Dss Analysis And Decision Support System Information Technology Essay

Dss depth psychology And last nates off frame info Technology EssayAbstractDuring our choose and research on DSS we came to coarse agreement that DSS is an ever evolving dry land. Lot of research has been carried push through on the trunk of DSS in nearly(prenominal) different domains especially in Clinic. But we lay down that research on the DSS ashes as a whole (regard little of which domain) has non been conducted m any(prenominal) times in the past. Based on the initial study we receive identified the following(a) lines 1. thither is no universally treasure description for DSS, 2. There gestate been a many reports of failure of DSS forms.In the research reputation below we have time- tasteed to trace DSS placement found on the Characteristics and the Targeted pulmonary tuberculosisrs. Paper also covers the finality fashioning extremity, the finish digest cycle, Framework of DSS which form the base of the DSS. We have also do an attempt to form ulate the sensitive mastery factors of the DSS and Reasons for the failure of DSS.We have tried to collect most of our entropy through secondary research which involves collating of tuition from existing research documents and books.In 1960 J. C. R. Licklider wrote a paper on his expression of how the interaction among man and ready reck aner base improve the eccentric and competency in recognizing and problem solving. His paper proved to be standardised a guide to many future researches on DSS. In 1962 with wasting disease of hypertext online arranging helped in storage and retrieval of documents and creation of digital libraries. keen-sighted (Semi Automatic Ground Environment) built by For breaker is probably the offset printing data dictated computerized DSS. In 1964 Scott Morton built up an interactional get drive heed ratiocination placement which could help managers make outstanding management conclusivenesss. In 1970 John D.C. Little noned that th e requirement for designing amazes and organization to make a management termination was completeness to data, simplicity, ease of control and robustness, which trough date atomic occur 18 germane(predicate) in improving and evaluating modern DSS. By 1975, he built up a DSS called Brandaid which could fight promotion, advertising, pricing and intersection point connect decisivenesss. In 1974 the focus was on giving managers with information which was from report and transaction processing arrangement with apply if MIS(Management Information Systems) provided MIS was get up up to non helping out managers with do key closings. Hence in 1979 Scott Morton and Gorry argued that MIS just primarily focussed on structured decisions and hence the ashes which also supports unstructured and semi-structured decision should be shapeed as closing support organisations.Gorry and Scott Morton coined the phrase DSS in 1971, about ten years after MIS became popular. (David Arno tt, An digest of stopping point corroboration Systems Research, p.1) decisiveness support form now-a-days be sarcastic for the daily operation and success of many arrangements. Due to which on that point is a huge investment being make on teaching, customization, implementation and upgradation of these systems. in spite of the rapid growth of information technology over the past decade, the success of Decision substitute System remains questionable due to the drop of insufficient studies on the outcomes. As David Arnott and Gemma Dodson give tongue to in Decision retain System Failure (David Arnott, Gemma Dodson, p.1) The development of a decision support system is a dangerous affair. The Volatile task environment and dynamic reputation of managerial work means that DSS Projects ar prone to Failure.As per David Arnott and Gemma Dodson interpretation preceding(prenominal) its very heavy to control why organization guard such a big risk and invest in a Decision s upport system. (Efraim Turban, Ramesh Sharda, Decision Support and Business Intelligence Systems, eighth Edition, p.12) Some of the factors why company use DSS Systems suggested by Efraim and Ramesh atomic number 18 active ComputationImproved Communication and CollaborationIncrease Productivity of company membersImproved data managementManaging Giant data w behousesQuality SupportAgility SupportOvercoming cognitive limits in processing and storing informationThe paper here deals with the study of how decision depth psychology happens in DSS, worrys and there types, why DSS are call for or implemented by organization, Decision fashioning process, Types of DSS, Reason for the failure of DSS, Critical success factor of DSS.Activities that require decision reservation form a set or a conference of problems, varying from structured problem to unstructured problem. As Simon States The boundary amidst fountainhead structured and ruin structured problems is vague, fluid and not open to formalization.(The structure of ill structured problems, 1973, Herbert A. Simon) the Decision qualification process, decision made and the style of making decision support be influenced by the spirit of the individual and their cognitive style, and which is one of the major reasons for different decision support being sought.(Management Information System 8/E Raymond McLeod, Jr. and George Schell)Decision types in terms of problem structure structure problems shadow be solved with algorithms and decision rules.A structured decision end be specify as one in which tierce fragments of a decision-the data, process, and evaluation. Structured decisions are made on a regular basis in business environments. If a rigid manikin 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 there character is diffe rent. For example, decision maker use different set of data and process to do a decision or goal. In addition, as the nature of the decision is different a few numbers of tribe indoors the organization are point 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 defy a level of piece-judgement in the decision making process.To determine which Support system is required it is necessary to analyze thoroughly and come across the limitations and ill effects, which the decision maker are manifested with.Apart from which it is also important to scan the objectives of the system.(Management Information System 8/E Raymond McLeod, Jr. and George Schell)Decision Support System ObjectivesEfficiency of the system.Maki ng decisions.To support managers, not to replace people.use when the decision is semi structured or unstructured. arrest a database.Incorporate models.It is also important that akin any other(a) computer based system the DSS should beSimpleRobustEasy to Use adaptativeEasy to communicate with.Now that we have a brief motif about the type of problems that are faced by the managers and the qualities that the DSS system should doctor 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 behavioural and cognitive processes of making rational human prizes, 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 confine within the limits of his knowledge has developed some working forces that partially outmatch these difficulties. These operations consist in assuming that he can isolate from the rest of the introduction a closed system containing a hold number of variables and a limited range of consequences.By this Simon mean that people with limited knowledge about a particular task or domain provide develop some technique that will help the psyche to overcome these difficulties. This in a sense defines the prefatory 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 th e criteria defined it should not come under any other influence.He also develop a model of decision making. (David Arnott, An analytic thinking of Decision Support Systems Research, p.1) Simons model of decision-making has been used in DSS research 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 meet his objectives or goal. Phases of Decision Making as per Simon Model are as followsIntelligenceIdentify reality.Get problem/opportunity understanding.Obtain required information.DesignMake decision criteria.Make decision alternatives.Look for related unmanageable events.Identify the links between criteria, alternatives, and events.ChoiceLogically valuate the decision alternatives.Make recomm oddityed actions that silk hat meet the decision criteria.Implementation distribu te the decisionanalysisand assessment.Evaluate the cost of the recommendations.Have confidence in the decision.Make an implementation formulate.Secure required supplies.Set implementation plan into act.Based on the Decision making process by Simon and the problem structure described above we can define the accuracy of decisions can be measured by the following criteriaThe methods or technique with which it achieves the want 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 moldiness support 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 digest(Ronal d 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 controvertion major unsurety, long-term targets and complex value issues. Decision Analysis comprises the philosophical, theoritical, methodo system of logical, and skipper practice 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 un authoritativeties, 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 construction of a geomorphological model of the decision in a form suitable for enumeration and manipulation the realization of this model is often a set of c omputer 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 are 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 World War II and was concerned with understanding dynamic systems. advert 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 color 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 Dan iel Bernoulli (Bernoulli, 1738) and Laplace. Bernoulli introduced the idea of logarithmic utility to apologize the puzzle called the St. Petersburg paradox. In the most influential book on opportunity ever written (Laplace, 1812), Laplace discusses the Esperance mathematique and the Esperance morale. at once we would call these the mean and the certain equivalent.The Third Pillar Epistemic chanceJaynes taught that there is no such thing as an objective probability a probability reflects a persons knowledge (or equivalently ignorance) about some uncertain distinction. 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 subjective probability and creates confusion. Probabilities describing uncertainties have no need of adjectives.This understanding goes back to Cox (2001), Jeffreys (1939 ), Laplace (1996) and maybe Bayes, yet in some way it was an idea that had been lost over time. A famous scientist put it trump over 150 years agoThe actual science of logic is conversant at put forward only with things either certain, im feasible, or tout ensemble doubtful, none of which (fortunately) we have to reason 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 mid-sixties few appreciated the important role that cognitive psychology would bump in understanding human behaviour. At the time of DAADT, we just did our outdo to help experts assign probabilities. In the 1970s the work of Tversky, Kahneman, and others provided two worth(predicate) 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 characteristic of human thought. Tversky and Kahneman called these heuristics methods of thought that could be useful in general but could can 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 und erlying premise of decision analysis is to distinguish between a serious 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 user. The aim is to arrive at good decisions in all situations which would go on to get wind as high a percentage of good outcomes. But at times it may be observed that even a good decision has achieved a good outcome. But for legal age 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 ternary-legged stool 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 specialisedation of the decision. keep 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 future, then all alternatives serve equally well because 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 after part 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 lyric for communication for the people involved in the decision-making process. During this, the basis for a decision becomes turn over, not just the decision itsel f. 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 daily roundThe 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 winnowing 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 application 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 transaction between the variables established, the values are assigned, and the importance of the variables is measured upt o a unexceptionable 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 in 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, shrill Scott-Morton, 1978) Keen and Scott define DSS as Decision support s ystems couple the intellectual resources of individuals with the capabilities of the computer to improve the look 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 limits 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 Persona l 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 puzzle outs. (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 Managers 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 cons trains 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 in the main as interactive computer based systems that help decision-makers use data and models to solve ill-structured, unstructured or semi-structured problems.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 more 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 intended 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 that the query language or the normal interactive screen 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 deal 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 wit h 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 acknowledge 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 Decision 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) sovereign Technological ComponentThe Targeted UsersPurposeDeployment Technology(Daniel J Powers, 2001, p.1) And the Five generic wine wine categories of DSS areCommunication goadedData impelledDocument DrivenKnowledge DrivenModel Driven decision support system.(Daniel J Powers, 2001, p.1) DSS Deployment technology can beMainframe ComputersA client boniface LANnetwork Based ArchitectureMarakas (2002) meant tha t it is important to understand the type of DSS to determine the best design and approach of a new DSS.In 1976 Steven interchange, 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 modelsRepresentational ModelsOptimization ModelsSuggestion Models.Alters first three types of DSS have been called data oriented or data determined the second three types have been called model oriented or model driven and Alters su ggestion 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 finishing Type.Based on Alters study Daniel J Power formulated an expand framework. The purpose of expanded DSS framework is to help people understand and leave the framework to integrate, evaluate, implement and select appropriate means for supporting and intercommunicate decision-makers.expand Framework suggested by Daniel J Power (Daniel J Power, Expanded DSS framework, June 2001, p.5)Dominant DSSComponentTarget Users ingrained / ExternalPurpose command /SpecificDeploymentTechnologyCommunicationsCommunications-Driven DSSInternal teams, nowexpanding to outerpartnersConduct a meeting or Help users collaborate blade or Client/ServerDatabaseData-Driven DSSManagers, staff, nowSuppliersQuery a Data WarehouseMain Frame, Client/Server, WebDocument baseDoc ument-DrivenDSSInternal users, butthe user group is expandingSearch Web pages or capture documentsWeb or Client/ServerKnowledge baseKnowledge-Driven DSSInternal users, nowCustomersManagement Adviceor Choose productsClient/Server, Web, complete 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) terce 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 bundle which provides a set of capabilities to quickly and easily build a specific DSS.DSS Tool 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 System contempt the benefits that DSS offers the implementation 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 addition the rate of success of DSS implementation and customization, the following factors should be considered and managed.Critical mastery Factors of DSSHartono (Hartono et al, 2006, p.257) uses the following words to describe their interpretation of Critical victor Factors Success antecedents are those key factors that organizations can manage so that the management information system is favorably received and the implementation 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 perceived 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 information as we go, p.1)The case study HotCross, a DSS under development to evaluate crossbreeding systems in nort hern Australia, provided evidence of a shift in the development process because greater dialect was put on the erudition process of breeding program design by end-users rather than emphasis on learning how to use the DSS itself. Greater end user interest group 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 small in scope, will provide avenues for impr