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Implementing and Integrating Management Support Systems

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1 Implementing and Integrating Management Support Systems

2 Implementing and Integrating MSS
Introducing MSS into organizations and use them for their intended purpose The issue of implementation Integration of MSS Technologies

3 1. Opening Vignette: INCA Expert Systems for the SWIFT Network
The Society for Worldwide Interbank Financial telecommunication (SWIFT) network Automated international message-processing and message transmission services between financial institutions on all continents A real-time decision–making system—INCA (Intelligent Network Controller Assistant) Network control Dealing with events (node failures, open communication links, etc.)

4 Opening Vignette (cont.)
INCA could not degrade or fail could be introduced online only once Not possible Rapid prototyping refinements Incremental extraction of knowledge from the experts Solution Quick, on-schedule development with tight quality control A modular prototyping approach User and expert involvement User training plans An object-oriented paradigm for automated event handling Introduced in modular phases to minimize risks Maintained by the internal system support group

5 2.1 Implementation: An Overview
Opening vignette: INCA - major points about systems implementation Standard methods would not work Custom implementation methods to be designed, tested, and implemented Users must be involved in every phase of the development Management support is crucial (not mentioned) Experts must be cooperative Criteria for success were clearly defined Large-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 process preparing an organization for the new system Introducing the system to assure success MSS implementation is complex MSS are linked to tasks that may significantly change the manner in which organizations operate

7 2.3 What Is Implementation?
Simplistically define Getting a newly developed or significantly changed, system to be used by those for whom it was intended MSS implementation It 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 organizations Expected synergy of human and machine not developed 1. Especially when millions of dollars have been spend on uncompleted or incorrect systems.

10 3.1 Major Issues of Implementation
Models of Implementation Success factors of implementation (figure 18.1) implementation Organizational factors External environment Values and ethics User involvement Process Behavioral Technical Project- related

11 3.2 Technical Factors Relate to the mechanics of the implementation procedure (Table 18.1) Level of complexity System response time and reliability Inadequate functionality Lack of equipment Lack of standardization Network problems Mismatch of hardware and/or software Low level of technical capacity of the project team Two categories Technical constraints Technical problems Several of major importance are listed in table18.1 Can be classified in two categories Network speed is a technical constraints , and budget is a technical problem.

12 3.3 Behavioral Factors The way people perceive systems and how people behave in accepting systems Table 18.2 Decision styles Need for explanation Organizational climate Organizational expectations Resistance 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 commitment Institutionalization Length of time users have been using computers and MSS User Involvement

14 3.5 Organizational Factors
Competence (skills) and organization of the MSS team Adequacy of Resources Relationship with the information systems department Organizational politics

15 3.6 Other organizational factors
Values and ethics Project goals Implementation process Possible Impact on other systems External environment Legal factors Social factors Economic factors Political factors (e.g., government regulations) Other factors Positive or negative

16 4.1 Implementation Strategies
Implementation Strategies for DSS Major Categories Divide the project into manageable pieces Keep the solution simple Develop a satisfactory support base Meet user needs and institutionalize the system

17 4.2 Expert System Implementation
Quality of the system Cooperation of the expert(s) Conditions justifying the need for a particular ES Other factors Commitment on the part of management User involvement The characteristics of the knowledge engineer

18 5.1 What Is Systems Integration and Why Integrate?
Two General Types of Integration Functional Integration (Our primary focus) Different support functions are provided as a single system A single, consistent interface and can switch from one task to another and back again Physical Integration Packaging 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 tools Increasing the applications’ capabilities Integrating DSS and ES provides mutual benefits (Table 18.5) Database and database management system Models and model base management system Interface System capabilities (synergy) Two General Types of Integration Different 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 MSS Within MSS Hybrids of different technologies Supporting different phases or activities in decision making Solve repetitive and/or dependent decision problems Facilitate 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 DSS Different Models: Expert Systems attached to DSS components ES as a separate DSS component ES output as input to a DSS DSS output as input to ES Feedback

22 7. Models of ES and DSS Integration
Sharing in the decision-making process Specification 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 consequences Explanation 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 results Explaining results and decisions

25 9. Intelligent DSS Self-evolving DSS
Automatically adapt to the evolution of its users. Dynamic menu Dynamic user interface Intelligent model base management system

26 9. Intelligent DSS Problem Management (table 18.6) Problem finding.
Problem representation. Information surveillance. Solution generation Solution evaluation

27 10. Intelligent modeling and model management
Tasks require considerable expertise Potential benefits could be substantial Integration implementation is difficult and slow

28 10. Intelligent modeling and model management
Issue in model management Problem diagnosis and selection of models. Construction of models. Use of models. Interpretation of results.

29 10. Intelligent modeling and model management
Quantitative models Human 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
Manufacturing Marketing Engineering Software Engineering Financial services Retailing Commodities trading Property casualty insurance industry decision making

31 12.Problems and issues in integration
Need for integration Justification and cost-benefit analysis Architecture of integration People problems Finding appropriate builders Attitudes of employees in the IS department

32 12.Problems and issues in integration
Development process Organizational impacts Data structure issues Data issues Connectivity

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