb about an appropriate ratio of staff to sales volume. Such rules-of-thumb, also known as heuristics, make up the knowledge base.
Architecture of Decision Support Systems
As shown in model, Decision support system consists of two major sub-systems: human decision makers and computer systems. Interpreting a DSS as only a computer hardware and software system is a common misconception. An unstructured (or semi-structured) decision by definition can not be programmed because its precise nature and structure are elusive and complex. The function of a human decision maker as a component of DSS is not to enter data to build a database, but to exercise judge or intuition throughout the entire decision-making process.
Imagine a manager who has to make a five-year production decision. The first step of the decision-making process begins with the creation of a decision support model, using an integrated DSS program such as Microsoft Excel, Lotus 1-2-3, Interactive Financial Planning Systems/Personal or Express/PC.
The user interface sub-system is the gateway to both database management systems and model-based management systems. DBMS area a set of computer programs that create and manage the database, as well as control access to the data stored within it.
The DBMS can be either an independent program or embedded within a DSS generator to allow users to create a database file that is to be used as an input to the DSS. MBMS is a set of computer programs embedded within a DSS generator that allows users to create, edit, update, and delete a model. User creates models and associated database files to make specific decisions.
The created models and database are stored in the model base and database in the direct access storage devices such as hard disks. From a user’s viewpoint, the user interface sub-system is the only part of DSS component with which they have to deal.
Therefore, providing an effective user interface must take several important issues into consideration, including choice of input and output devices, screen design, use of colors, data and information presentation format, using of different interface styles.
Today’s decision support system generator provides the user with a wide variety of interface modes: menu, based interaction mode, command language style, questions and answers, form interaction, natural language processing based dialogue, and graphical user interface (GUI). GUIs use icons, buttons, pull-down, menus, bars, and boxes extensively and have become the most widely implemented and versatile type. The interface system allows users to access to:
1. The data sub-system: (a) database (b) database management software;
2. The model sub-system: (a) model base (b) model base management software.
Types of systems
Decision Support Systems
Decision support systems are targeted systems that combine analytical for models with operational data and supportive interactive queries and analysis for middle managers who face semi-structured decision situations. Decision support systems support semi-structured and unstructured problem analysis and emphasizes change, flexibility, and a rapid response.
With a DSS (Decision Support Systems) there is less of an effort to link users to structured information flows and a correspondingly greater emphasis on models, assumptions, ad hoc queries, and display graphics.
Model-driven Decision Support Systems
Model-driven decision support systems emphasize access to and manipulation of a statistical, financial, optimization or simulation model. Online analytical processing (OLAP) systems that provide complex analysis of data can be classified as hybrid DSS systems providing both modeling and data retrieval and data summarization functionality.
Model-driven DSS use data and parameters provided by decision makers to aid decision makers in analyzing a situation, but they are not necessarily data intensive, that is very large data bases are not needed for many model-driven DSSs.
Data-driven Decision Support Systems
Data-driven DSS support decision making by enabling users to extract useful information that was previously buried in large quantities of data. Often data from transaction processing systems are collected in data warehouse for this purpose. Online analytical processing (OLAP) and data mining can then be used to analyze the data. Companies are starting to build data-driven DSS to mine customer data gathered from their Web sites as well as data from enterprise systems.
Executive Support Systems
Executive support systems are computer-based systems that provide top managers with the capability to attain easy access to internal and external information which is relevant to strategic decision making and other executive responsibilities. The terms of “executive support systems” and “executive information systems” are often used interchangeably, although executive support systems typically refer to a system with a broader set of capabilities.
ESSs are specifically designed for senior executives and have made many CEOs first-time hands-on computer users. The ESS study conducted by the authors was directed to CEOs, with instructions to complete the questionnaire or forward it to a more appropriate individual to complete. More than one-half of the questionnaires returned were completed by the chief executive officer of a Fortune 500 company.
One-quarter of the respondents were individuals at the vice-presidential level, with the remaining respondents comprising presidents and other upper-level managers. In addition to other ESS issues, this research explored the levels of management - utilizing executive support systems within major corporations. Contrary to the commonly defined target market for ESS, findings revealed that this software technology is used at many levels, including but not limited to middle management and above. The above model illustrates the extent of usage for each level of management.
Group Decision Support Systems
During the past decades, many types of decision support systems have been developed to meet the unique needs of individual managers and executives. Now attention is being directed to the design of DSS technologies for being used by groups of managers and executive teams. Vogel and Nunamakers have broadly defined a group decision support system as any application of information technology to support the work of groups.
One frequently used GDSS design is decision conferencing. Unlike other types of GDSS in which group members work rather independently at their own keyboards and screens, decision conferencing employs a highly portable, chauffeur-driven computer systems to support face-to-face meetings devoted to a focal problem that demands intensive collaboration and consensus building.
Typical of organization innovation generally, most of the new GDSS designs have been developed in research environments apart from organizational units in which their intended users are located. For this reason, the technologies of GDSS typically must be transferred from the research and development settings that hosted the early creative work on innovation to the organizational settings into which the innovation may be assimilated and eventually institutionalized.
Between the preliminary phase of innovation development and the second phase of assimilation are found those activities of technology transfer best described as initiation, when some rudimental form of GDSS is introduced and used for the first time in an organization. Successful initiation arguably encourages a willingness to use the technology again, perhaps repeatedly, while problems at the time of introduction may doom, at least temporarily, subsequent progress toward assimilation. Nevertheless, as Cooper and Zmud make clear, virtually no research on the implement technology has focused on the initiation stage.
As more groups of managers and executive teams attempt to appropriate GDSS technologies, more documentation of apparent assimilation failures is likely to become available. Why does the process of GDSS technology transfer not always lead eventually to adoption, adaption, and acceptance in a particular work environment but rather come to a halt at the initiation stage? Bikson and Eveland concluded, “what most often goes wrong has to do with the fit between the new technology and functioning organization.” Clearly, the first opportunity to study such fit is at the time of its introduction.
Management Information Systems
Management information systems, as with any field of study, can benefit from a framework into which past and present research can be classified and from which potential research hypotheses may be generated. Although there are existing models, they tend to be fairly narrow in scope. The limitations of existing frameworks suggest the need for a more comprehensive framework or model for research.
MIS is defined as a computer-based organizational information system which provides information support for management activities and functions. The term MIS is well accepted, but the MIS may also be called an organizational information system, a computer-based information system, or information system. MIS researchers have focused not only on management information systems but also on transaction processing systems in organizations.
Decision Support System
The definition of Decision Support Systems (DSS)
Decision Support Systems (DSS) is 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 compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions.
History of DSS
Information Systems researchers and technologists have built and investigated Decision Support Systems (DSS) for approximately 50 years. This paper chronicles and explores the developments in DSS beginning with building model-driven DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems, spreadsheet DSS and Group DSS in the early and mid 80s. Data warehouses, Executive Information Systems, OLAP and Business Intelligence evolved in the late 1980s and early 1990s. Finally, the chronicle ends with knowledge-driven DSS and the implementation of Web-based DSS in the mid-1990s. Beginning in about 1980, many activities associated with building and studying
DSS occurred in universities and organizations that resulted in expanding the scope of DSS applications. These actions also expanded the field of decision support systems beyond the initial business and management application domain.
These diverse systems were all called Decision Support Systems. In those early days, it was recognized that DSS could be designed to support decision-makers at any level in an organization.
Also, DSS could support operations decision making, financial management and strategic decision-making.