Dss Analysis And Decision Support System Information Technology Essay

Dss abbreviation And purpose Support placement Information applied science Essay plumeDuring our study and research on DSS we came to mutual agreement that DSS is an ever evolving domain. Lot of research has been carried out on the usage of DSS in some disaccordent domains oddly in Clinic. But we found that research on the DSS System as a whole (regardless of which domain) has non been conducted m both quantifys in the past. Based on the initial study we pee-pee identified the undermentioned problems 1. There is no universally accepted exposition for DSS, 2. There have been a m some(prenominal) reports of ill fortune of DSS constitutions.In the research paper below we have tried to define DSS body found on the Characteristics and the Targeted customrs. Paper also covers the stopping point making sour, the finis epitome cycle, Framework of DSS which form the base of the DSS. We have also make an attempt to formulate the Critical success factors of the DSS and Rea sons for the failure of DSS.We have tried to receive most of our data through secondary research which involves collating of data from existing research documents and books.In 1960 J. C. R. Licklider wrote a paper on his observation of how the interaction mingled with man and com disgorgeer send away improve the quality and competency in recognizing and problem solving. His paper proved to be like a guide to many future researches on DSS. In 1962 with use of hypertext online system helped in storage and retrieval of documents and creation of digital libraries. SAGE (Semi Automatic Ground Environment) build by Forrester is probably the first data driven computerized DSS. In 1964 Scott Morton built up an interactive warning driven management ending system which could help managers manage all distinguished(predicate) management decisions. In 1970 John D.C. Little noned that the requirement for designing models and system to make a management decision was completeness to data, simplicity, ease of fake and robustness, which till date be relevant in improving and evaluating modern DSS. By 1975, he built up a DSS called Brandaid which could stay promotion, advertising, pricing and harvest-time related decisions. In 1974 the concenter was on giving managers with information which was from accounting and transaction processing system with use if MIS(Management Information Systems) simply MIS was found to not helping out managers with making key decisions. Hence in 1979 Scott Morton and Gorry argued that MIS just primarily focused on organise decisions and hence the system which also nutriments formless and semi- coordinate decision should be termed as close support systems.Gorry and Scott Morton coined the phrase DSS in 1971, about ten years after MIS became popular. (David Arnott, An Analysis of conclusiveness Support Systems Research, p.1) conclusion support system now-a-days are critical for the daily operation and success of many constitutio ns. Due to which in that respect is a huge investment being make on development, customization, implementation and upgradation of these systems.Despite the rapid growth of information technology over the past decade, the success of Decision Support System remains alleged(prenominal) due to the lack of insufficient studies on the resultants. As David Arnott and Gemma Dodson stated in Decision Support System Failure (David Arnott, Gemma Dodson, p.1) The development of a decision support system is a questioning affair. The Volatile task environment and dynamic nature of managerial work means that DSS Projects are prone to Failure.As per David Arnott and Gemma Dodson definition higher up its very important to go steady why organization take such a big risk and invest in a Decision support system. (Efraim Turban, Ramesh Sharda, Decision Support and Business Intelligence Systems, 8th Edition, p.12) Some of the factors why company use DSS Systems suggested by Efraim and Ramesh areSpe edy Computation meliorate Communication and CollaborationIncrease Productivity of group membersImproved data managementManaging Giant Data warehousesQuality SupportAgility SupportOvercoming cognitive limits in processing and storing informationThe paper here deals with the study of how decision analysis happens in DSS, Problems and in that respect cases, Why DSS are required or employ by organization, Decision making process, Types of DSS, Reason for the failure of DSS, Critical success factor of DSS.Activities that require decision making form a preparedness or a group of problems, varying from structured problem to unstructured problem. As Simon States The boundary between hygienic structured and ill structured problems is vague, fluid and not susceptible to formalization.(The structure of ill structured problems, 1973, Herbert A. Simon) the Decision making process, decision made and the style of making decision weed be influenced by the temper of the individual and their co gnitive style, and which is one of the major footings for different decision aids being sought.(Management Information System 8/E Raymond McLeod, Jr. and George Schell)Decision fonts in terms of problem structureStructured problems can be solved with algorithms and decision rules.A structured decision can be outlined as one in which three components of a decision-the data, process, and evaluation. Structured decisions are made on a regular basis in business environments. If a nonindulgent framework is placed for the decision making process it helps to solve the problem.Unstructured problems have no structure in Simons phases.These decisions have the same components as structured ones-data, process, and evaluation- but on that point nature is different. For example, decision maker use different delineate of data and process to fulfil a decision or goal. In addition, as the nature of the decision is different a few numbers of people within the organization are even qualified to evaluate the decision and to confirm one.Semi structured problems have structured and unstructured phases.Most of the DSS System is focused on Semi Structured decision. Characteristics of this type of decisions of this type areHaving some agreement on the data, process, and/or evaluation to be used,Efforts to maintain a level of human-judgement in the decision making process.To determine which Support system is required it is necessary to analyze thoroughly and understand the limitations and ill effects, which the decision maker are manifested with.Apart from which it is also important to understand the objectives of the system.(Management Information System 8/E Raymond McLeod, Jr. and George Schell)Decision Support System ObjectivesEfficiency of the system.Making decisions.To support managers, not to replace people.Used when the decision is semi structured or unstructured.Incorporate a database.Incorporate models.It is also important that like any other computer based system the DS S should beSimple risqueEasy to UseAdaptiveEasy to communicate with.Now that we have a brief idea about the type of problems that are faced by the managers and the qualities that the DSS system should pertain understanding the decision making process would give an insight to the how a decision is made.Decision Making(Administrative Behavior, Herbert Simon, 1947) Herbert Simon in 1947 defines decision as the behavioral and cognitive processes of making rational human choices, that is, decisions.It states that any decision making is a behavioral and cognitive process of making choices from a set of options available. So, it is important for the DSS, to be accurate enough for making a choice from many different options available. To make accurate choices from the options available DSS takes help from constrains defined and the goals that it has to achieve.(Administrative Behavior, Herbert Simon, 1947) Simon states in his journalThe human being striving for rationality and restricted wi thin the limits of his knowledge has developed some working effects that partially overcome these difficulties. These procedures consist in expect that he can isolate from the rest of the world a closed system containing a limited number of variables and a limited melt down of consequences.By this Simon mean that people with limited knowledge about a detail task or domain will develop some proficiency that will help the person to overcome these difficulties. This in a sense defines the basic purpose of DSS system to make help managers with making decision. It is also important to understand the term isolated from the rest of the world, by this Simon meant that the decision should be purely be based on the goals to be obtained and based on the criteria defined it should not come under any other influence.He also formulated a model of decision making. (David Arnott, An Analysis of Decision Support Systems Research, p.1) Simons model of decision-making has been used in DSS researc h since the fields inception and was an integral component of Gorry and Scott Mortons seminal MIS/DSS framework.(Image Taken from Wikipedia, Figure 1)In Simon model of decision making (Figure 1) there are several phases through which an individual goes through to reach his objectives or goal. Phases of Decision Making as per Simon Model are as followsIntelligence depict reality.Get problem/opportunity understanding.Obtain required information.DesignMake decision criteria.Make decision alternatives.Look for related unmanageable events.Identify the link up between criteria, alternatives, and events.ChoiceLogically assess the decision alternatives.Make recommended actions that best meet the decision criteria.ImplementationConsider the decisionanalysisand assessment.Evaluate the make up of the recommendations.Have confidence in the decision.Make an implementation plan.Secure required supplies.Set implementation plan into act.Based on the Decision making process by Simon and the proble m structure draw offd above we can define the accuracy of decisions can be measured by the following criteriaThe methods or technique with which it achieves the desired results or goals andThe efficiency with which the goals and sub goals are obtained.By this we mean members of the organization may focus on the method and technique used to reach to the result or goal, but the administrative management must pay attention to the efficiency with which the desired result was obtained.To understand the efficiency of the decision made it is necessary to analysis the decision made. Decision Analysis in itself is a vast field and deals with many methodologies to measure the efficiency of the decision.Decision Analysis(Ronald Howard, 1965, Decision Analysis Applied Decision Theory)Decision Analysis is a discipline which was developed to deal with the challenges of making important decisions which involved handling major uncertainty, long-term targets and compound value issues. Decision Ana lysis comprises the philosophical, theoritical, methodological, and professional perpetrate necessary to formalize the analysis of important decisions.(Ronald Howard, 1965, Decision Analysis Applied Decision Theory) Decision analysis is a logical procedure for the balancing of the factors that influence a decision. The procedure incorporates uncertainties, values, and preferences in a basic structure that models the decision. Typically, it includes technical, marketing, competitive, and environmental factors. The essence of the procedure is the face of a structural model of the decision in a form suitable for computation and manipulation the realization of this model is often a set of computer programs.Decision-making consists of assigning values on the outcomes of interest to the decision-maker. Thus, decision analysis evaluates the decision-makers trade-offs between monetary and non-monetary outcomes and also establishes in quantitative terms his preferences for outcomes that ar e risky or distributed over time.Ronald A. Howard in his paper Advances Foundations of DA Revisited goes on to discuss the Pillars of Decision AnalysisThe First Pillar Systems AnalysisSystems analysis grew out of public War II and was concerned with understanding dynamic systems. Key notions were those of state variables, feedback, stability, and sensitivity analysis. The field of systems engineering is currently in a state of resurgence. Decision analysis and systems engineering have many complementary features (Howard, 1965, 1973).The Second Pillar Decision TheoryDecision theory is concerned primarily with making decisions in the face of uncertainty. Its roots go back to Daniel Bernoulli (Bernoulli, 1738) and Laplace. Bernoulli introduced the idea of logarithmic utility to explain the puzzle called the St. Petersburg paradox. In the most influential book on probability ever written (Laplace, 1812), Laplace discusses the Esperance mathematique and the Esperance morale. Today we w ould call these the mean and the certain equivalent.The Third Pillar Epistemic ProbabilityJaynes taught that there is no such thing as an objective probability a probability reflects a persons knowledge (or equivalently ignorance) about some uncertain feature. People think that probabilities can be found in data, but they cannot. Only a person can assign a probability, taking into account any data or other knowledge available. Since there is no such thing as an objective probability, using a term like playing areaive probability only creates confusion. Probabilities describing uncertainties have no conduct of adjectives.This understanding goes back to Cox (2001), Jeffreys (1939), Laplace (1996) and maybe Bayes, yet somehow it was an idea that had been lost over time. A historied scientist put it best over cl years agoThe actual science of logic is conversant at present only with things either certain, im realizable, or entirely doubtful, none of which (fortunately) we have to rea son on. Therefore the true logic for this world is the calculus of Probabilities, which takes account of the magnitude of the probability which is, or ought to be, in a reasonable mans mind. (Maxwell, 1850)The Fourth Pillar cognitive PsychologyIn the 1960s few appreciated the important role that cognitive psychology would play in understanding human behaviour. At the time of DAADT, we just did our best to help experts assign probabilities. In the 1970s the work of Tversky, Kahneman, and others provided two valuable contributions. First, it showed that people making decisions relying only on their intuition were subject to many errors that they would recognize upon reflecting on what they had done. This emphasized the need for a formal procedure like decision analysis to assist in making important decisions. The second contribution was to show the necessity for those who are assisting in the probability and preference assessments to be aware of the many pitfalls that are characterist ic of human design. Tversky and Kahneman called these heuristics methods of thought that could be useful in general but could trick us in particular settings. We can think of these as the optical illusions of the mind.An important distinction here is that between descriptive and normative decision-making. Descriptive decision-making, as the name implies, is concerned with how people actually make decisions. The test of descriptive decision-making models is whether they actually describe human behaviour. Normative decision-making is decision-making according to certain rules, or norms, that we want to follow in our decision-making processes.The underlying premise of decision analysis is to distinguish between a good decision and a good outcome. A good decision is termed as logical decision which is based on the information, values, and preferences of the decision-maker. A good outcome is one that benefits the end exploiter. The aim is to arrive at good decisions in all situations which would go on to ensure as high a function of good outcomes. But at times it may be observed that even a good decision has achieved a good outcome. But for bulk of the situations we may face making good decisions is the best way to ensure good outcomes.A decision can be defined as a choice among alternatives that will yield uncertain futures, for which we have preferences. To explain the formal aspects of decision analysis the image of the three-legged gutter shown in Figure 3.1 (Howard, 2000).The legs of the stool are the three elements of any decision what you can do, the alternatives what you know, the information you have and what you want, your preferences. Collectively, the three legs represent the decision basis, the specification of the decision. Note that if any leg is missing, there is no decision to be made. If you have only one alternative, then you have no choice in what you do. If you do not have any information linking what you do to what will happen in the futu re, then all alternatives serve equally well be wee-wee you do not see how your actions will have any effect. If you have no preferences regarding what will happen as a result of choosing any alternative, then you will be equally happy choosing any one. The seat of the stool is the logic that operates on the decision basis to produce the best alternative. We shall soon be constructing the seat to make sure that it operates correctly.Decision Analysis provides a formal language for communication for the people involved in the decision-making process. During this, the basis for a decision becomes clear, not just the decision itself. The views may differ on whether to adopt an alternative because individuals possess different relevant information or because they may value the consequences differentlly.Decision analysis CycleThe professional practice of decision analysis is decision engineering. Creating a focused analysis requires the continual elimination of every factor that will not contribute to making the decision. This sift has been a feature of decision analysis since the beginning (Howard, 1968, 1970). Since DAADT, the process has been described as a decision analysis cycle, depicted in Figure 3.4 (Howard, 1984a).The coating of decision analysis can be modeled in form of an iterative procedure called the Decision Analysis Cycle.Decision Analysis CycleThe procedure is divided into three phasesDeterministic phase the variables affecting the decision are defined and relations between the variables established, the values are assigned, and the importance of the variables is measured upto a acceptable level of certainity.Probabilistic phase the associated probability assignments on values are derived. We also take into account the assessment of risk preference, which identifies the best possible solution in the face of uncertainty.Informational phase the results of the first two phases are reviewed to determine the economic value of eliminating uncertainty i n each of the important variables in the problem.It is the most important phase among the three because it evaluates in monetary terms to have the perfect information.Decision Support SystemThere is no universally accepted definition for the DSS system as of now. It is the major reason we have to rely on the Characteristics and Objectives of the DSS to understand the system. Below are a few famous definition for the DSS we would refer to formulate a definition for the system.(Decision Support Systems An Organizational Perspective, Keen Scott-Morton, 1978) Keen and Scott define DSS as Decision support systems couple the capable 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.If we correlate the definition from Keen and Morton and Simons definition statingThe human being striving for rationality and restricted within the limi ts of his knowledge has developed some working procedures that partially overcome these difficulties. These procedures consist in assuming that he can isolate from the rest of the world a closed system containing a limited number of variables and a limited range of consequences.We understand that the base of the DSS system is to support the manager. But one of the drawbacks of the definition from Keen and Morton is that they state that the system deals with only semi structured problems but the present DSS system also handles Unstructured and Structured issues.Peter Keen in 1980 defined DSS as Personal System to assist Manager must be built from the Managers perspective and must be based on a very detailed understanding of how the manager makes decision and how the manager organization functions. (Donald R. Moscato, 2004, p.1)In the above definition Peter Keen tries to define DSS in terms of the implementation and customization of DSS and states that it should be done based on Manag ers perspective, styles of decision making and the organizations function. Drawback with this definition is that it defines DSS as a personnel system and with the introduction of Group DSS and Communication DSS the definition becomes obsolete.Bonczek, Holsapple and Whinston (Foundations of Decision Support Systems, Bonczek, Holsapple and Whinston, 1981, p.19) argued the system must possess an interactive query facility, with a query language that is easy to learn and use.The above definition tries to explain that DSS systems should be interactive and should have a language of its own so that constrains of the decision and the goals can be addressed to the system and is easy to understand and use. (We have stated in the section objectives of DSS).(Daniel J Power, 2001, p.1)Sprague and Carlson (1982) define Decision Support Systems broadly as interactive computer based systems that help decision-makers use data and models to solve ill-structured, unstructured or semi-structured pro blems.Sparague and Carlson explained the DSS system as an interactive system and which can help managers solve ill-structured, unstructured and semi-structured problem. If you observe the definition is a co-relation of definition provided by Peter Keen, Keen Scott-Morton 1978 and Bonczek, Holsapple and Whinston-1981 by removing there drawbacks.A few much definition that we thought explains DSS are as followsMarakas in 2002 (Marakas, 2002, p.4) stated the following is a formal definition of DSS A decision support system is a system under the control of one or more decision makers that assists in the activity of decision making by providing an organized set of tools think to impose structure on portions of the decision-making situation and to improve the ultimate effectiveness of the decision outcome.Importance of Marakas definition is that it takes into consideration the tools that a manager can use to work with DSS system (can term it as third party tools in some cases) other th at the query language or the normal interactive screen door of the DSS.From the above example it is pretty clear that to define a DSS not only we will have to study the characteristics and the tools, types of DSS but also the framework of the DSS to mete out a definition or to define one.(Ralph H. Sprague, Hugh J. Watson, Decision Support System Putting Theory into practice, 3rd edition, 1993, p.4)Characteristics of DSSThey tend to be aimed at the less well structured, underspecified problems that upper level managers typically face.They attempt to combine the use of models or analytic techniques with traditional data access and retrieval functionThey specifically focus on features which make them easy to use by non-computer people in an interactive modeThey emphasize flexibility and adaptability to accommodate changes in the environment and the decision making approach of the user.Framework of DSSFrom (Daniel J Powers, 2001, p.1) we come to know that the framework for the Decisi on support system should be based on the following factors (by this Daniel J Power meant System should be discussed and explained in terms of four descriptors to maintain better communication)Dominant Technological ComponentThe Targeted UsersPurposeDeployment Technology(Daniel J Powers, 2001, p.1) And the Five generic categories of DSS areCommunication DrivenData DrivenDocument DrivenKnowledge DrivenModel Driven decision support system.(Daniel J Powers, 2001, p.1) DSS Deployment technology can beMainframe ComputersA client server LANWeb Based ArchitectureMarakas (2002) meant that it is important to understand the type of DSS to determine the best design and approach of a new DSS.In 1976 Steven Alter, a doctoral student created a taxonomy of seven DSS types on Gorry and Scott-Morton framework based on a study of 56 DSSs. In 1980, Steven Alter (Daniel J Power, 2001, p.2) proposed his taxonomy of Decision Support Systems. Alters seven category typology is still relevant for discussing some types of DSS, but not for all DSS. Alters idea was that a Decision Support System could be categorized in terms of the generic operations it performs, independent of type of problem, functional area or decision perspective.His seven types includedFile Drawer SystemsData Analysis SystemsAnalysis Information SystemsAccounting and Financial models realistic ModelsOptimization ModelsSuggestion Models.Alters first three types of DSS have been called data orientated or data driven the second three types have been called model oriented or model driven and Alters suggestion DSS type has been called intelligent or knowledge driven DSS.Importance of Alters Study wasSupports concept of Developing Systems that address particular decisions.Makes clear that DSS need not be restricted to a particular Application Type.Based on Alters study Daniel J Power formulated an grow framework. The purpose of expanded DSS framework is to help people understand and apply the framework to integrate, eval uate, implement and select appropriate means for supporting and informing decision-makers.expand Framework suggested by Daniel J Power (Daniel J Power, Expanded DSS framework, June 2001, p.5)Dominant DSSComponentTarget UsersInternal / ExternalPurposeGeneral /SpecificDeploymentTechnologyCommunicationsCommunications-Driven DSSInternal teams, nowexpanding to externalpartnersConduct a meeting or Help users collaborateWeb or Client/ hordeDatabaseData-Driven DSSManagers, staff, nowSuppliersQuery a Data WarehouseMain Frame, Client/Server, WebDocument baseDocument-DrivenDSSInternal users, butthe user group is expanding attempt Web pages orFind documentsWeb or Client/ServerKnowledge baseKnowledge-Driven DSSInternal users, nowCustomersManagement Adviceor look at productsClient/Server, Web,Stand-alone PCModelsModel-DrivenDSSManagers and staff,now customersCrew Scheduling orDecision AnalysisStand-alone PC orClient/Server or Web(Ralph H. Sprague, Hugh J. Watson, Decision Support System Putting Theory into practice, 3rd edition, 1993, p.4) Three Technology LevelsSpecific DSS System which actually accomplishes the work might be called the specific DSS.DSS Generator This is a set of related hardware and software which provides a set of capabilities to quickly and easily build a specific DSS.DSS cats-paw These are hardware or software elements which facilitates the development of a specific DSS or DSS Generator.Based on the details above we would like to define DSS asDSS can be defined as use of computer application that can help managers, staff members, or people who interact within the organization to make decisions and identify problems by using available data and communication technology.It is also very important to understand the reason for the failure of DSS. And what are the factors that could cause the failure of system and which factors are to be termed as the success factors of DSS.Reason for Failure of DSS SystemDespite the benefits that DSS offers the impleme ntation of such system has been limited. Some of the reasons can be the followingProper evaluation of the DSS preceding and during DSS development.DSS output does not fit the producers decision-making style.Complexity involved while operating the DSS.Post Implementation support.Benefits from these systems are not always realizedOther than the above reason few disadvantages of the DSS system areOver dependency for Decision makingAssuming it to be correct.Unanticipated effectsDeflect personal responsibilitiesInformation overload.Considering the above reason, to increase the rate of success of DSS implementation and customization, the following factors should be considered and managed.Critical Success Factors of DSSHartono (Hartono et al, 2006, p.257) uses the following words to describe their interpretation of Critical Success Factors Success antecedents are those key factors that organizations can manage so that the management information system is favourably received and the implem entation is deemed as successful(Johannes Johansson Bjorn Gustafson, Critical Success Factors affecting Decision Support System Success, from an end-user perspective,2009, p.1)Johannes Johansson and Bjorn Gustafson identified three factors that significantly affect end-users sensed net benefits, namely Data Quality, Problem Match and Support Quality.(S. Newman1, T. Lynch, and A. A. Plummer Success and failure of decision support systems Learning as we go, p.1)The case study HotCross, a DSS under development to evaluate crossbreeding systems in northern Australia, provided evidence of a shift in the development process because greater emphasis was put on the learning process of breeding program design by end-users rather than emphasis on learning how to use the DSS itself. Greater end user involvement through participatory learning approaches (action learning, action research, and soft systems methodologies), iterative prototyping (evolving development processes), as well as keeping DSS development manageable and elfin in scope, will provide avenues for impr