Presentation on theme: "Implementing and Integrating Management Support Systems"— Presentation transcript:
1 Implementing and Integrating Management Support Systems
2 Implementing and Integrating MSS Introducing MSS into organizations and use them for their intended purposeThe issue of implementationIntegration of MSS Technologies
3 1. Opening Vignette: INCA Expert Systems for the SWIFT Network The Society for Worldwide Interbank Financial telecommunication (SWIFT) networkAutomated international message-processing and message transmission services between financial institutions on all continentsA real-time decision–making system—INCA (Intelligent Network Controller Assistant)Network controlDealing with events (node failures, open communication links, etc.)
4 Opening Vignette (cont.) INCAcould not degrade or failcould be introduced online only onceNot possibleRapid prototyping refinementsIncremental extraction of knowledge from the expertsSolutionQuick, on-schedule development with tight quality controlA modular prototyping approachUser and expert involvementUser training plansAn object-oriented paradigm for automated event handlingIntroduced in modular phases to minimize risksMaintained by the internal system support group
5 2.1 Implementation: An Overview Opening vignette: INCA - major pointsabout systems implementationStandard methods would not workCustom implementation methods to be designed, tested, and implementedUsers must be involved in every phase of the developmentManagement support is crucial (not mentioned)Experts must be cooperativeCriteria for success were clearly definedLarge-scale, real-time ES can be developed on schedule and be very reliable
6 2.2 Introduction MSS systems implementation is not always successful Implementation is an ongoing processpreparing an organization for the new systemIntroducing the system to assure successMSS implementation is complexMSS are linked to tasks that may significantly change the manner in which organizations operate
7 2.3 What Is Implementation? Simplistically defineGetting a newly developed or significantly changed, system to be used by those for whom it was intendedMSS implementationIt is an ongoing process during the entire development of the system.It can have partial implementation.一開始The definition of implementation is complicated because implementation is long, involved process with vague boundaries.Partial implementation:At one place in the system may precipitate compensatory and negative impacts.Others reasons are budget reduction or cost overruns.
8 2.4 Measuring Implementation Success Five independent criteria for success.Others measures for judging the success of MSS include the following.Additional measures of success in evaluating DSS.
9 2.5 MSS Implementation Failures Usually a closely held secret in many organizationsExpected synergy of human and machine not developed1. Especially when millions of dollars have been spend on uncompleted or incorrect systems.
10 3.1 Major Issues of Implementation Models of ImplementationSuccess factors of implementation (figure 18.1)implementationOrganizationalfactorsExternalenvironmentValues andethicsUserinvolvementProcessBehavioralTechnicalProject-related
11 3.2 Technical FactorsRelate to the mechanics of the implementation procedure (Table 18.1)Level of complexitySystem response time and reliabilityInadequate functionalityLack of equipmentLack of standardizationNetwork problemsMismatch of hardware and/or softwareLow level of technical capacity of the project teamTwo categoriesTechnical constraintsTechnical problemsSeveral of major importance are listed in table18.1Can be classified in two categoriesNetwork speed is a technical constraints , and budget is a technical problem.
12 3.3 Behavioral FactorsThe way people perceive systems and how people behave in accepting systemsTable 18.2Decision stylesNeed for explanationOrganizational climateOrganizational expectationsResistance to change (user resistance)Reasons (Ex.change indecision-making approach)Strategies for dealing (Ex.participative strategies)The resisters (Focus 18.3)
13 3.4 Process Factors The process of developing and implementing MSS Top management support (one of the most important)Management and user commitmentInstitutionalizationLength of time users have been using computers and MSSUser Involvement
14 3.5 Organizational Factors Competence (skills) and organization of the MSS teamAdequacy of ResourcesRelationship with the information systems departmentOrganizational politics
15 3.6 Other organizational factors Values and ethicsProject goalsImplementation processPossible Impact on other systemsExternal environmentLegal factorsSocial factorsEconomic factorsPolitical factors (e.g., government regulations)Other factorsPositive or negative
16 4.1 Implementation Strategies Implementation Strategies for DSSMajor CategoriesDivide the project into manageable piecesKeep the solution simpleDevelop a satisfactory support baseMeet user needs and institutionalize the system
17 4.2 Expert System Implementation Quality of the systemCooperation of the expert(s)Conditions justifying the need for a particular ESOther factorsCommitment on the part of managementUser involvementThe characteristics of the knowledge engineer
18 5.1 What Is Systems Integration and Why Integrate? Two General Types of IntegrationFunctional Integration(Our primary focus)Different support functions are provided as a single systemA single, consistent interface and can switch from one task to another and back againPhysical IntegrationPackaging hardware, software, and communication features required together for functional integration
19 5.2 Why Integrate? Two Major Objectives for MSS Software Integration Enhancements of basic toolsIncreasing the applications’ capabilitiesIntegrating DSS and ES provides mutual benefits (Table 18.5)Database and database management systemModels and model base management systemInterfaceSystem capabilities (synergy)Two General Types of IntegrationDifferent systems (e.g., ES and DSS)Same type systems (e.g., multiple ES)
20 6.1 Generic Models for MSS Integration Two different levels (figure 18.2)Across different MSSWithin MSSHybrids of different technologiesSupporting different phases or activities in decision makingSolve repetitive and/or dependent decision problemsFacilitate integration by assisting in the transformation of the outputs for one system to the inputs to another system
21 7. Models of ES and DSS Integration Names ranging from expert support systems to intelligent DSSDifferent Models：Expert Systems attached to DSS componentsES as a separate DSS componentES output as input to a DSSDSS output as input to ESFeedback
22 7. Models of ES and DSS Integration Sharing in the decision-making processSpecification of objectives, parameters, probabilities.Retrieval and management of data.Generation of decision alternatives.Inference of consequences of decision alternatives.Assimilation of verbal, numeric, and graphical information.Evaluation of sets of consequencesExplanation and implementation of decisions.Strategy formulation.
23 8. Integrating EIS, DSS, and ES, and Global Integration Information generated by EIS is used as an input to DSS.DSS feeds back to EIS.A possible interpretation and explanation capability performed by ES.See Fig 18.6 (P.753)
24 9. Intelligent DSS Active (Symbiotic) DSS Understanding the domain. Formulating problems.Relating a problem to a solver.Interpreting resultsExplaining results and decisions
25 9. Intelligent DSS Self-evolving DSS Automatically adapt to the evolution of its users.Dynamic menuDynamic user interfaceIntelligent model base management system
26 9. Intelligent DSS Problem Management (table 18.6) Problem finding. Problem representation.Information surveillance.Solution generationSolution evaluation
27 10. Intelligent modeling and model management Tasks require considerable expertisePotential benefits could be substantialIntegration implementation is difficult and slow
28 10. Intelligent modeling and model management Issue in model managementProblem diagnosis and selection of models.Construction of models.Use of models.Interpretation of results.
29 10. Intelligent modeling and model management Quantitative modelsHuman experts often use quantitative models to support their experience and expertise.ES contributions can be demonstrated by examining the work of a consultant. (7 steps ,P.758)ES can be used as an intelligent interface between user and quantitative models.There are several commercial systems assist with statistical analysis.
30 11. Examples of integrated systems ManufacturingMarketingEngineeringSoftware EngineeringFinancial servicesRetailingCommodities tradingProperty casualty insurance industry decision making
31 12.Problems and issues in integration Need for integrationJustification and cost-benefit analysisArchitecture of integrationPeople problemsFinding appropriate buildersAttitudes of employees in the IS department
32 12.Problems and issues in integration Development processOrganizational impactsData structure issuesData issuesConnectivity