Implementing and Integrating Management Support Systems

G913821 吳昇洋G913838 林家誼

Implementing and Integrating MSS

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

1. Opening Vignette: INCA Expert Systems for the SWIFT Network

The Society for Worldwide Interbank Financial telecommunication (SWIFT) network Automated international message-processing and messa

ge 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 communicatio

n links, etc.)

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

2.1 Implementation: An Overview

Opening vignette: INCA - major pointsabout 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

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

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 devel

opment of the system. It can have partial implementation.

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.

2.5 MSS Implementation Failures Usually a closely held secret in many

organizations Expected synergy of human and machine

not developed

3.1 Major Issues of Implementation

Models of Implementation

Success factors of implementation (figure 18.1)

implementation

Organizational factors

External environment

Values and ethics

Userinvolvement

Processfactors

Behavioralfactors

Technicalfactors

Project-related

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

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)

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

3.5 Organizational Factors

Competence (skills) and organization of the MSS team

Adequacy of Resources Relationship with the information systems d

epartment Organizational politics

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

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

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

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

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)

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

7. Models of ES and DSS Integration

Integration： Names ranging from expert support systems to intelli

gent 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

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.

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)

9. Intelligent DSS

Active (Symbiotic) DSS Understanding the domain. Formulating problems. Relating a problem to a solver. Interpreting results Explaining results and decisions

9. Intelligent DSS

Self-evolving DSS Automatically adapt to the evolution

of its users. Dynamic menu Dynamic user interface Intelligent model base management

system

9. Intelligent DSS

Problem Management (table 18.6) Problem finding. Problem representation. Information surveillance. Solution generation Solution evaluation

10. Intelligent modeling and model management

Tasks require considerable expertise Potential benefits could be substantial Integration implementation is difficult

and slow

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.

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.

11. Examples of integrated systems

Manufacturing Marketing Engineering Software Engineering Financial services Retailing Commodities trading Property casualty insurance industry

decision making

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

12.Problems and issues in integration

Development process Organizational impacts Data structure issues Data issues Connectivity